大数据文献综述英文版
大数据外文翻译参考文献综述
大数据外文翻译参考文献综述(文档含中英文对照即英文原文和中文翻译)原文：Data Mining and Data PublishingData mining is the extraction of vast interesting patterns or knowledge from huge amount of data. The initial idea of privacy-preserving data mining PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed even to the partyrunning the algorithm. In contrast, privacy-preserving data publishing (PPDP) may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing. PPDP studies how to transform raw data into a version that is immunized against privacy attacks but that still supports effective data mining tasks. Privacy-preserving for both data mining (PPDM) and data publishing (PPDP) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. One well studied approach is the k-anonymity model [1] which in turn led to other models such as confidence bounding, l-diversity, t-closeness, (α,k)-anonymity, etc. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. The aim of this paper is to present a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explain their effects on Data Privacy.Although data mining is potentially useful, many data holders are reluctant to provide their data for data mining for the fear of violating individual privacy. In recent years, study has been made to ensure that the sensitive information of individuals cannot be identified easily.Anonymity Models, k-anonymization techniques have been the focus of intense research in the last few years. In order to ensure anonymization of data while at the same time minimizing the informationloss resulting from data modifications, everal extending models are proposed, which are discussed as follows.1.k-Anonymityk-anonymity is one of the most classic models, which technique that prevents joining attacks by generalizing and/or suppressing portions of the released microdata so that no individual can be uniquely distinguished from a group of size k. In the k-anonymous tables, a data set is k-anonymous (k ≥ 1) if each record in the data set is in- distinguishable from at least (k . 1) other records within the same data set. The larger the value of k, the better the privacy is protected. k-anonymity can ensure that individuals cannot be uniquely identified by linking attacks.2. Extending ModelsSince k-anonymity does not provide sufficient protection against attribute disclosure. The notion of l-diversity attempts to solve this problem by requiring that each equivalence class has at least l well-represented value for each sensitive attribute. The technology of l-diversity has some advantages than k-anonymity. Because k-anonymity dataset permits strong attacks due to lack of diversity in the sensitive attributes. In this model, an equivalence class is said to have l-diversity if there are at least l well-represented value for the sensitive attribute. Because there are semantic relationships among the attribute values, and different values have very different levels of sensitivity. Afteranonymization, in any equivalence class, the frequency (in fraction) of a sensitive value is no more than α.3. Related Research AreasSeveral polls show that the public has an in- creased sense of privacy loss. Since data mining is often a key component of information systems, homeland security systems, and monitoring and surveillance systems, it gives a wrong impression that data mining is a technique for privacy intrusion. This lack of trust has become an obstacle to the benefit of the technology. For example, the potentially beneficial data mining re- search project, Terrorism Information Awareness (TIA), was terminated by the US Congress due to its controversial procedures of collecting, sharing, and analyzing the trails left by individuals. Motivated by the privacy concerns on data mining tools, a research area called privacy-reserving data mining (PPDM) emerged in 2000. The initial idea of PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. The solutions were often tightly coupled with the data mining algorithms under consideration. In contrast, privacy-preserving data publishing (PPDP) may not necessarily tie to a specific data mining task, and the data mining task is sometimes unknown at the time of data publishing. Furthermore, some PPDP solutions emphasize preserving the datatruthfulness at the record level, but PPDM solutions often do not preserve such property. PPDP Differs from PPDM in Several Major Ways as Follows ：1) PPDP focuses on techniques for publishing data, not techniques for data mining. In fact, it is expected that standard data mining techniques are applied on the published data. In contrast, the data holder in PPDM needs to randomize the data in such a way that data mining results can be recovered from the randomized data. To do so, the data holder must understand the data mining tasks and algorithms involved. This level of involvement is not expected of the data holder in PPDP who usually is not an expert in data mining.2) Both randomization and encryption do not preserve the truthfulness of values at the record level; therefore, the released data are basically meaningless to the recipients. In such a case, the data holder in PPDM may consider releasing the data mining results rather than the scrambled data.3) PPDP primarily “anonymizes” the data by hiding the identity of record owners, whereas PPDM seeks to directly hide the sensitive data. Excellent surveys and books in randomization and cryptographic techniques for PPDM can be found in the existing literature. A family of research work called privacy-preserving distributed data mining (PPDDM) aims at performing some data mining task on a set of private databasesowned by different parties. It follows the principle of Secure Multiparty Computation (SMC), and prohibits any data sharing other than the final data mining result. Clifton et al. present a suite of SMC operations, like secure sum, secure set union, secure size of set intersection, and scalar product, that are useful for many data mining tasks. In contrast, PPDP does not perform the actual data mining task, but concerns with how to publish the data so that the anonymous data are useful for data mining. We can say that PPDP protects privacy at the data level while PPDDM protects privacy at the process level. They address different privacy models and data mining scenarios. In the field of statistical disclosure control (SDC), the research works focus on privacy-preserving publishing methods for statistical tables. SDC focuses on three types of disclosures, namely identity disclosure, attribute disclosure, and inferential disclosure. Identity disclosure occurs if an adversary can identify a respondent from the published data. Revealing that an individual is a respondent of a data collection may or may not violate confidentiality requirements. Attribute disclosure occurs when confidential information about a respondent is revealed and can be attributed to the respondent. Attribute disclosure is the primary concern of most statistical agencies in deciding whether to publish tabular data. Inferential disclosure occurs when individual information can be inferred with high confidence from statistical information of the published data.Some other works of SDC focus on the study of the non-interactive query model, in which the data recipients can submit one query to the system. This type of non-interactive query model may not fully address the information needs of data recipients because, in some cases, it is very difficult for a data recipient to accurately construct a query for a data mining task in one shot. Consequently, there are a series of studies on the interactive query model, in which the data recipients, including adversaries, can submit a sequence of queries based on previously received query results. The database server is responsible to keep track of all queries of each user and determine whether or not the currently received query has violated the privacy requirement with respect to all previous queries. One limitation of any interactive privacy-preserving query system is that it can only answer a sublinear number of queries in total; otherwise, an adversary (or a group of corrupted data recipients) will be able to reconstruct all but 1 . o(1) fraction of the original data, which is a very strong violation of privacy. When the maximum number of queries is reached, the query service must be closed to avoid privacy leak. In the case of the non-interactive query model, the adversary can issue only one query and, therefore, the non-interactive query model cannot achieve the same degree of privacy defined by Introduction the interactive model. One may consider that privacy-reserving data publishing is a special case of the non-interactivequery model.This paper presents a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explains their effects on Data Privacy. k-anonymity is used for security of respondents identity and decreases linking attack in the case of homogeneity attack a simple k-anonymity model fails and we need a concept which prevent from this attack solution is l-diversity. All tuples are arranged in well represented form and adversary will divert to l places or on l sensitive attributes. l-diversity limits in case of background knowledge attack because no one predicts knowledge level of an adversary. It is observe that using generalization and suppression we also apply these techniques on those attributes which doesn’t need th is extent of privacy and this leads to reduce the precision of publishing table. e-NSTAM (extended Sensitive Tuples Anonymity Method) is applied on sensitive tuples only and reduces information loss, this method also fails in the case of multiple sensitive tuples.Generalization with suppression is also the causes of data lose because suppression emphasize on not releasing values which are not suited for k factor. Future works in this front can include defining a new privacy measure along with l-diversity for multiple sensitive attribute and we will focus to generalize attributes without suppression using other techniques which are used to achieve k-anonymity because suppression leads to reduce the precision ofpublishing table.译文：数据挖掘和数据发布数据挖掘中提取出大量有趣的模式从大量的数据或知识。
文献综述写法英文版
Finding, formulating and exploring your topic.Different topic creationsMany students have in mind something that they want to work on; others want to work with a particular scholar or research centre. In the first case, students search for a compatible supervisor. In the second, for a topic. Regardless of these preliminary circumstances, the topic is very likely only roughly formulated at this stage. This is usually enough to have your enrolment accepted. Reading the literatureOnce you have a general idea, you could start by talking to your supervisor and other scholars. But, most importantly, you have to think why you would like to work on it, or why anyone would want to do so. Ask yourself, "Why is it important? What is interesting about this? Suppose I solve it, or find it, or pull it all together, what use is it? What is its significance?" Then, with some questions such as these in mind, go and read more about it to see what is there and find out what aspects of it have been exhausted, what neglected, what the main ideas, issues and controversies are in the area. It is regarded as your supervisor's role to direct you to the most fruitful starting point in reading and surveying the literature.Cycle of literature reviewAll of this is not a once only activity, but is a cycle you go through again and again. So you read, think, and discuss it with your supervisor and then, as a result, come closer to the formulation of the topic. And then with each cycle of reading, thinking and discussing your topic becomes more specific and focused.This is not the final formulation and the last time you will focus your topic. But you could probably let go of this round of general exploration and embark on the next stage. Your supervisor by this time should have enough of an idea of your topic to judge whether or not what you propose to do is feasible within the time available and has the potential to meet the required standards for a PhD. To see the full potential of your topic or, to the contrary, see that it is not going to deliver what you wanted, you do need to begin doing your research. This, of course, is why pilot studies are often undertaken.Making sense of the literatureWe do truly wish we could tell you about a reliable or simple way to make sense of the literature. We can say, however, that you need to attend to things attwo levels:∙One is establishing a system that will allow you to organise the hard copies of the articles etc., and develop a data base for references, soyou have easy access under relevant categories and don't chase the samereferences repeatedly.∙The other is the more demanding task of understanding and using the literature for your purposes.Without attending to the first task, you could easily become inefficient and frustrated. However, although it is necessary to have some way of keeping track, don't spend all your energies on perfecting your system. It may be a good idea to attend a course for researchers on handling information. Check whether your university's library or computer centre offers such a course.The other task ahead of you - of understanding, reviewing and using theliterature for your purposes - goes to the heart of your thesis. We considerthis in three stages.Making sense of the literature - first passWhen you first come to an area of research, you are filling in the backgroundin a general way, getting a feel for the whole area, an idea of its scope,starting to appreciate the controversies, to see the high points, and to become more familiar with the major players. You need a starting point. This may come out of previous work you've done. If you're new to the area, your supervisor could suggest fruitful starting points. Or you could pursue some recent review articles to begin.Too much to handleAt this stage there seems to be masses of literature relevant to your research. Or you may worry that there seems to be hardly anything. As you read, think about and discuss articles and isolate the issues you're more interested in. In this way, you focus your topic more and more. The more you can close in on what your research question actually is, the more you will be able to have a basisfor selecting the relevant areas of the literature. This is the only way to bring it down to a manageable size.Very little thereIf initially you can't seem to find much at all on your research area - and you are sure that you've exploited all avenues for searching that the library can present you with - then there are a few possibilities:∙You could be right at the cutting edge of something new and it's not surprising there's little around.∙You could be limiting yourself to too narrow an area and not appreciating that relevant material could be just around the corner in a closely related field.∙Unfortunately there's another possibility and this is that there's nothing in the literature because it is not a worthwhile area ofresearch. In this case, you need to look closely with your supervisor at what it is you plan to do.Quality of the LiteratureThis begins your first step in making sense of the literature. You are not necessarily closely evaluating it now; you are mostly learning through it. But, sometimes at this stage students do ask us how they can judge the quality ofthe literature they're reading, as they're not experts.You learn to judge, evaluate, and look critically at the literature by judging, evaluating and looking critically at it. That is, you learn to do so by practising. There is no quick recipe for doing this but there are somequestions you could find useful and, with practice, you will develop many others:∙Is the problem clearly spelled out?∙Are the results presented new?∙Was the research influential in that others picked up the threads and pursued them?∙How large a sample was used?∙How convincing is the argument made?∙How were the results analysed?∙What perspective are they coming from?∙Are the generalisations justified by the evidence on which they are made?∙What is the significance of this research?∙What are the assumptions behind the research?∙Is the methodology well justified as the most appropriate to study the problem?∙Is the theoretical basis transparent?In critically evaluating, you are looking for the strengths of certain studies and the significance and contributions made by researchers. You are also looking for limitations, flaws and weaknesses of particular studies, or of whole lines of enquiry.Indeed, if you take this critical approach to looking at previous research in your field, your final literature review will not be a compilation of summaries but an evaluation. It will then reflect your capacity for critical analysis.Making sense of the literature - second passYou continue the process of making sense of the literature by gaining more expertise which allows you to become more confident, and by being much more focused on your specific research.You're still reading and perhaps needing to re-read some of the literature.You're thinking about it as you are doing your experiments, conducting your studies, analysing texts or other data. You are able to talk about it easily and discuss it. In other words, it's becoming part of you.At a deeper level than before,∙you are now not only looking at findings but are looking at how others have arrived at their findings;∙you're looking at what assumptions are leading to the way something is investigated;∙you're looking for genuine differences in theories as opposed to semantic differences;∙you also are gaining an understanding of why the field developed in the way it did;∙you have a sense for where it might be going.First of all you probably thought something like, "I just have to get a handle on this". But now you see that this 'handle' which you discovered for yourself turns out to be the key to what is important. You are very likely getting to this level of understanding by taking things to pieces and putting them back together.For example, you may need to set up alongside one another four or five different definitions of the same concept, versions of the same theory, or different theories proposed to account for the same phenomenon. You may need to unpack them thoroughly, even at the very basic level of what is the implied understanding of key words (for example 'concept', 'model', 'principles' etc.), before you can confidently compare them, which you need to do before synthesis is possible.Or, for example, you may be trying to sort through specific discoveries which have been variously and concurrently described by different researchers in different countries. You need to ask questions such as whether they are the same discoveries being given different names or, if they are not the same,whether they are related. In other words, you may need to embark on very detailed analyses of parts of the literature while maintaining the general picture.Making sense of the literature - final passYou make sense of the literature finally when you are looking back to place your own research within the field. At the final pass, you really see how your research has grown out of previous work. So now you may be able to identify points or issues that lead directly to your research. You may see points whose significance didn't strike you at first but which now you can highlight. Or you may realise that some aspect of your research has incidentally provided evidence to lend weight to one view of a controversy. Having finished your own research, you are now much better equipped to evaluate previous research in your field.From this point when you have finished your own research and you look back and fill in the picture, it is not only that you understand the literature and can handle it better, but you could also see how it motivates your own research. When you conceptualise the literature in this way, it becomes an integral part of your research.Writing the literature reviewWhat we are talking about here is the writing of the review. We assume that you have made sense of the literature, and that you know the role of the literature and its place in your thesis. Below are links to other sections covering these aspects.You will doubtless write your literature review several times. Since each version will serve a different purpose, you should not think you are writing the same thing over and over and getting nowhere. Where you may strike trouble is if you just try to take whole sections out of an earlier version and paste them into the final version which, by now, has to be differently conceived.In practical terms, it is necessary to have an overall picture of how the thread runs through your analysis of the literature before you can get down to actually writing a particular section. The strategy which writers use as a way to begin the literature review is to proceed from the general, wider view of the research you are reviewing to the specific problem. This is not a formula but is a common pattern and may be worth trying.Let's look at an example taken from the first pages of a literature review. This shows us the progression from general to specific and the beginning of that thread which then continues through the text leading to the aims.[This is basically an introductory section, which starts with astatement of the problem in very broad terms, alerting us to the factthat not everything is rosy, and proceeds to sketch in specific aspects.][The text moves on to specify issues at various levels. Although the[Thus the text has set up the situation where all aspects of theproblem--theoretical, practical, etc.--are brought together.]Whatever the pattern which fits your work best, you need to keep in mind that what you are doing is writing about what was done before. But, you are not simply reporting on previous research. You have to write about it in terms of how well it was done and what it achieved. This has to be organised and presented in such a way that it inevitably leads to what you want to do and shows it is worth doing. You are setting up the stage for your work.demonstrates neither your understanding of the literature nor your ability to evaluate other people's work.Maybe at an earlier stage, or in your first version of your literature review, you needed a summary of who did what. But in your final version, you have to show that you've thought about it, can synthesise the work and can succinctly pass judgement on the relative merits of research conducted in your field. So,Approaching it in this way forces you to make judgements and, furthermore, to distinguish your thoughts from assessments made by others. It is this whole process of revealing limitations or recognising the possibility of taking research further which allows you to formulate and justify your aims.Keep your research focusedIt is always important to keep your research focused, but this is especially so at two points. First when you have settled into the topic and the time forwider exploration has to end. And then again at a later stage when you may have gathered lots of data and are starting to wonder how you are going to deal with it all.Focus after literature reviewFirst, it is a common temptation to prolong the exploration phase by finding more and more interesting things and straying away from what was once regarded as the possible focus. Either you or your supervisor could be guilty of this.In some cases, it might be you who is putting off having to make a commitmentto one line of enquiry because exploration and realising possibilities is enjoyable and you're always learning more. In other cases, it could be your supervisor who, at every meeting, becomes enthusiastic about otherpossibilities and keeps on suggesting alternatives. You might not be sure ifthis is just sharing excitement with you or if you are supposed to follow them all up.Either way you need to stop the proliferation of lines of enquiry, sift through what you have, settle on one area, and keep that focus before you. It could even be a good idea to write it up on a poster in front of your desk. Unlessyou have this really specified in the first place, with the major question and its sub-questions, and you know exactly what you have to find out to answer these, you will never be focused and everything you find will seem to be 'sort of' relevant.You have to close off some lines of enquiry and you can do so only once you decide they are not relevant to your question. We continually meet students who, when we ask, "So what is the question you're researching?", will answer, "My topic is such and such and I'm going to look at x, y and z". Sometimes further probing from us will reveal that they do indeed have a focus, but many timesthis is not so. Thinking in terms of your topic is too broad. You need to think,Focus after data collectionThen, at a later stage, you could find yourself surrounded by lots of datawhich you know are somewhat relevant to your project, but finding the ways of showing this relevance and using the data to answer your question could be a difficult task. Now you have to re-find your focus to bring it all together.Again, it is your research question and sub-questions which will help youto do this because your whole thesis is basically the answer to these questions, that is, the solution to the problem you presented at the beginning. This may strike you as a very simplistic way to view it. However, approaching it in this way does help to bring the parts together as a whole and get the whole to work. We even recommend that, to relate the parts to each other and keep yourself focussed , you could tell yourself the story of the thesis.Making a deliberate attempt to keep focused will help you to shape your research and keep you motivated.Apparently I have to write a research proposal. What do I need to do? The main purpose of a research proposal is to show that the problem you propose to investigate is significant enough to warrant the investigation, the method you plan to use is suitable and feasible, and the results are likely to prove fruitful and will make an original contribution. In short, what you are answering is 'will it work?'The level of sophistication or amount of detail included in your proposal will depend on the stage you are at with your PhD and the requirements of your department and University.∙In initial stages, the document you need to write will probably be three to five pages long. It will give a general idea of what you areproposing to do but it isn't a binding contract. Often it serves as astarting point for discussions with your supervisor to firm up the topic, methodology and mechanics of your research.∙Some of you will be required to write a proposal at the time of confirming your candidature (usually at the end of the first year). Insome instances, this is a document of four to five pages and may beviewed as a mere formality. In other cases a much more substantialdocument of 30 - 40 pages is expected. Therefore it is essential for you to check the requirements with your department.Regardless of the above distinctions you should never see writing a proposal as a worthless chore. Indeed, if it isn't formally required, it is a very good idea to write one anyway. You can use it to your advantage. It always forces you to think about your topic, to see the scope of your research, and to review the suitability of your methodology. Having something in writing also gives an opportunity to your supervisor to judge the feasibility of the project (whether it is possible to finish in time, costs, the equipment needed and other practicalities, time needed for supervision), to assess its likelihood of success, and its ability to meet the academic standard required of a PhD thesis.While there are no hard and fast rules governing the structure of a proposal, a typical one would include: aims and objectives, significance, review of previous research in the area showing the need for conducting the proposed research, proposed methods, expected outcomes and their importance. In experimentally based research it often includes detailed requirements for equipment, materials, field trips, technical assistance and an estimation of the costs. It could also include an approximate time by which each stage is to be completed.write a abstract. Indeed, the final version of the abstract will need to be written after you have finished reading your thesis for the last time.However, if you think about what it has to contain, you realise that the abstract is really a mini thesis. Both have to answer the following specific questions:Therefore, an abstract written at different stages of your work will help you to carry a short version of your thesis in your head. This will focus your thinking on what it is you are really doing , help you to see the relevance of what you are currently working on within the bigger picture, and help to keep the links which will eventually unify your thesis.ProcessThe actual process of writing an abstract will force you to justify and clearly state your aims, to show how your methodology fits the aims, to highlight the major findings and to determine the significance of what you have done. The beauty of it is that you can talk about this in very short paragraphs and seeif the whole works. But when you do all of these things in separate chapters you can easily lose the thread or not make it explicit enough.If you have trouble writing an abstract at these different stages, then this could show that the parts with which you are having a problem are not well conceptualised yet.We often hear that writing an abstract can't be done until the results are known and analysed. But the point we are stressing is that it is a working tool that will help to get you there.Before you know what you've found, you have to have some expectation of what you are going to find as this expectation is part of what is leading you to investigate the problem. In writing your abstract at different stages, any part you haven't done you could word as a prediction. For example, at one stage you could write, "The analysis is expected to show that …". Then, at the next stage, you would be able to write "The analysis showed that …." or "Contrary to expectation, the analysis showed that …..".The final, finished abstract has to be as good as you can make it. It is the first thing your reader will turn to and therefore controls what the first impression of your work will be. The abstract has∙to be short-no more than about 700 words;∙to say what was done and why, how it was done, the major things that were found, and what is the significance of the findings (rememberingthat the thesis could have contributed to methodology and theory aswell).In short, the abstract has to be able to stand alone and be understood separately from the thesis itself.Is there a particular thesis structure I have to follow? There are certain conventions specific to certain disciplines. However, these structures are not imposed on a piece of work. There are logical reasons why there is a conventional way of structuring the thesis, which is after all the account of what you've achieved through your research. Research is of course not conducted in the step-by-step way this structure suggests, but it gives the reader the most accessible way of seeing why this research was done, how it was done and, most importantly, what has been achieved. If you put side by side all the questions you had to answer to finish your research and what is often proposed as a typical structure of a thesis, then you see the logic of the arrangement. That does not mean, however, that you have to name your chaptersin this way. In some disciplines, it very often is like this; in others, this structure is implied. For example, in many science theses, the following basically is the structure; in many humanities theses, the final structure looks very different, although all of these questions are answered one way or another.Occasionally a thesis is written which does not in any way comply with this structure. Generally the reasons you want to have a recognised, transparent structure are that, to some extent, it is expected and the conventional structure allows readers ready access to the information. If, however, you wantto publish a book based on the thesis, it is likely the structure would need to be altered for the different genre and audience.。
大数据的英文作文
大数据的英文作文Title: The Impact of Big Data on the Modern World。
Introduction。
In the age of information, the concept of big data has become increasingly significant. It refers to the vast amount of structured and unstructured data that inundates individuals and organizations on a daily basis. This essay explores the profound impact of big data on various aspects of our modern world.Big Data in Business。
In the realm of business, big data has revolutionized decision-making processes. Companies now harness data analytics tools to extract valuable insights from massive datasets, enabling them to make informed strategic decisions. For instance, retailers analyze customer purchasing patterns to optimize product placement andmarketing strategies, thereby enhancing profitability.Moreover, big data has transformed marketing practices. Through advanced analytics, businesses can personalize marketing campaigns based on individual preferences and behavior, leading to higher customer engagement and conversion rates. Additionally, data-driven insights enable companies to anticipate market trends and stay ahead of competitors in today's dynamic business environment.Big Data in Healthcare。
literaturereview文献综述的写法（英文版）
literaturereview⽂献综述的写法（英⽂版）Literature ReviewThis packet details the steps necessary to produce a literature review that may be required for work in various disciplines, including English, history and psychology. This packet is not intended to replace instructor guidelines and should not be used in that manner. The packet’s intended use is as a supplement to classroom instruction on assembling a literature review. Therefore, it contains only general information that must be tailored to fit specific guidelines as required by your discipline and by your instructor.This packet is subdivided into six sections:I. General InformationStates what a literature review is and what purpose it serves.II. ProcessGives step-by-step instructions on how to get started on your literature review.III. OrganizationExplains the two most common ways of arranging information in a literature review.IV. FormatProvides descriptions for two of the most common formats used in a literature review, the item to item comparison and contrast (Format A) and the criteria to criteria comparison and contrast (Format B).V. ChecklistAllows appraisal of your completed literature review to assure that it follows all necessary guidelines.VI. ResourcesLists helpful resources used to compile this packet so that you may obtain further information.M General Information MDefinitionLiterature reviews can have two roles: In their first role, they function as a stand-alone paper. At other times they will actually be part of a larger research thesis. In this handout, literature reviews will be referred to in the stand-alone sense. As a stand-alone paper, literature reviews are multi-layered and are more formal and detailed than book reviews. As the author of a literature review, you must become familiar with a large amount of research on a specific topic. You will then develop your own thesis about the topic related to this research. After this, you will classify and critically analyze research on the topic by making a comparison between several different studies and by emphasizing how these studies and their comparison relate to your own thesis.In effect, a literature review is a paper that compiles, outlines and evaluates previously established research and relates it to your own thesis. It provides a context for readers as if theywere researching the topic on their own. Just from reading your paper, readers should be able to gain insight into the amountand quality of research on the topic. Your thesis and the literature reviewed serve several important functions within the paper:Your thesis creates a foundation for the literature review because it helps narrow the topic by providing a sense of direction; however, you will have to conduct some initial research and reading before deciding on an appropriate thesis. Your personal thesis may be a statement addressing some of the following situations: “why your research needs to be carried out, how you came to choose certain methodologies or theories to work with, how your work adds to the research already carried out”(Brightwell, G. and Shaw, J., 1997-98), or it may present some other logical perspective.Reviewed literature is organized in a logical manner that best suits the topic of the review and the hypothesis of the literature (see Organization and Format). The selected method of organization and style of format should draw attention to similarities and differences among the reviewed literature; these similarities and differences are based on specific criteria you revealed in the literature review’s introduction. According to Brightwell and Shaw (1997-98), your goal in the body of the review “. . . should be to evaluate and show relationships between the work already done (Is Researcher Y’s theory more convincing than Researcher X’s? Did Researcher X build on the work of Researcher Y?) and between this work and your own [thesis].” Additional information on these topics can be found in the Organization and Format sections of this packet. Therefore, carefully planned organization is an essential part of any literature review.PurposeAlthough literature reviews may vary according to discipline, their overall goal is similar. A literature review serves as a compilation of the most significant sources on a subject and relates the findings of each of these sources in a rational manner while supporting the literature review author’s own thesis. A literature review establishes which sources are most relevant to its author’s point and which sources are most credible to the discipline at hand.In a literature review, the results of previous research are summarized, organized and evaluated. Discipline-SpecificityA literature review’s organization, format, level of detail and citation style may vary according to discipline because different disciplines have different audiences. Examples here pertain to the natural sciences, social sciences and humanities.Natural and social sciences The author of a literature review in the natural or social sciences must pay close attention to measurements, study populations and technical aspects of experimental findings. Typically, a portion of the natural or social sciences literature review is set aside for reviewing sources on the primary topic. Then, a comparative analysis or discussion section is used to analyze the similarities and differences among the sources, tying them in with the literature review author’s original thesis.Humanities The author of a literature review in the humanities usually does not set aside a special section for reviewing the sources; instead, citations may be found randomly throughout the paper. The literature being reviewed is arranged according to paragraphs based on the author’s points, which in turn, support the author’s thesis. The paper itself may not be called a literature review at all. It is more likely to be called a critical analysis.Remember that the best bet for determining what type of literature review is appropriate for your course is checking with the instructor prior to beginning research.REVIEW1. What is the purpose of a literature review? What is the connection between theauthor’s thesis and the literature being reviewed?2. What discipline will your literature review be classified in?M Process M1. Find several articles that deal with your research topic. Sometimes it is helpful to review the bibliography of one of the first scholarly sources that you encounter and compare it to the bibliographies of other sources on the topic. If the same source is listed within several of these bibliographies, it is probably a fundamental, credible source that will aid you in your review.2. Before you begin reviewing literature, realize that you are looking to accomplish two things:A. Defining your research problem/thesis (examples: finding a flaw inresearch, continuing previous research, etc . . .)B. Reading and evaluating significant works that are relevant to yourresearch problem.You will be conducting Steps A and B simultaneously because the two form a circular pattern. As you read related sources (Step B), you define your problem, and as you define your problem (Step A) you will more easily be able to decide what material is relevant enough to be worthy of reading (Step B).3. Once you begin reviewing, make an entry with complete bibliographical information and comments for each work that you are going to include in the review.4. Compare the articles by evaluating the similarities and differences among them. This will be the initial stage in the formulation of your thesis.5. Form a thesis that is clearly written and can be logically supported by the literature you will include in your review.6. View the articles briefly again and jot down any notes that seem to relateto your thesis.7. Decide which organizational pattern and format are best for the topic of your review.8. Construct an appropriate outline for the literature review.9. Write an introduction that introduces the topic, reveals your thesis statement, and arranges key issues.10. Organize and write the body of your paper according to the appropriate format: topical or chronological.11. Write a conclusion that reconciles similarities and differences on the topic and reemphasizes the criteria used to arrive at this conclusion./doc/8b508cc25fbfc77da269b1c1.html plete the final draft of the literature review.13. Check over the final draft for grammar and punctuation errors.14. Use the checklist provided here to make sure that all parts of the literature review are addressed and focused. REVIEW1. What do you consider to be the most crucial step(s) in the process of your literature review ? Why? Justify your response(s).A literature review can be arranged either topically or chronologically.Topical organization occurs in reviews where previous research being evaluated is divided into segments with each one representing a part of some larger issue. In a topical review, the author begins by describing the characteristics of research shared by several studies and then moves on to analyze their similarities and differences. For more information, see the example below.ExampleThe organization of a literature review begins in the introduction. For example, in the introduction of a literature review about the effect of seating arrangements on peer tutoringcommunication, you would first introduce the topic and what your literature review will attempt to assess:…Writing centers can set the table for collaborative tutoring sessions through a careful consideration of spatial arrangement . . .Then state what angle is going to be explored:…These studies will be used to support the author’s claims that spatial arrangement is instrumental in encouraging collaborative environments in the writing center…Then, arrange key issues that will be addressed in this review by answering questions that you have personally developedand are tailored to fit your topic. In the introduction, give the audience a clear picture of how you will organize your paper: Establishing a Critical Response for a Literature ReviewYou may find this section helpful at Steps 3, 4 and 5 of the process. When reviewing your sources, explore the following areas to help develop your critical response:What is the purpose of the research or work?What research or literary methods are used?How do the major concepts operate?In a research study, how accurate are the measurements?In a literary work, is the author’s position objective or biased? What are the different interpretations of the results of the study or of the literary work itself?M Organization MIn the following, I1 first review some relevant research concerning spatial arrangement and then discuss some recommended and alternate seating arrangements to encourage a collaborative environment in the writing center. Finally, I include some other considerations.In the body of this literature review, you would organize the information topically around each point (or question) that you asked yourself:-Research Review-Recommended Spatial Arrangement-Alternative Spatial Arrangement-Other ConsiderationsThen, write a conclusion that explains the significance of your findings:…While the seating arrangements outlined above are generally a good ‘setting’ for peer tutoring sessions, we should remember that each tutoring session is unique. Not all students will be comfortable with a side-by-side spatial arrangement at a round table. Tutors should be perceptive of and receptive to students who may have other spatial needs…Chronological organization occurs when a review is organized in time order and is most often used when a historical context is needed for discussing a topic from its beginning to its current state; chronological organization is especially helpful when discussing inactive periods and shifts in perspective on a given topic.ExampleThe organization of a literature review begins in the introduction. For example, in the introduction of a literature review entitled Development of Social Science Research on Attitudes Towards Gender in America, you would first introduce the topic and what your review hopes to assess:…This literature review will assess the development of research designed to uncover gender attitudes in America during the latter part of the 20th Century…Then state what angle is going to be explored:..As research progressed throughout the 20th century, the methods that social scientists use for measuring these attitudes developed and changed as well…Then, chronologically arrange issues that will be addressed in this review:Gender stereotypes still exist today, and varying attitudes can be traced over the past fifty years. Survey instruments used to gather data on these varying attitudes have also changed drastically over the course of time.In the body of this literature review, you would organize the information chronologically, adressing each point (or question)that is being asked for a particular time period:-Stereotypes and Survey Instruments of the 1950’s-Stereotypes and Survey Instruments of the 1960’s-Stereotypes and Survey Instruments of the 1970’s-Stereotypes and Survey Instruments of the 1980’s-Stereotypes and Survey Instruments of the 1990’s-Current Advancements1 Always clear the use of I with your instructor. An alternative to this would be the use of third person wording, such as “This paper reviews some relevant research concerning spatial arrangement and then discusses some recommended and alternate seating arrangements to encourage a collaborative environment in the writing center.”Then, write a conclusion that explains the significance of your findings:Although the survey instruments used in the 1950’s and 1960’s developed an obvious bias when surveying Americans regarding gender attitudes, the 1970’s brought about great change. Today social scientists are more careful than ever about testing the quality of a survey instrument before using it on the general public.M Format MThere are also two suggested formats for composing your literature review. Format A is used when comparing several studies that have similar hypothesis but different findings. Each piece of research is summarized individually. Format A is good for reviews with a small number of entries; however, this format may confuse the audience when used with a large number of reviews because descriptions of so many studies may get in the way of the analysis. Keep in mind that each piece of research usually will not receive equal attention in the review.Format A OutlineI. Introduction consists of four parts that are usually discussed in one paragraph.a. Identify the general topic being discussed.b. Mention trends published about the topic.c. State thesis establishing the reason for writing the literaturereview.d. Explain criteria by giving a description of each of the criteria used in evaluating theliterature review and rationalizing its organizationII. Literature reviewed section is divided up according to study.a.First study is summarized and discussed.b.Second study is summarized and discussed.c. Third study is summarized and discussed.III. Comparative analysis acknowledges the similarities and differences between studies.a. Similarities (if any) among the studies are evaluated and discussed.b. Differences (if any) among the studies are evaluated and discussed.IV. Conclusion/Summary effectively wraps up the review.a.Summarize points of comparison or contrast among the works based on Section IIIof your review.b.Provide insight of relationship between the topic of the review and a larger area ofstudy such as a specific discipline or professionFormat B organizes the literature review according to similarities and differences among research rather than by literature studied. In a review organized according to Format B, little background information on the literature being reviewed is given outright. Instead, it is worked into the body paragraphs of the sections on similarities and differences. The conclusion then uses these two sections (similarities and differences) to tie in points of comparison and contrast between the works. Format B better suits papers that are topically organized. Format B is outlined below.Format B OutlineI. Introduction consists of four parts usually discussed in one paragraph.a. Identify the general topic being discussed.b. Mention trends published about the topic.c. State thesis establishing the reason for writing the literaturereview.d. Explain criteria by giving a description of each of the criteria used in evaluating theliterature review and rationalizing its organizationII.Similiarities within the research are discussed.a. First similarity among research is discussed.b. Second similarity among research is discussed.c. Third similiarity among research is discussed.III. Differences in the research are discussed.a.First difference between research is discussedb.Second difference between research is discussedc.Third difference between research is discussedIV. Conclusion/Summarya. Summarize points of comparison or contrast between the works.b. Provide insight into relationship between the topic of the literature and a largerarea of study such as a specific discipline or profession.The most important thing to remember when organizing a literature review is that it is not a list summarizing one work after another. The review should be organized into sections according to theme that are set apart by subject-related headings. REVIEW1.Which format have you chosen for your literature review? Why?M A Literature Review Checklist: MDid I . . . . . . ?□ Establish a valid thesis based on the examined research□ State this thesis clearly in my introduction□ Define unfamiliar terms□ Incorporate background information to define the problem□ Begin each entry in the review with a complete bibliographical reference□ List and describe the hypothesis/thesis in each work reviewed□ Describe the outcome of the work or the research□ Develop and incorporate my own comments, including response to the research, similarities and differences among literature reviewed, and reservations regarding author’s methods or conclusions□ Avoid overquoting□ Check for grammar and punctuation errors□ Correctly cite all references in uniform documentation styleM Resources MBrightwell, G. and Shaw, J. (1997-98). Writing up research. RetrievedAugust 20, 2002 from Languages and Educational Development at theAsian Institute of Technology’s Web page at/doc/8b508cc25fbfc77da269b1c1.html nguages.ait.ac.th/EL21OPEN.HTMCentral Queensland University Library. (2000). The literature review. RetrievedJuly 22, 2003 from /doc/8b508cc25fbfc77da269b1c1.html .au/litreviewpages/Cuba, L. (2002). A short guide to writing about social science. New York:Addison-Wesley Publishers.Leibensperger, S. (2003). Setting the table: Encouraging collaborative environments with spatial arrangement in the writing center. Unpublished literature review.Northern Arizona University. (1999). Electronic textbook - A blast from thepast: Your literature review. Retrieved May 30, 2002 from/doc/8b508cc25fbfc77da269b1c1.html /~mid/edr720/class/literature/blast/reading2-1-1.htmlTaylor, D., & Procter, M. (2001). The literature review: A few tips onconducting it. Retrieved June 17, 2002 fromhttp://www.utoronto.ca/writing/litrev.htmlTrinder, L. (2002). Appendix. The literature review. Retrieved August 27, 2003/doc/8b508cc25fbfc77da269b1c1.html /~w071/teaching/ppf/Appendix%20Lit%20Review.pdfThe University of Wisconsin-Madison Writing Center. (2001). Academicwriting: Reviews of literature. Retrieved May 30, 2002 from/doc/8b508cc25fbfc77da269b1c1.html /writing/Handbook/ReviewofLiterature.html*In traditional APA style, this section would be entitled “References” and would be listed on a separate page double-spaced. Due to space constraints in this packet, it has been formatted differently.Copyright 2003 by the Academic Center and the University of Houston-Victoria.Created 2003 by Candice Chovanec-Melzow.。
文献综述英文格式
文献综述英文格式The Impact of Artificial Intelligence on Healthcare。
Introduction。
Artificial intelligence (AI) has revolutionized various industries, and healthcare is no exception. With its ability to analyze vast amounts of data, AI has the potential to transform healthcare delivery, improve patient outcomes, and enhance the efficiency of healthcare systems. This article aims to provide a comprehensive review of the impact of AI on healthcare, covering various aspects such as diagnosis, treatment, patient monitoring, and healthcare management.1. AI in Diagnosis。
AI has shown great promise in improving the accuracy and efficiency of medical diagnosis. Machine learning algorithms can analyze medical images, such as X-rays, CT scans, and MRIs, to detect abnormalities and assist radiologists in making accurate diagnoses. For example, a deep learning algorithm developed by researchers at Stanford University achieved a level of accuracy comparable to human dermatologists in identifying skin cancer from images. AI-powered diagnostic tools can help reduce diagnostic errors, speed up the diagnosis process, and enable early detection of diseases.2. AI in Treatment。
大数据应用的英文作文
大数据应用的英文作文Title: The Application of Big Data: Transforming Industries。
In today's digital age, the proliferation of data has become unprecedented, ushering in the era of big data. This vast amount of data holds immense potential,revolutionizing various sectors and industries. In this essay, we will explore the applications of big data and its transformative impact across different domains.One of the primary areas where big data has made significant strides is in healthcare. With the advent of electronic health records (EHRs) and wearable devices, healthcare providers can now collect and analyze vast amounts of patient data in real-time. This data includesvital signs, medical history, genomic information, and more. By applying advanced analytics and machine learning algorithms to this data, healthcare professionals canidentify patterns, predict disease outbreaks, personalizetreatments, and improve overall patient care. For example, predictive analytics can help identify patients at risk of developing chronic conditions such as diabetes or heart disease, allowing for proactive interventions to prevent or mitigate these conditions.Another sector that has been transformed by big data is finance. In the financial industry, data-driven algorithms are used for risk assessment, fraud detection, algorithmic trading, and customer relationship management. By analyzing large volumes of financial transactions, market trends, and customer behavior, financial institutions can make more informed decisions, optimize investment strategies, and enhance the customer experience. For instance, banks employ machine learning algorithms to detect suspicious activities and prevent fraudulent transactions in real-time, safeguarding both the institution and its customers.Furthermore, big data has revolutionized the retail sector, empowering companies to gain deeper insights into consumer preferences, shopping behaviors, and market trends. Through the analysis of customer transactions, browsinghistory, social media interactions, and demographic data, retailers can personalize marketing campaigns, optimize pricing strategies, and enhance inventory management. For example, e-commerce platforms utilize recommendation systems powered by machine learning algorithms to suggest products based on past purchases and browsing behavior, thereby improving customer engagement and driving sales.The transportation industry is also undergoing a profound transformation fueled by big data. With the proliferation of GPS-enabled devices, sensors, andtelematics systems, transportation companies can collect vast amounts of data on vehicle performance, traffic patterns, weather conditions, and logistics operations. By leveraging this data, companies can optimize route planning, reduce fuel consumption, minimize delivery times, and enhance overall operational efficiency. For instance, ride-sharing platforms use predictive analytics to forecast demand, allocate drivers more effectively, and optimizeride routes, resulting in improved service quality and customer satisfaction.In addition to these sectors, big data is making significant strides in fields such as manufacturing, agriculture, energy, and government. In manufacturing, data analytics is used for predictive maintenance, quality control, and supply chain optimization. In agriculture, precision farming techniques enabled by big data help optimize crop yields, minimize resource usage, and mitigate environmental impact. In energy, smart grid technologies leverage big data analytics to optimize energy distribution, improve grid reliability, and promote energy efficiency. In government, big data is utilized for urban planning, public safety, healthcare management, and policy formulation.In conclusion, the application of big data is transforming industries across the globe, enabling organizations to make data-driven decisions, unlock new insights, and drive innovation. From healthcare and finance to retail and transportation, the impact of big data is profound and far-reaching. As we continue to harness the power of data analytics and machine learning, we can expect further advancements and breakthroughs that will shape the future of our society and economy.。
ai写文献综述英文版
ai写文献综述英文版Writing a literature review in English involves several steps and considerations. Here is a comprehensive guide on how to write a literature review:1. Understand the Purpose: The purpose of a literature review is to provide an overview and critical evaluation of existing research on a specific topic. It helps identify the current state of knowledge, gaps in research, and potential areas for further investigation.2. Select a Topic: Choose a specific research topicthat is relevant and interesting. It should have enough existing literature to review.3. Conduct a Literature Search: Use academic databases, search engines, and other relevant sources to gather scholarly articles, books, and other publications related to your topic. Ensure that your sources are recent and reputable.4. Organize Your Sources: Create a system to manage and organize your sources. This can be done using reference management software like EndNote or Mendeley. Keep track of the bibliographic information, including authors,publication dates, titles, and page numbers.5. Read and Evaluate: Read each source carefully and critically evaluate its relevance, credibility, and methodology. Take notes on key findings, arguments, and any gaps in the research.6. Identify Themes and Patterns: Look for common themes, ideas, and patterns across the literature. Group similar sources together based on their main arguments or findings.7. Develop an Outline: Create an outline for your literature review. It should include an introduction, main body paragraphs organized by themes, and a conclusion. The introduction should provide background information andstate the purpose of the review. The main body paragraphs should discuss the findings from each theme or subtopic.The conclusion should summarize the main points andhighlight any gaps or areas for future research.8. Write the Review: Start by writing an engaging introduction that provides context and states theobjectives of the review. In the main body paragraphs, present a synthesis of the literature, discussing the main findings, theories, and methodologies. Be sure tocritically analyze and evaluate the strengths and weaknesses of each source. Use smooth transitions between paragraphs to maintain a logical flow. In the conclusion, summarize the key points and provide suggestions for future research.9. Revise and Edit: Review your draft for clarity, coherence, and logical structure. Ensure that your arguments are well-supported by evidence. Check for grammar, spelling, and punctuation errors. Seek feedback from peersor professors to improve the quality of your review.10. Cite and Reference: Use the appropriate citation style (e.g., APA, MLA, or Chicago) to cite your sourceswithin the text and create a reference list or bibliography at the end of your review. Make sure to follow the specific formatting guidelines of the chosen citation style.In conclusion, writing a literature review in English requires careful planning, thorough research, critical analysis, and effective writing skills. By following these steps, you can create a comprehensive and well-structured literature review. Remember to acknowledge thecontributions of other researchers and avoid plagiarism by properly citing all sources used in your review.。
大数据挖掘外文翻译文献
文献信息：文献标题：A Study of Data Mining with Big Data（大数据挖掘研究）国外作者：VH Shastri，V Sreeprada文献出处：《International Journal of Emerging Trends and Technology in Computer Science》,2016,38(2):99-103字数统计：英文2291单词，12196字符；中文3868汉字外文文献：A Study of Data Mining with Big DataAbstract Data has become an important part of every economy, industry, organization, business, function and individual. Big Data is a term used to identify large data sets typically whose size is larger than the typical data base. Big data introduces unique computational and statistical challenges. Big Data are at present expanding in most of the domains of engineering and science. Data mining helps to extract useful data from the huge data sets due to its volume, variability and velocity. This article presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective.Keywords: Big Data, Data Mining, HACE theorem, structured and unstructured.I.IntroductionBig Data refers to enormous amount of structured data and unstructured data thatoverflow the organization. If this data is properly used, it can lead to meaningful information. Big data includes a large number of data which requires a lot of processing in real time. It provides a room to discover new values, to understand in-depth knowledge from hidden values and provide a space to manage the data effectively. A database is an organized collection of logically related data which can be easily managed, updated and accessed. Data mining is a process discovering interesting knowledge such as associations, patterns, changes, anomalies and significant structures from large amount of data stored in the databases or other repositories.Big Data includes 3 V’s as its characteristics. They are volume, velocity and variety. V olume means the amount of data generated every second. The data is in state of rest. It is also known for its scale characteristics. Velocity is the speed with which the data is generated. It should have high speed data. The data generated from social media is an example. Variety means different types of data can be taken such as audio, video or documents. It can be numerals, images, time series, arrays etc.Data Mining analyses the data from different perspectives and summarizing it into useful information that can be used for business solutions and predicting the future trends. Data mining (DM), also called Knowledge Discovery in Databases (KDD) or Knowledge Discovery and Data Mining, is the process of searching large volumes of data automatically for patterns such as association rules. It applies many computational techniques from statistics, information retrieval, machine learning and pattern recognition. Data mining extract only required patterns from the database in a short time span. Based on the type of patterns to be mined, data mining tasks can be classified into summarization, classification, clustering, association and trends analysis.Big Data is expanding in all domains including science and engineering fields including physical, biological and biomedical sciences.II.BIG DATA with DATA MININGGenerally big data refers to a collection of large volumes of data and these data are generated from various sources like internet, social-media, business organization, sensors etc. We can extract some useful information with the help of Data Mining. It is a technique for discovering patterns as well as descriptive, understandable, models from a large scale of data.V olume is the size of the data which is larger than petabytes and terabytes. The scale and rise of size makes it difficult to store and analyse using traditional tools. Big Data should be used to mine large amounts of data within the predefined period of time. Traditional database systems were designed to address small amounts of data which were structured and consistent, whereas Big Data includes wide variety of data such as geospatial data, audio, video, unstructured text and so on.Big Data mining refers to the activity of going through big data sets to look for relevant information. To process large volumes of data from different sources quickly, Hadoop is used. Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. Its distributed supports fast data transfer rates among nodes and allows the system to continue operating uninterrupted at times of node failure. It runs Map Reduce for distributed data processing and is works with structured and unstructured data.III.BIG DATA characteristics- HACE THEOREM.We have large volume of heterogeneous data. There exists a complex relationship among the data. We need to discover useful information from this voluminous data.Let us imagine a scenario in which the blind people are asked to draw elephant. The information collected by each blind people may think the trunk as wall, leg as tree, body as wall and tail as rope. The blind men can exchange information with each other.Figure1: Blind men and the giant elephantSome of the characteristics that include are:i.Vast data with heterogeneous and diverse sources: One of the fundamental characteristics of big data is the large volume of data represented by heterogeneous and diverse dimensions. For example in the biomedical world, a single human being is represented as name, age, gender, family history etc., For X-ray and CT scan images and videos are used. Heterogeneity refers to the different types of representations of same individual and diverse refers to the variety of features to represent single information.ii.Autonomous with distributed and de-centralized control: the sources are autonomous, i.e., automatically generated; it generates information without any centralized control. We can compare it with World Wide Web (WWW) where each server provides a certain amount of information without depending on other servers.plex and evolving relationships: As the size of the data becomes infinitely large, the relationship that exists is also large. In early stages, when data is small, there is no complexity in relationships among the data. Data generated from social media and other sources have complex relationships.IV.TOOLS:OPEN SOURCE REVOLUTIONLarge companies such as Facebook, Yahoo, Twitter, LinkedIn benefit and contribute work on open source projects. In Big Data Mining, there are many open source initiatives. The most popular of them are:Apache Mahout:Scalable machine learning and data mining open source software based mainly in Hadoop. It has implementations of a wide range of machine learning and data mining algorithms: clustering, classification, collaborative filtering and frequent patternmining.R: open source programming language and software environment designed for statistical computing and visualization. R was designed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand beginning in 1993 and is used for statistical analysis of very large data sets.MOA: Stream data mining open source software to perform data mining in real time. It has implementations of classification, regression; clustering and frequent item set mining and frequent graph mining. It started as a project of the Machine Learning group of University of Waikato, New Zealand, famous for the WEKA software. The streams framework provides an environment for defining and running stream processes using simple XML based definitions and is able to use MOA, Android and Storm.SAMOA: It is a new upcoming software project for distributed stream mining that will combine S4 and Storm with MOA.Vow pal Wabbit: open source project started at Yahoo! Research and continuing at Microsoft Research to design a fast, scalable, useful learning algorithm. VW is able to learn from terafeature datasets. It can exceed the throughput of any single machine networkinterface when doing linear learning, via parallel learning.V.DATA MINING for BIG DATAData mining is the process by which data is analysed coming from different sources discovers useful information. Data Mining contains several algorithms which fall into 4 categories. They are:1.Association Rule2.Clustering3.Classification4.RegressionAssociation is used to search relationship between variables. It is applied in searching for frequently visited items. In short it establishes relationship among objects. Clustering discovers groups and structures in the data.Classification deals with associating an unknown structure to a known structure. Regression finds a function to model the data.The different data mining algorithms are:Table 1. Classification of AlgorithmsData Mining algorithms can be converted into big map reduce algorithm based on parallel computing basis.Table 2. Differences between Data Mining and Big DataVI.Challenges in BIG DATAMeeting the challenges with BIG Data is difficult. The volume is increasing every day. The velocity is increasing by the internet connected devices. The variety is also expanding and the organizations’ capability to capture and process the data is limited.The following are the challenges in area of Big Data when it is handled:1.Data capture and storage2.Data transmission3.Data curation4.Data analysis5.Data visualizationAccording to, challenges of big data mining are divided into 3 tiers.The first tier is the setup of data mining algorithms. The second tier includesrmation sharing and Data Privacy.2.Domain and Application Knowledge.The third one includes local learning and model fusion for multiple information sources.3.Mining from sparse, uncertain and incomplete data.4.Mining complex and dynamic data.Figure 2: Phases of Big Data ChallengesGenerally mining of data from different data sources is tedious as size of data is larger. Big data is stored at different places and collecting those data will be a tedious task and applying basic data mining algorithms will be an obstacle for it. Next we need to consider the privacy of data. The third case is mining algorithms. When we are applying data mining algorithms to these subsets of data the result may not be that much accurate.VII.Forecast of the futureThere are some challenges that researchers and practitioners will have to deal during the next years:Analytics Architecture:It is not clear yet how an optimal architecture of analytics systems should be to deal with historic data and with real-time data at the same time. An interesting proposal is the Lambda architecture of Nathan Marz. The Lambda Architecture solves the problem of computing arbitrary functions on arbitrary data in real time by decomposing the problem into three layers: the batch layer, theserving layer, and the speed layer. It combines in the same system Hadoop for the batch layer, and Storm for the speed layer. The properties of the system are: robust and fault tolerant, scalable, general, and extensible, allows ad hoc queries, minimal maintenance, and debuggable.Statistical significance: It is important to achieve significant statistical results, and not be fooled by randomness. As Efron explains in his book about Large Scale Inference, it is easy to go wrong with huge data sets and thousands of questions to answer at once.Distributed mining: Many data mining techniques are not trivial to paralyze. To have distributed versions of some methods, a lot of research is needed with practical and theoretical analysis to provide new methods.Time evolving data: Data may be evolving over time, so it is important that the Big Data mining techniques should be able to adapt and in some cases to detect change first. For example, the data stream mining field has very powerful techniques for this task.Compression: Dealing with Big Data, the quantity of space needed to store it is very relevant. There are two main approaches: compression where we don’t loose anything, or sampling where we choose what is thedata that is more representative. Using compression, we may take more time and less space, so we can consider it as a transformation from time to space. Using sampling, we are loosing information, but the gains inspace may be in orders of magnitude. For example Feldman et al use core sets to reduce the complexity of Big Data problems. Core sets are small sets that provably approximate the original data for a given problem. Using merge- reduce the small sets can then be used for solving hard machine learning problems in parallel.Visualization: A main task of Big Data analysis is how to visualize the results. As the data is so big, it is very difficult to find user-friendly visualizations. New techniques, and frameworks to tell and show stories will be needed, as for examplethe photographs, infographics and essays in the beautiful book ”The Human Face of Big Data”.Hidden Big Data: Large quantities of useful data are getting lost since new data is largely untagged and unstructured data. The 2012 IDC studyon Big Data explains that in 2012, 23% (643 exabytes) of the digital universe would be useful for Big Data if tagged and analyzed. However, currently only 3% of the potentially useful data is tagged, and even less is analyzed.VIII.CONCLUSIONThe amounts of data is growing exponentially due to social networking sites, search and retrieval engines, media sharing sites, stock trading sites, news sources and so on. Big Data is becoming the new area for scientific data research and for business applications.Data mining techniques can be applied on big data to acquire some useful information from large datasets. They can be used together to acquire some useful picture from the data.Big Data analysis tools like Map Reduce over Hadoop and HDFS helps organization.中文译文：大数据挖掘研究摘要数据已经成为各个经济、行业、组织、企业、职能和个人的重要组成部分。
大数据英文版
大数据英文版Big Data (English Version)Introduction:Big data refers to the large and complex sets of data that cannot be easily managed, processed, or analyzed using traditional data processing techniques. It involves the collection, storage, and analysis of massive amounts of structured and unstructured data from various sources. This English version document aims to provide a comprehensive understanding of big data, its applications, challenges, and future prospects.1. Definition and Characteristics of Big Data:Big data is characterized by the "3Vs": volume, velocity, and variety. It refers to data that is too large to be processed using traditional methods, generated at high speeds, and comes in various formats such as text, images, videos, and sensor data. Additionally, big data possesses the following characteristics:1.1. Volume: Big data involves a massive amount of data that exceeds the processing capacity of conventional databases. It includes data from social media, e-commerce transactions, scientific research, and more.1.2. Velocity: Big data is generated at an unprecedented speed. Real-time data streams, such as social media updates, sensor data, and financial transactions, require immediate processing to extract valuable insights.1.3. Variety: Big data encompasses diverse data types, including structured, semi-structured, and unstructured data. It includes text, numerical data, images, audio, and video files.2. Applications of Big Data:2.1. Business Analytics: Big data enables organizations to gain valuable insights into customer behavior, preferences, and market trends. It helps in making data-driven decisions, improving customer satisfaction, and optimizing business processes.2.2. Healthcare: Big data analytics can be used to analyze patient records, medical imaging, and genomic data. It aids in personalized medicine, disease prediction, and improving healthcare outcomes.2.3. Finance: Big data analytics helps financial institutions in fraud detection, risk assessment, and customer segmentation. It enables real-time monitoring of transactions and enhances security measures.2.4. Transportation: Big data is used to optimize traffic management, predict maintenance needs, and improve public transportation systems. It helps in reducing congestion, enhancing safety, and improving efficiency.2.5. Manufacturing: Big data analytics assists in optimizing production processes, predicting equipment failures, and improving supply chain management. It enables proactive maintenance, reduces downtime, and enhances overall productivity.3. Challenges of Big Data:Despite its numerous advantages, big data also presents several challenges that need to be addressed:3.1. Data Quality: Big data often contains errors, inconsistencies, and missing values. Ensuring data quality is crucial for accurate analysis and decision-making.3.2. Privacy and Security: Handling large volumes of sensitive data raises concerns about privacy and security. Safeguarding data from unauthorized access and ensuring compliance with regulations is essential.3.3. Scalability: Big data requires scalable infrastructure and technologies to handle the ever-increasing volume and velocity of data. Scaling up existing systems can be complex and costly.3.4. Data Integration: Combining and integrating data from various sources with different formats and structures can be challenging. Data integration techniques and tools need to be employed for effective analysis.4. Future Prospects:The future of big data holds immense potential for advancements in various fields. Some of the key areas of development include:4.1. Artificial Intelligence (AI): Big data fuels the development of AI algorithms and machine learning models. AI, in turn, enhances big data analytics capabilities, enabling better predictions and insights.4.2. Internet of Things (IoT): The proliferation of IoT devices generates vast amounts of data. Big data analytics helps in extracting meaningful information from IoT-generated data for improved decision-making.4.3. Cloud Computing: The scalability and flexibility of cloud computing make it an ideal platform for big data processing and storage. The integration of big data and cloud computing enables cost-effective and efficient data analysis.4.4. Data Governance: As big data continues to grow, the need for effective data governance becomes crucial. Establishing policies, procedures, and frameworks for data management and privacy protection will be a priority.Conclusion:Big data has revolutionized the way organizations analyze and leverage data for decision-making. Its applications span across various sectors, including business, healthcare, finance, transportation, and manufacturing. However, challenges related to data quality, privacy, scalability, and integration need to be addressed. The future prospects of big data are promising, with advancements in AI, IoT, cloud computing, and data governance. Embracing big data and harnessing its potential will undoubtedly drive innovation and transform industries globally.。
英文文献综述万能模板范文
英文文献综述万能模板范文英文回答：Introduction.A literature review is a comprehensive survey of the existing research on a particular topic. It provides a critical analysis of the literature, identifying the key themes, gaps, and areas for future research. A well-written literature review can help readers quickly and easily understand the current state of knowledge on a topic.Steps to Writing a Literature Review.1. Define your topic. The first step is to define the scope of your literature review. This includes identifying the key concepts, variables, and research questions that you will be addressing.2. Search for relevant literature. Once you havedefined your topic, you need to search for relevant literature. This can be done through a variety of sources, including academic databases, Google Scholar, and library catalogs.3. Evaluate the literature. Once you have found a bodyof literature, you need to evaluate it to determine its relevance, quality, and credibility. This involves reading the abstracts and full text of the articles and assessing their strengths and weaknesses.4. Organize your review. Once you have evaluated the literature, you need to organize it into a logical structure. This may involve grouping the articles by theme, methodology, or research question.5. Write your review. The final step is to write your literature review. This should include a clear introduction, a body that discusses the key findings of the literature, and a conclusion that summarizes your findings andidentifies areas for future research.Tips for Writing a Literature Review.Be comprehensive. Include all of the relevant literature on your topic, even if it is not supportive of your hypothesis.Be critical. Evaluate the strengths and weaknesses of the literature, and identify any gaps in the research.Be clear and concise. Write in a clear and concise style, and avoid using jargon or technical language.Proofread carefully. Make sure to proofread your literature review carefully before submitting it.中文回答：文献综述的撰写步骤。
数据化转型文献综述范文模板
数据化转型文献综述范文模板Data transformation is the process of converting traditional analog data into digital form for analysis and decision-making. 数据化转型是将传统的模拟数据转化为数字形式，以便进行分析和决策的过程。
Intoday's digital age, businesses are increasingly relying on data-driven insights to gain a competitive edge. 在当今数字时代，企业越来越依赖数据驱动洞察来获得竞争优势。
As a result, there is a growing need for organizations to undergo digital transformation to stay relevant and innovative. 因此，组织需要不断进行数字化转型，以保持相关性和创新性。
This literature review aims to explore the various aspects of data transformation and its implications for businesses. 本文综述旨在探讨数据转型的各个方面以及对企业的影响。
One of the key benefits of data transformation is the ability to streamline operations and improve efficiency. 数据转换的主要优势之一是通过整合运营和提高效率。
By digitizing processes and workflows, organizations can automate repetitive tasks and reduce human error. 通过数字化流程和工作流程，组织能够自动化重复性任务并减少人为错误。
经典英文文献综述范文
经典英文文献综述范文English:A classic English literature review typically provides an overview and analysis of key works in a particular field or topic. It often begins with an introduction that outlines the scope and purpose of the review, followed by a discussion of the historical context and the evolutionof the topic. The literature review then delves into a detailed examination of the most influential and significant texts, identifying key themes, arguments, and methodologies employed by the authors. The review also seeks to identify gaps or unresolved issuesin the existing research, and may propose avenues for future exploration in the field. This type of literature review is valuable for scholars and researchers seeking to gain a comprehensive understanding of the state of knowledge in their area of study, and often serves as a foundation for new research projects and critical debates within the academic community.中文翻译:经典的英文文献综述通常提供了一个特定领域或主题关键作品的概述和分析。
英文版文献综述范文英文文献综述写
英文版文献综述范文英文文献综述写文献综述是对某一方面的专题搜集大量情报资料后经综合分析而写成的一种学术论文，它是科学文献的一种。
格式与写法文献综述的格式与一般研究性论文的格式有所不同。
这是因为研究性的论文注重研究的方法和结果，特别是阳性结果，而文献综述要求向读者介绍与主题有关的详细资料、动态、进展、展望以及对以上方面的评述。
因此文献综述的格式相对多样，但总的来说，一般都包含以下四部分：即前言、主题、总结和 ___。
撰写文献综述时可按这四部分拟写提纲，在根据提纲进行撰写工。
前言部分，主要是说明写作的目的，介绍有关的概念及定义以及综述的范围，扼要说明有关主题的现状或争论焦点，使读者对全文要叙述的问题有一个初步的轮廓。
主题部分，是综述的主体，其写法多样，没有固定的格式。
可按年代顺序综述，也可按不同的问题进行综述，还可按不同的观点进行比较综述，不管用那一种格式综述，都要将所搜集到的文献资料归纳、及分析比较，阐明有关主题的历史背景、现状和发展方向，以及对这些问题的评述，主题部分应特别注意代表性强、具有科学性和创造性的文献引用和评述。
总结部分，与研究性论文的小结有些类似，将全文主题进行扼要总结，对所综述的主题有研究的作者，最好能提出自己的见解。
___虽然放在文末，但却是文献综述的重要组成部分。
因为它不仅表示对被引用文献作者的尊重及引用文献的依据，而且为读者深入探讨有关问题提供了文献查找线索。
因此，应认真对待。
___的编排应条目清楚，查找方便，内容准确无误。
关于 ___的使用方法，录著项目及格式与研究论文相同，不再重复。
文学太宽泛了，你必须一点点的把你的论文论述的观点缩小，不然你写什么都不知道。
1. 去图书馆找所有与汤姆叔叔的小屋相关的所有英文资料，甚至是原文读本序言之类，也有你可以借鉴的句子。
2. 去电子阅览室，搜索所有与汤姆叔叔的小屋相关的论文评论，找到你觉得有用的拷下来。
3. 到网上搜索下相关词条，如wiki百科的英文相关词条4. 最好浏览一遍原文，看不懂的话中英文对照本也行。
英语文献综述范文
英语文献综述范文Literature Review on the Impact of Social Media on Mental Health。
Introduction。
Social media has become an integral part of our daily lives, with millions of people using platforms such as Facebook, Instagram, Twitter, and Snapchat to connect with friends and family, share updates about their lives, and consume news and entertainment. While social media has undoubtedly revolutionized the way we communicate and interact with one another, there is growing concern about its impact on mental health. This literature review aims to explore the existing research on the relationship between social media use and mental health, with a focus on the potential negative effects it may have on individuals.The Impact of Social Media on Mental Health。
Several studies have investigated the link between social media use and mental health, with a particular emphasis on the potential negative consequences. One study by Twenge and Campbell (2018) found that the use of social media, particularly among adolescents, was associated with higher levels of depression and anxiety. The researchers suggested that the constant comparison with others, cyberbullying, and the fear of missing out (FOMO) were some of the factors contributing to these negative outcomes.Similarly, another study by Primack et al. (2017) found that higher social media use was associated with increased feelings of social isolation and loneliness among young adults. The researchers suggested that excessive use of social media may lead to a decrease in face-to-face interactions and a lack of meaningful social connections, which in turn could have a detrimental impact on mental well-being.On the other hand, some studies have also highlighted the potential positive effects of social media on mental health. For example, a study by Berryman et al. (2018) found that social media use can provide a sense of social support and connection, particularlyfor individuals who may feel socially isolated in their offline lives. The researchers suggested that social media can be a valuable tool for maintaining and strengthening social relationships, which may have a protective effect on mental health.The Role of Social Media in Shaping Body Image and Self-Esteem。
大数据文献综述范文
大数据综述可以写哪些方面的问题大数据（big data），是指在可承受的时间范围内用常规软件工具进行捕捉、管理和处理的数据集合。
大数据的特点：1、容量（Volume）：数据的大小决定所考虑的数据的价值的和潜在的信息；2、种类（Variety）：数据类型的多样性；3、速度（Velocity）：指获得数据的速度；4、可变性（Variability）：妨碍了处理和有效地管理数据的过程。
5、真实性（Veracity）：数据的质量大数据的意义：现在的社会是一个高速发展的社会，科技发达，信息流通，人们之间的交流越来越密切，生活也越来越方便，大数据就是这个高科技时代的产物。
有人把数据比喻为蕴藏能量的煤矿。
煤炭按照性质有焦煤、无烟煤、肥煤、贫煤等分类，而露天煤矿、深山煤矿的挖掘成本又不一样。
与此类似，大数据并不在“大”，而在于“有用”。
价值含量、挖掘成本比数量更为重要。
对于很多行业而言，如何利用这些大规模数据是成为赢得竞争的关键。
大数据的缺陷：文献综述范文怎么写英语毕业论文文献综述好写的，根据题目写相关学者的总结，开始我也不会，还是师姐介绍的莫文网，专业的就是不一样，很快就帮忙完成了对大学英语翻译教学若干问题的思考应用英语翻译呼唤理论指导大学英语翻译教学：现状与对策国内商务英语翻译研究综述商务英语翻译中存在的问题与对策顺应理论视角下科技英语翻译切雅实证分析经济一体化环境下的商务英语翻译教学新世纪十年来商务英语翻译研究：回顾与前瞻语用观视角下的中医英语翻译教学实证研究翻译——找到源语的所指——对规划教材《商务英语翻译》误译译例的批判研究从功能对等角度看商务英语翻译高校科技英语翻译课程设置探讨科技英语汉译的英语翻译技巧研究——以船舶英语文本中的汉译为例功能对等视角下的科技英语翻译论商务英语翻译的4Es标准大学英语翻译教学存在的问题与对策关联理论在科技英语翻译中的应用——以Climate Change and Peak Oil文本的翻译为例功能对等理论指导下的商务英语翻译大学英语翻译教学：问题与对策英语翻译专业本科生的笔译能力调查分析——以某师范大学英语翻译专业为例中国职业篮球俱乐部体育英语翻译人员现状及发展对策研究从目的论的角度下看商务英语翻译论高职商务英语翻译教学中学生跨文化交际意识的培养试论近代国人英语翻译任务型教学法在《商务英语翻译》教学中的运用商务英语翻译与文化信息等值研究大学英语翻译教学教材编写探讨——以《新时代交互英语（读写译1-4册）》为例东西方文化差异对商务英语翻译的影响顺序分析在科技英语翻译中的应用——以翻译项目《大气污染排放系数手册》为案例从关联理论分析法律英语翻译中的文化差异及其翻译补偿商务英语翻译原则探讨跨文化因素对商务英语翻译的影响及调整策略###世界经济一体化、文化多元化的快速发展，英语作为世界通用语地位的确立，为我国大学英语教学提出培养具有跨文化交际能力的高素质人才的新要求。
文献综述(英文版)
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Literature Retrieval
✓Any research was based on a previous one.
✓Any high-quality research was based on the quantity and quality of literature one examinated.
Get needed resources. But how?
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How to get?
3 questions you should keep focusing on: What to get? Where to get? By what means?
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Four Criteria:
Accuracy
Completeness
High-quality
Efficiency
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What is literature review?
through critical reading revealing the research progress putting forth the questions
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Study Aims:
• Learn to retrieve required literature. • Learn to write a literature review.
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Tasks after Class:
Based on the literature you’ve retrieved, write a literature review about 3,000 words.
大数据文献综述范文docx(一)2024
大数据文献综述范文docx（一）引言概述：本文旨在综述大数据领域的相关文献，通过对现有研究成果的整理和分析，归纳出目前大数据领域的研究热点和发展趋势，为进一步的研究提供参考和借鉴。
正文：一、大数据的定义与特征1. 大数据的概念及演变2. 大数据的四个基本特征：3V（Volume、Velocity、Variety）+ Value3. 大数据与传统数据的差异与联系4. 大数据对经济、社会、科学等领域的影响二、大数据的采集与存储1. 大数据采集的主要方法：传感器网络、物联网等2. 大数据存储的常用技术：分布式文件系统、NoSQL数据库等3. 大数据采集和存储过程中面临的挑战及解决方案4. 大数据隐私与安全保护的技术与方法三、大数据的分析与挖掘1. 大数据分析的基本流程与方法：数据清洗、数据集成、数据挖掘、模型建立、结果验证等2. 大数据分析常用的算法和技术：关联规则挖掘、聚类分析、分类与预测等3. 大数据分析的应用领域与案例研究4. 大数据分析在决策支持中的作用与价值四、大数据的可视化与交互1. 大数据可视化的基本原理及方法2. 大数据可视化工具的比较与选择3. 大数据可视化的应用案例与效果评估4. 大数据可视化的交互技术与方法五、大数据的发展趋势与挑战1. 大数据发展趋势：云计算、边缘计算、人工智能等技术的融合与应用2. 大数据面临的挑战：数据质量、隐私与安全、算法效率等问题3. 大数据发展的政策与法律环境4. 大数据发展的前景与应用展望总结：通过对大数据领域相关文献的综述，可以发现大数据在经济、社会和科学领域的重要作用和潜在价值。
同时，大数据采集、存储、分析与可视化面临许多挑战和难题，需要我们进一步研究和探索。
随着技术的不断发展和应用的深入推广，大数据必将在各个领域中发挥更大的作用，为社会进步和经济发展提供有力支持。
大数据文献综述英文版
大数据文献综述英文版PrePared On 24 NOVember 2020The development and tendency Of Big DataTang Xia(GUiIin UniVerSity Of electronic technology, electronic engineeringandautomation,GUilin)Abstract: H Big Data H iS the most POPuIar IT WOrd after the H lnternet Of things πand H CIOUdcomputing*1. FrOm the source, development, StatUS quo and tendency Of big data, We Can UnderStand every aspect Of it. Big data is One Of the most important technologies around the WOrld and every COUntry has their OWn Way to develop the technolog y. Key words: big data; IT; technology1 The SOUrCe Of big dataDeSPite the famous futurist TOffler PrOPOSe the COnCePtiOn Of U Big Data” in 1980, for a IOng time, because the Primary Stage is Still in the development Of IT industry and USeS Of information sources, U Big Data n is not get enough attention by the PeOPle in that age 1*1. 2 The development Of big dataUntil the financial CriSiS in 2008 force the IBM ( multi-national COrPOratiOn Of ITindustry) PrOPOSing COnCePtiOn Of U Smart City n and VigOrOUSly PrOmOte Internet Of ThingS and ClOUd COmPUting SO that information data has been in a massive growth meanwhile the need for the technology is Very urgent. Under this condition, SOme AmeriCan data PrOCeSSing COmPanieS have focused On developing Iarge-SCale COnCUrrent PrOCeSSing system, then the “Big Data'9 technology become available SOOner and HadOOP mass data COnCUrrent PrOCeSSing SyStem has received Wide attention. SinCe 2010, IT giants have PrOPOSed their PrOdUCtS in big data area. Big COmPanieS SUCh as EMC S HP 、IBM X MiCrOSOft all PUrChaSe Other manufacturer relating to big data in Order toachieve technical integration 111. BaSed On this, We Can Iearn how ImPOrtant the big data Strategy is. DeVelOPment Of big data thanks to SOnIe big IT COmPanieS SUCh as GOOgle S AmaZOn S Chinamobile s AIibaba and SO on, because they need a OPtimiZatiOn Way to StOre and analysis data ・Besides, there are also ClemandS Of health SyStemS X geographic SPaCe remote SenSing and digitalmedia 121.3 The StatUS quo Of big dataNOWadayS AmeriCa is in the Iead Of big data technology and market application. USAfederal government announced a U Big Data ,s research and development^ Plan in MarCh,2012, WhiCh involved SiX federal government department the NatiOnal SCienCe FOUndation, Health ReSearCh Institute, DePartment Of Energy, DePartment Of Defense 5 AdVanCedReSearChPrOjeCtSAgenCy andGeOlOgiCal SUrVey in Order to improve the ability to extract information and VieWPOint Of big data 1*1. Thus, it Can SPeeCl SCienCe and engineering discovery up, and it is a major move to PUSh SOme research institutions making innovations.The federal government PUt big data development into a Strategy place, WhiCh has a bigimpact On every COUntry. At present, many big EUrOPean institutions is Still at the Primary Stage to USe big data and SeriOUSly IaCk technology about big data. MOSt improvements and technology Of big data are COme from AmenCa. Therefore, there are kind Of ChallengeS Of EUrOPe to keep in SteP With the development Of big data. But, in the financial SerViCe industry especially investmentbanking in LOndOn is One Of the earliest industries in EUrOPe. The experiment and technology Of big data is as good as the giant institution Of AmeriCa. And, the investment Of big data has been maintained PrOmiSing efforts. JanUary 2013, BritiSh government announced InilIiOn POUnd Will be invested in big data and CalCUlatiOn Of energy SaVing technology in earth ObSerVatiOn and health care131・JaPaneSe government timely takes the Chanenge Of big data strategy. JUly 2013, JaPan,s COmmUniCatiOnS IniniStry PrOPOSed a SyntheSiZe Strategy Caned U Energy ICT Of Japan" WhiCh focused On big data application. JUne 2013, the abe Cabinet formally announced the new IT Strategy——44The announcement Of Creating the most advanced IT country". ThiS announcement COmPrehenSiVely expounded that JaPaneSe new IT national Strategy is With the COre Of developing OPening PUbIiC data and big data in 2013 to 2020l41.Big data has also drawn attention Of China government.《GUidingOPiniOnS Of the State COunCil On PrOmOtingthe healthy and Orderly developmentof thelnternet Of things》PrOmOte to quicken the COre technology including SenSOr network、intelligent terminal s big data intelligent analysis and SerViCe integration. DeCember 2012, the national development PrOCeSSingXand reform COnInIiSSiOn add data analysis SOftWare into SPeCial guide, in the beginning Of 2013 ministry Of SCienCe and technology announced that big data research is One Of the most important COntent Of “973 program^1*1. ThiS PrOgram requests that We need to research the expression, measure and SemantiC UnderStanding Of multi-source heterogeneous data, research modeling theory and COmPUtatiOnal InOdeL PrOmOte hardware and SOftWare SyStem aιchitecture by energ y OPtimal distributed StOrage and processing, analysis the relationship Of COmPIeXityCaICUIability andXtreatment efficiency11,. AbOve all, We Can PrOVide theory evidence for Setting UP SCientifiC SyStem Of big data.4The tendency Of big dataSee the future by big (IataIn the beginning Of 2008, AIibaba found that the WhOle number Of SenerS Were On a SliPPery SIOPe by mining analyzing USer-behavior data meanwhile the PrOCUrement to EUrOPe and AmeriCa WaS also glide・ They accurately PrediCting the trend Of WOrId economic trade UnfOId half year earlier SO they avoid the financial CriSiS121・ DOCUment [3] Cite an example WhiCh turned OUt Can PrediCt a ChOlera One year earlier by mining and analysis the data Of storm, drought and Other natural disaster13Great ChangeS and business OPPOrtUnitieSWith the approval Of big data values, giants Of every industry all SPend more money in big data industry・ Then great ChangeS and business OPPOrtUnity COmeS I4 In hardware industry, big data are facing the ChallengeS Of manage, StOrage and real-time analysis. Big data Will have an important impact Of ChiP and StOrage industry, besides, SOme new industry Win be Created because Of big data141・In SOftWare and SerViCe area, the Urgent demand Of fast data PrOCeSSing Win bring great boom to data mining and business intelligence industry.The hidden ValUe Of big data Can Create a IOt Of new companies, new products, new technology and new PrOjeCtS12LDeVeIOPmeilt direction Of big (IataThe StOrage technology Of big data is relational database at Primary. BUt due to theefficient ability dealing With OnIine affair, Big data CanOniCal design, friendly query Ianguage5dominate the market a IOng term. However, its StriCt design pattern, it ensures COnSiStenCy to give UP function, its POOr expansibility these PrObIemS are exposed in big data analysis. Then, NOSQL data StOrage model and Bigtable PrOPSed by GOOgle Start to be in fashion^1 2 3 4 5.Big data analysis technology WhiCh USeS MaPRedUCe technological frame PrOPOSed by GOOgle is USed to deal With Iarge SCale COnCUrrent batch transaction・ USing file SyStem to StOre UnStrUCtUred data is not IOSt function but also Win the expansilility. LZaten there are big data analysis PlatfOrm Iike HAVEn PrOPOSed by HP and FUSiOn InSight PrOPOSed by HUaWei . BeyOnd doubt, this SitUatiOn Will be continued, new technology and measures Will COme OUt SUCh as next generation data WarehOuse, HadOOP distribute and SO on161・COnClUSiOnThiS PaPer We analysis the CleVelOPment and tendency Of big data. BaSed On this, We know that the big data is Still at a Primaly stage, there are too many PrOblemS need to deal with. But the COmmerCial ValUe and market VakIe Of big data are the direction Of development to information age.[6]Meng XiaOfeng, Wang HUijU・ DU XiaOyOng, Big Daya AnalySis: COnlPetitiOn and SUrViVal Of RDBMS and ManRedUce[JJOUrnal Of SOftWare, 2012,23(1): 32-451 Li Chunwei, DeVelOPment report Of China，S E-COmnlerCe enterprises, Beijing , 2013,2 Li Fen, ZhU Zhixiang. LiU Shenghui. The development StatUS and the PrOblemS Of Iarge data, JOUrnal Of Xi,an UniVerSity Of POStS and Telecommunications, 18 volume, pp. 102∙103,3 Kira Radinsky, EriC Horivtz, Mining the Web to PrediCt FUtUrC EVentS(CJ4 ChaPman A. Allen M D. BlaUStein B. It,s AbOUt the Data: PrOVenanCe as a TOll for ASSeSSing Data Fitness(C)5 Li RUiqilL Zheng Janguo, Big data ReSearch: StatUS quo. PrOblemS and IendenCy[J],Network APPliCatiOn.Shanghai, 1994,。
关于大数据的学术英文文献
关于大数据的学术英文文献Big Data: Challenges and Opportunities in the Digital Age.Introduction.In the contemporary digital era, the advent of big data has revolutionized various aspects of human society. Big data refers to vast and complex datasets generated at an unprecedented rate from diverse sources, including social media platforms, sensor networks, and scientific research. While big data holds immense potential for transformative insights, it also poses significant challenges and opportunities that require thoughtful consideration. This article aims to elucidate the key challenges and opportunities associated with big data, providing a comprehensive overview of its impact and future implications.Challenges of Big Data.1. Data Volume and Variety: Big data datasets are characterized by their enormous size and heterogeneity. Dealing with such immense volumes and diverse types of data requires specialized infrastructure, computational capabilities, and data management techniques.2. Data Velocity: The continuous influx of data from various sources necessitates real-time analysis and decision-making. The rapid pace at which data is generated poses challenges for data processing, storage, andefficient access.3. Data Veracity: The credibility and accuracy of big data can be a concern due to the potential for noise, biases, and inconsistencies in data sources. Ensuring data quality and reliability is crucial for meaningful analysis and decision-making.4. Data Privacy and Security: The vast amounts of data collected and processed raise concerns about privacy and security. Sensitive data must be protected fromunauthorized access, misuse, or breaches. Balancing data utility with privacy considerations is a key challenge.5. Skills Gap: The analysis and interpretation of big data require specialized skills and expertise in data science, statistics, and machine learning. There is a growing need for skilled professionals who can effectively harness big data for valuable insights.Opportunities of Big Data.1. Improved Decision-Making: Big data analytics enables organizations to make informed decisions based on comprehensive data-driven insights. Data analysis can reveal patterns, trends, and correlations that would be difficult to identify manually.2. Personalized Experiences: Big data allows companies to tailor products, services, and marketing strategies to individual customer needs. By understanding customer preferences and behaviors through data analysis, businesses can provide personalized experiences that enhancesatisfaction and loyalty.3. Scientific Discovery and Innovation: Big data enables advancements in various scientific fields,including medicine, genomics, and climate modeling. The vast datasets facilitate the identification of complex relationships, patterns, and anomalies that can lead to breakthroughs and new discoveries.4. Economic Growth and Productivity: Big data-driven insights can improve operational efficiency, optimize supply chains, and create new economic opportunities. By leveraging data to streamline processes, reduce costs, and identify growth areas, businesses can enhance their competitiveness and contribute to economic development.5. Societal Benefits: Big data has the potential to address societal challenges such as crime prevention, disease control, and disaster management. Data analysis can empower governments and organizations to make evidence-based decisions that benefit society.Conclusion.Big data presents both challenges and opportunities in the digital age. The challenges of data volume, velocity, veracity, privacy, and skills gap must be addressed to harness the full potential of big data. However, the opportunities for improved decision-making, personalized experiences, scientific discoveries, economic growth, and societal benefits are significant. By investing in infrastructure, developing expertise, and establishing robust data governance frameworks, organizations and individuals can effectively navigate the challenges and realize the transformative power of big data. As thedigital landscape continues to evolve, big data will undoubtedly play an increasingly important role in shaping the future of human society and technological advancement.。
文献综述(英文版)
What to get?
Where to get?
By what means?
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Four Criteria:
Accuracy
Completeness
High-quality
Efficiency
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What is literature review?
through critical reading
revealing the research progress
putting forth the questions
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Literature Review Procreessosurces-selecting
文献综述(英文版)
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Five Steps for Research
➢ Choose & Define a Topic
➢ Literature Retrieval &
Review
➢ Proposal Design
notestaking reviewwriting reference-listing
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How to write a literature review?
prelude
literature-analyzing
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The development and tendency of Big DataTang Xia(Guilin University of electronic technology, electronic engineering?and?automation, Guilin)Abstract: Big Data?is the most popular IT word after the Internet of things?and Cloud computing. From the source, development, status quo and tendency of big data, we can understand every aspect of it. Big data is one of the most important technologies around the world and every country has their own way to develop the technology.Key words: big data; IT; technology1 The source of big dataDespite the famous futurist Toffler propose the conception of “Big Data” in 1980, for a long time, because the primary stage is still in the development of IT industry and uses of information sources, “Big1][.” is not get enough attention by the people in that age Data2 The development of big dataUntil the financial crisis in 2008 force the IBM ( multi-national corporation of IT industry) proposing conception of “Smart City” and vigorously promote Internet of Things and Cloud computing so that information data has been in a massive growth meanwhile the need for the technology is very urgent. Under this condition, some American data processing companies have focused on developing large-scale concurrent processing system, then the “Big Data” technology become available sooner and Hadoop mass data concurrent processing system has received wide attention. Since 2010, IT giants have proposed their products in big data area. Big companies such as EMC、HP、IBM、Microsoft all purchase other manufacturer relating to big data in order to achieve technical[1]. Based on this, we can learn how important the big data integrationstrategy is. Development of big data thanks to some big IT companies suchas Google、Amazon、China mobile、Alibaba and so on, because they need a optimization way to store and analysis data. Besides, there are also demands of health systems、geographic space remote sensing and digital3 The status quo of big data[2].mediaNowadays America is in the lead of big data technology and market application. USA federal government announced a “Big Data's research and development” plan in March,2012, which involved six federal government department the National Science Foundation, Health ResearchInstitute, Department of Energy, Department of Defense,Advanced?Research?Projects?Agency and Geological Survey in order to [1]. improve the ability to extract information and viewpoint of big dataThus, it can speed science and engineering discovery up, and it is a majormove to push some research institutions making innovations.The federal government put big data development into a strategy place, which has a big impact on every country. At present, many big European institutions is still at the primary stage to use big data and seriously lack technology about big data. Most improvements and technology of big data are come from America. Therefore, there are kind of challenges of Europe to keep in step with the development of big data. But, in the financial service industry especially investment banking in London is one of the earliest industries in Europe. The experiment and technology of big data is as good as the giant institution of America. And, the investment of big data has been maintained promising efforts. January 2013,British government announced million pound will be invested in big data and calculation of energy saving technology in earth observation and [3]. health care Japanese government timely takes the challenge of big data strategy.July 2013, Japan's communications ministry proposed a synthesize strategy called “Energy ICT of Japan” which focused on big data application. June 2013, the abe cabinet formally announced the new IT strategy----“The announcement of creating the most advanced IT country”. This announcement comprehensively expounded that Japanese new IT national strategy is with the core of developing opening public data and big data [4].in 2013 to 2020 Big data has also drawn attention of China government.《Guiding?opinions of the State Council on promoting?the healthy and orderly development?of the?Internet of things》 promote to quicken the core technology including sensor network、intelligent terminal、big data processing、intelligent analysis and service integration. December 2012, the national development and reform commission add data analysis software into special guide, in the beginning of 2013 ministry of science and technology announced that big data research is one of the most important [1]. This program requests that we need to 973 program”content of “research the expression, measure and semantic understanding ofmulti-source heterogeneous data, research modeling theory and computational model, promote hardware and software system architecture by energy optimal distributed storage and processing, analysis the [1]. calculability and treatment efficiencyrelationship of complexity、Above all, we can provide theory evidence for setting up scientific system of big data.4 The tendency of big dataSee the future by big dataIn the beginning of 2008, Alibaba found that the whole number of sellers were on a slippery slope by mining analyzing user-behavior data meanwhile the procurement to Europe and America was also glide. They accurately predicting the trend of world economic trade unfold half year2][. Document [3] cite they so avoid the financial crisisan example earlier which turned out can predict a cholera one year earlier by mining and 3[ Great analysis the data of storm, drought and other naturaldisaster changes and business opportunitiesWith the approval of big data values, giants of every industry all spend more money in big data industry. Then great changes and business 4[ In hardware industry, big data are facing the opportunity comeschallenges of manage, storage and real-time analysis. Big data will havean important impact of chip and storage industry, besides, some new[4].industry will be created because of big data In software and service area, the urgent demand of fast data processingwill bring great boom to data mining and business intelligence industry. The hidden value of big data can create a lot of new companies, new [2].products, new technology and new projects Development direction of big dataThe storage technology of big data is relational database at primary. But due to the canonical design, friendly query language, efficient ability dealing with online affair, Big data dominate the market a long term. However, its strict design pattern, it ensures consistency to give up function, its poor expansibility these problems are exposed in big data analysis. Then, NoSQL data storage model and Bigtable propsed by Google 5][.start to be in fashionBig data analysis technology which uses MapReduce technological frame proposed by Google is used to deal with large scale concurrent batch transaction. Using file system to store unstructured data is not lost function but also win the expansilility. Later, there are big data analysis platform like HAVEn proposed by HP and Fusion Insight proposed by Huawei . Beyond doubt, this situation will be continued, new technology and measures will come out such as next generation data warehouse, Hadoop 6][.distribute and so onConclusionThis paper we analysis the development and tendency of big data. Based on this, we know that the big data is still at a primary stage, there are too many problems need to deal with. But the commercial value and market value of big data are the direction of development to information age.[1] Li Chunwei, Development report of China's E-Commerce enterprises, Beijing , 2013,statusdevelopment The Shenghui, Liu Zhixiang, Zhu Fen, Li ] 2[and the problems of large data, Journal of Xi'an University of Posts and Telecommunications, 18 volume, pp. 102-103, [3] Kira Radinsky, Eric Horivtz, Mining the Web to Predict Future Events[C][4] Chapman A, Allen M D, Blaustein B. It's About the Data: Provenance as a Toll for Assessing Data Fitness[C][5] Li Ruiqin, Zheng Janguo, Big data Research: Status quo, Problems and Tendency[J],Network Application,Shanghai,1994, [6] Meng Xiaofeng, Wang Huiju, Du Xiaoyong, Big Daya Analysis: Competition and Survival of RDBMS and ManReduce[J], Journal of software, 2012,23(1): 32-45．。