Database Modeling of Engineering Information
REFERENCE DATA MODELS FOR SUPPORTING THE NETWORK FOR EARTHQUAKE ENGINEERING SIMULATION (NEE
REFERENCE DATA MODELS FOR SUPPORTING THE NETWORK FOR EARTHQUAKEENGINEERING SIMULATION (NEES)Jun Peng1, and Kincho H. Law2ABSTRACTNEES (the George E. Brown, Jr. Network for Earthquake Engineering Simulation) is a national, networked resource for next-generation experimental earthquake engineering research and education in the United States. The goal of NEES is to support open access to and use of NEES’s facilities and data by the earthquake engineering community. The system infrastructure to support the NEES activities is the NEESgrid which is intended as a distributed virtual “collaboratory” cyber-infrastructure for earthquake experimentation and simulation. This paper describes an effort in developing reference data models for NEESgrid shake table experiments and large scale structural testing. Data models provide the “semantic” information describing the project and experimental data. Brief description of the data models and some of their features are presented in this paper. The proposed reference data models are shown to be flexible and extendible. Validation and usability of the reference data models have demonstrated that the data models are sufficiently comprehensive to save and organize the experimental data from typical earthquake engineering simulations.KEY WORDSNEESgrid, data model, cyberinfrastructure, data and metadata, earthquake simulation. INTRODUCTIONIn order to facilitate collaboration within the NEES framework, one of the key services that NEESgrid needs to support is with respect to the data and metadata for earthquake engineering simulations. It is well known that engineering design and manufacturing activities generally involve a large set of independent but interrelated data items (Law and Jouaneh 1986; Law et al. 1987). Traditional hierarchical, network, and relational database models, which are designed for highly structured commercial applications, do not adequately support technical engineering problem domains. Influenced by the field of artificial intelligence, semantic relationships such as classification, association, aggregation and generalization can be used for organizing and structuring engineering data (Law 1988). Besides the mechanism needed to represent and manipulate data, data model development also requires some knowledge on the intended use of the data (Law et al. 1991). For this 1Research Associate, Department of Civil and Envir. Engrg., Stanford University, Stanford, CA 94305, Phone +1 650/725-1886, FAX 650/723-4806, junpeng@2Professor, Department of Civil and Envir. Engrg., Stanford University, Stanford, CA 94305, Phone +1 650/725-3154, FAX 650/723-7514, law@purpose sample data sets are being developed that demonstrate the features of the data models along with scenarios for the use of the data and models. Specifications for the tools necessary to support entering, importing, storing, searching, and extracting data from the repository are being proposed and developed. This paper briefly describes the current effort in developing a reference NEESgrid data model, focusing on shake table experiments and large-scale structural testing.A data model is in essence a representation of the data and their interrelationship and provides a conceptual or implementation view of the data. A data model can be viewed as the “grammar”, “vocabulary” and “content” that represent the types of “information” stored in a “system”. The grammar defines the relationships among the data elements in the system; the vocabulary defines the terminology used to describe these elements; the content defines what is to be included in the system. Within the scope of the NEESgrid data/metadata effort, data is defined as all project related information and encompasses observational (or acquired) data recorded prior to the experiments and during the experiments by means of sensors, cameras and the like; computational (or generated) data for and as a result of simulations, post-processing, etc.; literature in the form of reports, journal papers, drawings, etc. Associated descriptive and related data, i.e. metadata (or data about the data), are defined and published in a prescribed (by NEESgrid) format and language.DATA MODELING TOOL AND APPROACHThere are many existing data modeling techniques and tools that are available to help design a data model and to structure the data. A brief review of relevant techniques and approaches has been reported earlier (Peng and Law 2004a). This section briefly discusses the tool selected for developing the NEESgrid data model and the basic concepts of object-oriented data modeling.P ROTÉGÉ – A N O NTOLOGY D EVELOPMENT T OOLTo facilitate the design of the NEESgrid data model, we selected Protégé (), which is an open-source software package designed to help developing ontology for knowledge-based systems (Gennari et al. 2002). Ontology represents explicit formal specifications of the terms in the domain and the relations among them. As open source software, Protégé has attracted a wide variety of plug-ins from around the world to enhance its capabilities. Some of these software plug-ins allow a model developed in Protégé to be exported in many standard formats, including UML (Unified Modeling Language (Arlow and Neustadt 2001)), XML Schema (/XML/ Schema), RDF (Resource Description Framework, /RDF/), OWL (Web Ontology Language, /2001/sw/ WebOnt/), and others.In Protégé, a graphical user interface (GUI) is provided to facilitate ontology development. The interface enables the modeling of an ontology of classes to describe a particular subject with a set of concepts and their relationships. The interface also allows direct entering of specific instances of data and the creation of a knowledge base. Figure 1 shows an example of the GUI, with the view of classes shown in the left window, and the view of detailed attributes of a class (e.g. Project class) shown in the lower right window.Figure 1: Protégé Interface with OWL Plug-inO BJECT-O RIENTED D ATA M ODELLINGIn an object-oriented data model, information is modeled as objects, which can be any sorts of entities (Rumbaugh et al. 1990). The general representation of certain type of objects is called a class, which represents explicit description of concepts in a domain. The creation of an object of a certain class is called instantiation. The relationship between an object and a class can be viewed analogically in a procedural language in that a variable being a particular instance of a pre-defined type such as an integer. For example, Project is modeled as a class, and a Mini-MOST experiment (Nakata et al. 2004) is an object instance of the Project class.An object encapsulates certain related data as slots, which are also called attributes or properties. Slots can have different facets describing the value type, allowed values, the number of values (cardinality), and other features of the values the slot can take. A value-type facet describes what types of values can fill in the slot. Some common value types are string, number, Boolean, enumerator, and object instance. Allowed objects and the variety of values (e.g. minimum number, maximum number) of a slot are often referred to as a range of a slot. Slot cardinality defines how many values a slot can have, such as single (at most one value) or multiple (more than one value). For example, “minute” is a slot of class Time, with data type defined as integer, cardinality one and range from 0 to 59.One important feature of object-oriented modeling is the concept of class hierarchies, with slots of a superclass being inherited by its subclasses. This inheritance feature allows us to define the common slots used by several classes at the highest possible level in the hierarchy, which avoids the duplication of slots at the lower levels. A superclass can have subclasses that represent concepts that are more specific than the superclass.A REFERENCE DATA MODEL FOR NEESGRIDAs depicted in Figure 2, the NEESgrid data/metadata effort is working towards producing end-to-end solutions that integrate site specifications database, project level model, domain specific data models, and common elements. To capture all these data, the reference data model is designed to include six base classes, namely SiteInformation, Activity, Apparatus, ApparatusSetup, DataElement, and ComplexDataType. The high-level class diagram of the reference data model is presented in Figure 3, which shows the association relationship among classes. (The ComplexDataType class, which is employed to support other base classes, is not shown in the figure.) The association relationship exists between classes when an object of one class knows/contains an object of another class. For example, a Project object knows about its Tasks objects, a Project also contains Organizations, Sites, and RolePersons. RolePerson is in turn defined as the combination of a Person and his/her role in a Project. The arrow in Figure 3 denotes the direction of the relationship contains; i.e., AÆB indicates that class A knows/contains class B.O VERVIEW OF T HE R EFERENCE D ATA M ODELThe following gives a brief description about the six groups of base classes defined in the NEESgrid reference data model.• SiteInformation: SiteInformation is modeled to represent a typical experiment site and its associated personnel, facilities, equipments and other information. In the reference data model, Site is modeled as the aggregation of other component classes, such as RolePerson, Organization, PrimaryEquipment, and SecondaryEquipment.This group of classes is intended to be associated with the site specifications database (Kutter et al. 2004) that is currently under development by the NEES community.• Activity: Activity is designed to support project level modeling. Common properties of Activity classes include objective, description, start time and end time. The duration of an Activity can be calculated by using start time together with end time.The start time of an Activity can be used to identify which Activity happens first, i.e.to sort out the sequence of several Activities. There are four basic types of Activities, including Project, Task, EventGroup and Event. A Project is a collection (organized group) of tasks designed to achieve specific goals and objectives of a project. A Task contains one or more EventGroups, and each Task typically serves a specific role in a Project. An EventGroup is defined as a collection of related Events. An Event, which is the atomic level of Activity, refers to each single run of an experiment or a simulation.• Apparatus: Apparatus is defined as any equipment (such as shake table or sensors), specimen, or computational resource that may be used in an Activity. Explicit modeling of Specimen is not considered in the current version of the reference model (Peng and Law 2004a). Only the most basic modeling is provided (as a collection of descriptive files, drawings, and/or photos). This design reflects current approach used to describe specimen in earthquake engineering experiments. However, if so desired, the Specimen class can be extended to support other, more detailed, models.• ApparatusSetup: ApparatusSetup models the arrangement and setup of apparatus for all experiments. Universal modeling of ApparatusSetup is very difficult. Not only are there different types of experiments (such as shake table, pseudo-dynamic tests, centrifuge, and tsunami) and different materials (such as concrete, steel, wood, etc.), but also the geometry of specimen and the arrangement of sensors may be too complicated and cumbersome to model. For example, the “as-built” locations of sensors may be different from the “design” locations, and the exact physical locations (i.e., the coordinates x, y, z values) of sensors may be very hard to be recorded. Therefore, current design for the ApparatusSetup model focuses on tools and methodologies that can capture and organize CAD drawings, sketched drawings and notes, photos, narrative descriptions, and electronic notes, etc.• DataElement: DataElement represents all types of data that may be generated or processed during an Activity. The DataElement normally serves as Input/Output to an Activity. Types of DataElement include text document, publication, earthquake record, photo, CAD drawing, movie, etc. In NEESgrid data/metadata effort, it is assumed that the data is saved in or translated into computer-readable format.Therefore, a DataElement object is represented in the format (such as a file) that can be saved in computer memory, on disks, or in some kind of data storage repository. • ComplexDataType: ComplexDataType is defined in the reference data model to represent any data type that is not a simple data type such as integer, float, Boolean, or character string. Some of the example subclasses of ComplexDataType include Folder, RolePerson, Unit, DateTime, Location, Measurement, Angle, etc.SiteSpecificationsDatabaseProjectDescriptionDomainSpecificmodelsCommonElementsData /ObservationsFigure 2: Overall Data Model for NEESgrid (Courtesy of Chuck Severance)Figure 3: High-level Class Diagram of the Reference Data ModelF EATURES OF T HE R EFERENCE D ATA M ODELThe following briefly describes two important but unique features of the developed reference NEESgrid data model. Details of these features have been discussed elsewhere (Peng and Law 2004b). The reference data model explicitly models certain Activities that are carried out at multiple Sites. Figure 4 shows an example project that has a single site Task (e.g. Task1) and a MultiSiteTask (e.g. M_Task1). The MultiSiteTask M_Task1 has Tasks that are undertaken at both Site1 and Site2. The MultiSiteEvent M_E1 has an Event E2 at Site1 and an Event E4 at Site2, and the MultiSiteEvent M_E2 has an Event E3 at Site1 and an Event E5 at Site2. As shown in Figure 4, although Project does not directly contain Task2 that takes place at Site2, Task2 can still be accessed from the Project since M_Task1 contains Task2. This design enables the support of the types of experiments that are carried out either simultaneously or independently at several Sites.The reference data model includes a sensor model that is designed specifically to support earthquake engineering experiments. The data acquisition equipment is modeled as a collection Sensor, DAQCable, DAQChannel, and DAQSystem. Figure 5 shows the relationships and the slots of these classes. Typically a data acquisition system involves at least three main components: (1) the sensors which respond to a physical stimulus and generate analog voltage signals; (2) a DAQchannel which receives the signal and uses predefined filter, gain, offset, excitation, sensitivity information for Analog-to-Digital and Engineering Unit conversions; and (3) a PC unit which uses some communications link to retrieve the data.EG:EventGroupM_EG:MultiSiteEventGroupE:EventM_E:MultiSiteEvent M_Task:MultiSiteTaskFigure 4: Layout of an Example ProjectFigure 5: Setup and Modeling of DAQ DevicesVALIDATION AND USABILITY TESTThe usability of the reference data model has been tested with legacy experimental data (Peng and Law 2004c). Legacy data from several earthquake experiments have been used. For illustration purpose, this paper focuses on the data sets obtained from a Mini-MOST experiment (Nakata et al. 2004).M INI -MOST E XPERIMENTThe main purpose of the Mini-MOST experiment is to show the capability of the various NEESgrid service components using a small-scale physical experimental setup (Nakata et al. 2004). The Mini-MOST experimental hardware, as implied by its name, is small in size and can be easily packed and shipped to experimental sites. The Mini-MOST experimentprovides a platform for researchers to become familiar with the NEESgrid software and to gain first-hand experience in using the NEESgrid services. For the validation test of the reference data model, the data sets generated from a particular Mini-MOST test on February 28, 2004 at University of Illinois at Urbana-Champaign (UIUC) are employed.I NPUTTING E XPERIMENTAL D ATAFor validation purpose, data generated from the Mini-MOST experiment was ingested using Protégé (Gennari et al. 2002) and saved as files in a local file system. As we mentioned earlier, besides defined classes and their properties, Protégé can also be used to handle instances of data. Data related to Mini-MOST experiment can be entered using the slots (properties) as defined in the reference data model. If a slot is defined as primitive type, such as Integer, Real Number, Time, or String, etc., we can simply type in the value. If a slot is defined as Objects, then we can either choose a previously created object or create a new one. If a slot is defined as of type “URI” (Universal Resource Identification, which would normally refers to a file), we can save the file in a particular location and then enter the URI for the file location. The metadata, i.e. information about the data, are saved as an OWL (/2001/sw/WebOnt/) file. Other experimental data, such as specimen photos and sensor readings, can be stored in a file on a web server with its URI saved in the OWL file.B ROWSING E XPERIMENTAL D ATAFor validation purpose, we implemented a project viewer to retrieve the saved data and to view the data on a web browser according to the data model. The server program is implemented using Java Servlet technology (/products/servlet/); and the parsing of the OWL file is handled by using Jena (McBride et al. 2004).Since the data is organized according to the project it belongs to, the project viewer is started with a webpage that contains a list of projects. When we click on a particular project, say miniMOST-1, the details of the project will be shown on the browser. The details include the values for all the slots defined for Project class. If a slot is defined to have data with primitive data type, the value is shown directly in the webpage. If a slot is defined to be a reference to other object, then a link is shown in the webpage. We can then follow these links to browse the details of contained objects. For example, we can navigate and access all the Tasks that belong to the Project by following specific links. We can further follow links to access EventGroups, as each Task in a project may contain one or more EventGroups. Since an EventGroup is defined as a collection of Events, each Event can be accessed from the EventGroup object. An Event, which is an entity at the atomic (or lowest) level of an Activity, refers to a single run of an experiment or a simulation. Experimental results, such as SensorReading, can be accessed from an Event object.The details of other objects, such as InfrastructureSetup, can also be retrieved by navigating the project viewer. An InfrastructureSetup object essentially is a collection of texts, documents (in the format of Word, PDF, Excel, etc.), figures and drawings stored as files. Files are saved in a web server and their URIs are saved as metadata. The files can be dynamically downloaded and shown on a web browser, as illustrated in Figure 6.(a) MiniMost_Wiring.pdf(b) MiniMOST_Overall.jpgFigure 6: Access of Files Representing the InfrastructureSetupSUMMARY AND DISCUSSIONSThis paper has discussed the approach for the development of a reference NEESgrid data model. Brief description of the developed data model and some of its features are also presented. Six base classes are presented and the relationships among these classes are defined. These classes represent the essential elements to support the end-to-end solution of NEESgrid data efforts. We believe the proposed reference data model is sufficiently flexible and extendible: (1) new classes can easily be introduced; (2) the slots of a particular class can be added, deleted, or modified; (3) and the relationships among the classes can be altered. Other models, such as specimen model, unit model, geometry/location model, and Site model, can be appended to (or even replace certain parts of) the reference data model. Although the NEESgrid reference data model is intended to focus on the data requirements for shake table experiments, large portion of the model is of sufficient generality to be used for other types of experiments, such as centrifuge, pseudo-dynamic tests, field tests, etc.To validate the reference data model, we have populated the model with the Mini-MOST experimental data. This validation process helps evaluate the completeness, flexibility and usability of the data model. The usability test has demonstrated that the data model is sufficiently comprehensive to save and organize all the Mini-MOST data. In addition, as the experimental data are organized according to the data model, browsing and accessing them are fairly intuitive and straightforward. Efforts will continue to validate, evaluate and refine the reference data model using other experimental projects and data.ACKNOWLEDGEMENTSThis work was supported by the George E. Brown, Jr. Network for Earthquake Engineering Simulation (NEES) Program of the National Science Foundation, Award CMS-0117853. Any opinions, findings, and conclusions or recommendations expressed in this material are,however, those of the authors and do not necessarily reflect the views of others and the National Science Foundation.We gratefully acknowledge the contributions of the NEESgrid’s Data/Metadata task committee members: Bill Spencer, Chuck Severance, Jim Eng, Jean-Pierre Bardet, Jennifer Swift, Andrei Reinhorn, Gokhan Pekcan, Hank Ratzesberger, Ken Ferschweiler, Lelli Van Den Einde. We also like to thank Steve Mahin, Bozidar Stojadinovic, Greg Fenves, Frank McKenna, Patrick Laplace, Jerome Hajjar and Catherine French for their time and supports. REFERENCESArlow, J. and Neustadt, I. (2001). UML and the Unified Process: Practical Object-Oriented Analysis and Design, Addison-Wesley Pub Co., Boston, MA.Gennari, J., Musen, M. A., Fergerson, R. W., Grosso, W. E., Crubézy, M., Eriksson, H., Noy, N. F. and Tu, S. W. (2002). The Evolution of Protégé: An Environment for Knowledge-Based Systems Development. Technical Report SMI-2002-0943, Stanford Medical Informatics Group, Stanford University.Kutter, B. L., Wilson, D. W., Pancake, C. and Haerer, S. (2004). Introduction to the Site Specifications Database (SSDB). Network for Earthquake Engineering Simulation, (available at /IT/site.specs.db/).Law, K. H. (1988). "Conceptual Database Design for Engineering Modeling." The ASME International Computers in Engineering Conference and Exhibition, Managing Engineering Data: Emerging Issues, San Francisco, CA.Law, K. H. and Jouaneh, M. K. (1986). "Data Modeling for Building Design." The 4th Computing Conference in Civil Engineering, Boston, MA.Law, K. H., Jouaneh, M. K. and Spooner, D. L. (1987). "Abstraction Database Concept for Engineering Modeling." Engineering with Computers, 2 79-94.Law, K. H., Wiederhold, G., Siambela, N., Sujansky, W., Zingmond, D., Singh, H. and Barsalou, T. (1991). "Architecture for Managing Design Objects in a Sharable Relational Framework." International Journal of Systems Automation: Research and Applications (SARA), 1 47-65.McBride, B., Boothby, D. and Dollin, C. (2004). An Introduction to RDF and the Jena RDF API. (available at /tutorial/RDF_API/index.html).Nakata, N., Yang, G. and Spencer, B. F. (2004). System Requirements for Mini-MOST Experiment. NEESgrid Technical Report, (available at /mini-most/Mini_MOST_requirements_revised3.pdf).Peng, J. and Law, K. H. (2004a). A Brief Review of Data Models for NEESgrid. Technical Report No.TR-2004-01, The NEES Cyberinfrastructure Center, (available at /documentation/pdf/TR_2004_01.pdf).Peng, J. and Law, K. H. (2004b). Reference NEESgrid Data Model. Technical Report No.TR-2--4-40, (available at /documentation/pdf/TR-2004-40.pdf).Peng, J. and Law, K. H. (2004c). Validity and Usability of the NEESgrid Reference Data Model. Technical Report No.TR-2004-44, The NEES Cyberinfrastructure Center, (available at /documentation/pdf/TR_2004_44.pdf).Rumbaugh, J. R., Blaha, M. R., Lorensen, W., Eddy, F. and Premerlani, W. (1990). Object-Oriented Modeling and Design, Prentice Hall.。
信息资源管理_ch03Database Processing
Metadata
• Database:
A database is a self-describing collection of integrated records.
• Metadata
Data that describe data
3-15
Study Questions
Q1 What is the purpose of a database?
3-6
Study Questions
Q1 What is the purpose of a database?
Q2 What is a database?
Q3 Q4 What are the components of a database application system? How do database applications make databases more useful?
Q7
What is the users’ role in the development of databases?
3-16
Components of a Database Application System
• Applications make database data more accessible and useful.See page 5. • Users employ a database application that consists of forms, formatted reports, queries, and application programs. • Database management system (DBMS) processes database tables for applications.
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有机合成几个常用网址有机合成：/（CAS网址： §CAS网上检索：/websearch.html ）§欧洲专利局、美国国会图书馆、国内的维普和万方Journal of Medicinal ChemistryJournal of Chemical Information and ModelingBioorganic & Medicinal ChemistryBioorganic & Medicinal Chemistry LettersDrug Discovery TodayDrug Discovery Today: TARGETSJournal of Computer-Aided Molecular Design另外Nature Review Drug Discoverychembio.ethz.ch/links/en/orgchem_reaktionen_lehr.html/organicreactions.htmhttp://210.34.14.15/teach/yjhx/onr/contents.html/ (imduping 站友提供)/~hxx/jpkc/yjhx/dizi.htm (MeYoung 站友提供)laotree wrote:/Reaction%20reference/reaction_index.htmCheck here, you might find some answer of you questions.偶然发现的一个有用连接/chemlinks.html如: (里面介绍了几乎所有常用的NMR spectra 能给出的信息)/litmtg/2003/garg-lit-1_9_03.pdfCharacteristics of E2 Mechanism:/chem/255/CHM%20255%20Fall%2005/255nt0518.htm索引预留页以下连接可供大家参与讨论./bbs/post/view?bid=77&id=4405732&tpg=1&ppg=1&sty=1&age=30#4405732 /bbs/post/view?bid=77&id=3884446&tpg=1&ppg=1&sty=1&age=30#3884446/bbs/post/view?bid=77&id=4040484&tpg=1&ppg=1&sty=1&age=30#4040484 /bbs/actions/archive/post/2756386_1.html/bbs/actions/archive/post/2905166_1.html/bbs/actions/archive/post/1777499_1.html/bbs/actions/archive/post/2607216_0.html/bbs/actions/archive/post/2721866_0.html/bbs/post/view?bid=77&id=4476601&sty=3&keywords=%BB%FA%C0%ED/bbs/post/view?bid=77&id=3260544&sty=3&keywords=%BB%FA%C0%ED/bbs/actions/archive/post/2739439_0.html/bbs/actions/archive/post/1825596_0.html/bbs/actions/archive/post/2009200_0.html/bbs/post/view?bid=77&id=3970449&sty=3&keywords=%BB%FA%C0%ED/bbs/post/view?bid=77&id=2733748&sty=3&keywords=%BB%FA%C0%ED/bbs/post/view?bid=77&id=4504481&sty=3&keywords=%BB%FA%C0%ED/bbs/post/view?bid=77&id=2733748&sty=3&keywords=%BB%FA%C0%ED/bbs/post/view?bid=77&id=4504481&sty=3&keywords=%BB%FA%C0%ED/bbs/actions/archive/post/2592048_1.html/bbs/post/view?bid=77&id=3623146&sty=3&keywords=%BB%FA%C0%ED/bbs/post/view?bid=77&id=4455972&tpg=1&ppg=1&sty=1&age=30#4455972 /bbs/post/view?bid=77&id=3334663&sty=3&keywords=%BB%FA%C0%ED/bbs/post/view?bid=77&id=3483039&tpg=1&ppg=1&sty=1&age=30#3483039 /bbs/actions/archive/post/783770_0.html/bbs/actions/archive/post/783770_1.html/bbs/post/view?bid=77&id=3652957&sty=3&keywords=%BB%FA%C0%ED/bbs/post/view?bid=77&id=4462825&sty=3&keywords=%BB%FA%C0%ED/bbs/actions/archive/post/1841030_0.html/bbs/actions/archive/post/2132405_1.html/bbs/actions/archive/post/2132405_1.html/bbs/post/view?bid=77&id=4357649&tpg=1&ppg=1&sty=1&age=30#4357649 /bbs/post/view?bid=77&id=4113980&tpg=1&ppg=1&sty=1&age=30#4113980可以搜索关键词进行期刊查询，并有人名反应，保护基等资源。
信息类人才三位一体培养模式的研究与实践
信息类人才三位一体培养模式的研究与实践刘洞波（湖南工程学院计算机与通信学院湖南湘潭411104）摘要文章针对信息类本科毕业生就业率和平均就业酬金呈逐年下降的问题，结合IT业发展对人才的需求，提出一种“实践—创新—就业”三位一体的培养模式，加强对学生创新能力和实践动手能力的培养，提高信息类毕业生的就业能力，并取得了良好的实践效果。
关键词三位一体就业能力创新实践教学体系中图分类号：G642文献标识码：AResear ch and Practice of the Tr aining Mode of theTr inity of Infor mation Technological TalentLIU Dongbo(College of Computer and Communication,Hu'nan Institute of Engineering,Xiangtan,Hu'nan411101) Abs tra c t This paper according to the employment rate of graduates and the average employment remuneration of the In-formation Technological T alent to decrease year by year,combined with the needs of the IT industry,put forward a kind of"practice-innovation-employment"the trinity of information technological talent training mode,strengthen the cultivation of students'innovation ability and practice ability,improve information graduates employment ability,and have achieved good practice effect.K e y words the trinity;employment ability;innovation;practice teaching system近年来，在高等教育快速发展的形势下，信息类人才培养过程中普遍存在学校培养与生产实践脱节、理论教学与社会需求脱节，高校的实践教学环节受到很大的影响。
SDG建模及其应用的进展
由相容支路组成的路径称为相容通路. 事故只 有通过相容通路才能进行传播. 在研究故障诊断的 问题中 ,只有有效节点和相容通路才描述了故障的 传递关系 ,所以研究的范围就是 SDG 图的一个子图 ———原因- 后果图 ( CEG) .
障诊断 , 即寻找故障源. 在这方面 , Iri M , Umeda T , Shiozaki J , Yu C C 等学者在定性 、半定量等故障诊 断方法方面做出了比较突出的成果. 而 SDG 在安全 评价方面的应用是 Venkatasubramanian 等近几年开 始研究的 ,现已取得一些成果. 近几年 SDG 技术的 研究是以计算机 、人工智能等技术为基础的 ,而企业 大规模生产又亟需这方面的技术支撑 , 所以在这方 面的研究又掀起了一个新的高潮.
的阈值. 定义 3 具有样本 ψ的 SDG 模型 γ = ( G0 ,φ)
之中 , 如果 ψ(5+ ak) φ( ak) ψ(5- ak) = + , 则该支路
ak 称为相容 ; 如果 ψ( nk) ≠0 ,则该节点 nk 称为有效节点. 定义 4 当一个 SDG 模型 γ = ( G0 ,φ1 , …,φk)
model)
SDG 最初的定义是完全定性的 , 但由于应用中 的需要 ,需要适当引入定量信息 , 与之相对应 , SDG 的模型也需要做出一些修改 , 这主要体现在支路的 定义上. 例如增益 、延迟 、概率等信息都可以在支路 上体现出来 ,而不仅仅是相容和不相容的两个值了. 比较典型的例子是 Yu C C 等[10]通过模糊集理论中 的隶属函数 ( membership functions ) 将稳态增益的知 识引入定性的 SDG 模型. 此外 , Wilcox[11 ] 在 SDG 的 基础上提出类似的 PCEG ( Possible Cause and Effect Graph) 概念.
几种不同时频分析法对INS信号的分析和比较
暋文章编号:1673灢6338201002灢0092灢05几种不同时频分析法对INS信号的分析和比较吴富梅12张晓东121.信息工程大学测绘学院河南郑州暋4500522.西安测绘研究所陕西西安暋710054摘要:目前用于分析INS随机噪声的方法主要有Fouirer变换、功率谱密度以及Allan方差法。
针对INS信号中存在的几种噪声分别给出了它们的功率谱密度函数和Allan方差分析了它们在时频域表达中的不同特性最后用模拟数据和实测数据对这3种时频域分析法进行了比较和验证。
结果表明:相比于Fouirer变换和功率谱密度法Allan方差法在随机噪声分析方面具有明显的优势不仅能够分析出信号中不同噪声的特性还能够确定其系数的大小。
关暋键暋词:Fouirer变换功率谱密度Allan方差惯性导航系统随机误差中图分类号:P227暋暋暋文献标识码:A暋暋暋DOI 编码:10.3969/j.issn.1673灢6338.2010.02.005AnalysisandComparisonofINSSignalBasedonSeveralMethodsWUFu灢mei1敩2敩ZHANGXiao灢dong1敩2敤1敭InstituteofSurveyingandMapping敩InformationEngineeringUniversity敩Zhengzhou450052敩China敾2敭Xi 「anResearchInstituteofSurveyingandMapping敩Xi「an710054敩China敥Abstract敽Fouirertransform敩powerspectraldensityandAllanvariancearethethreemethodswhichareoftenusedtoanalyzethe randomnoisesofINS敭Inthispaper敩thepowerspectraldensityfunctionsandAllanvari灢ancefunctionsarepresentedforfivekindsofrandomnoisesofINS敩thecharacteristicsofwhichareanalyzed敭Twoexamplesofsimulateddataandrealdataaregiventoshowthatcomparedwithfouirertransfor mandpow灢erspectraldensity敩theAllanvariancecannotonlydistinguishthedifferentrandomnoisesbutalsogetthecoef灢ficientsofthenoises敭Keywords敽Fouirertransform敾powerspectraldensity敾Allanvariance敾INS敾randomnoise暋暋惯性导航系统INS能够在短时间内提供高精度的位置、速度和姿态信息。
BUILDINGINFORMATIONMODELING
Building Information ModelingIntroductionBuilding Information Modeling (BIM) is a process that involves the creation and management of digital representations of physical and functional characteristics of buildings. BIM provides a collaborative platform for architects, engineers, contractors, and other stakeholders involved in the construction industry to work together on a virtual model of a building, enabling better coordination, communication, and visualization throughout the entire lifecycle of the project.Benefits of BIMBIM offers several benefits compared to traditional 2D drafting and design methods:1.Improved collaboration: BIM allows differentdisciplines to work together on a shared model, reducing conflicts and improving coordination between teams. This collaboration leads to improved efficiency and reducederrors during construction.2.Visualization and simulation: BIM enables thecreation of realistic 3D models that can be visualized and simulated before construction begins. This allowsstakeholders to better understand the design, identifypotential issues, and make informed decisions before costly construction mistakes occur.3.Clash detection: BIM software can automatically detect clashes or conflicts between different elements of the design, such as MEP systems and structural components. This helps to identify and resolve clashes early in the design phase, reducing the need for costly rework during construction.4.Improved communication: BIM facilitates better communication between project teams through the use of a centralized database. All stakeholders have access to the most up-to-date information, reducing misunderstandings and improving decision-making.5.Efficient cost estimation: BIM software can generate accurate quantity takeoffs and cost estimates based on the 3D model, reducing the time and effort required for manual calculations. This helps to improve the accuracy of cost estimation and reduce the risk of cost overruns.BIM ProcessThe BIM process typically consists of the following key steps:1.Conceptualization and design: The first step in the BIM process is to create a conceptual design of the building using BIM software. This involves defining the basic architectural and structural elements of the building.2.Collaborative modeling: After the conceptual design is complete, different disciplines, such as architects, engineers, and contractors, collaborate on the model to addfurther details. This includes the addition of MEP systems, structural elements, and other building components.3.Clash detection and coordination: Once the modelis complete, it is checked for clashes and conflicts usingclash detection software. Any clashes or conflicts areresolved, and coordination between different disciplines is ensured to avoid errors during construction.4.Construction documentation: The BIM model isused to generate construction documentation, includingdetailed drawings and specifications. These documents are used by contractors during the construction phase.5.Construction and facility management: Duringthe construction phase, the BIM model is used to monitor the progress of the project and support decision-making.After construction, the BIM model serves as a valuableresource for facility management, allowing for efficientoperation and maintenance of the building.BIM ToolsThere are several BIM software tools available in the market that support the creation and management of BIM models. Some popular BIM tools include:•Autodesk Revit•ArchiCAD•Bentley MicroStation•Tekla StructuresThese tools provide a wide range of features and functionalities, including 3D modeling, clash detection, cost estimation, and construction documentation generation. The choice of the BIM tool depends on the specific requirements of the project and the preferences of the project team.ConclusionBuilding Information Modeling (BIM) has revolutionized the construction industry by improving collaboration, communication, and visualization throughout the entire project lifecycle. BIM enables stakeholders to work together on a shared model, detect and resolve clashes early, and generate accurate construction documentation. By embracing BIM and leveraging the power of BIM software tools, construction professionals can improve the efficiency, quality, and sustainability of their projects.。
阳泉矿区工程地质特征及对策
第43卷第15期• 66 • 2 0 1 7 年 5 月山西建筑SHANXI ARCHITECTUREVol.43 No.15May.2017文章编号：1009-6825 (2017) 15-0066-03阳泉矿区工程地质特征及对策王海祥(山西国辰建设工程勘察设计有限公司，山西阳泉045000)摘要:结合阳泉矿区的地质特征，探讨了适合该矿区的勘察方法，并阐述了斜坡场区、河流阶地场区、填土场区、采空区场区以及 河床、河漫滩场区的地基处理措施，以供参考。
关键词:矿区，地质特征,地基处理措施，勘察方法中图分类号:P642 文献标识码:A〇引言阳泉矿区地处太行山中段西侧，以中~中低山地貌为主，区内沟壑纵横，风化剥蚀严重，地形起伏大，海拔标高从650 m到 1 100 m不等，除河床、河漫滩、河流阶地较为平坦之外，多为陡峻 的坡地，坡率多在20° ~ 70°不等。
在部分坡地、山顶、河流阶地分 布有厚薄不一的第四系中~晚更新世黄土、冲洪积成因的砂类土及卵石、坡积及残积成因的碎石土覆盖。
河床及河漫滩等沟谷则 以薄层~中厚层的第四系全新世河流冲洪积成因的卵石、砂砾、砂类土覆盖。
坡地、山脊多为石炭纪~二叠纪时期地层的岩石大 面积出露。
阳泉矿区表现为“多山、多石、少土、少平地”的特点。
阳泉矿区煤炭资源丰富，埋藏浅,开采条件优越，交通便利，开采 历史十分久远，北宋时已有煤炭开采的记载。
特别是从20世纪 开始逐步进人煤炭开采的全盛时期，在矿区形成了不同时期、不 同规模、不同形式的各类采空区。
由于多年的开采结果，使得矿 区地质环境遭受到严重的破坏，由此引发的地表沉陷、开裂大范 围发生，甚至引起崩塌滑坡等地质灾害发生，“采空区”成为矿区 建设中不可回避的主要工程地质问题。
1矿区工程地质特征1.1 斜坡场区的工程地质特征矿区斜坡场区表层多有黄土覆盖，厚度一般在5 m~10 m，达 15 m以上者较少，自上而下依次为粉土、粉质粘土、碎石土、基岩。
英语词汇
序号认知词汇中译词意序号认知词汇中译词意1360-degreefeedbackprocess360度反馈过程151Jobanalysis工作分析2Absence缺席152Jobclassificationsystem工作分类法3Acceptability可接受性153Jobdescription工作描述4Achievementtests成就测试154Jobdesign工作设计5Actionplan行动计划155Jobenlargement工作扩大化6Accountability有责任156Jobenrichment工作丰富化7Adolescent青少年157Jobevaluation工作评价8Adverseimpact负面影响158Jobcommitment工作认同9Aggressive有闯劲的，敢做敢为的159Jobrankingsystem工作重要性排序法10Allowance津贴，补助160Jobrotation工作轮换11Ambition 野心，雄心161Jobsatisfaction工作满意度12Analyticapproach分析法162Jobspecification工作规范13Announcement公告163Jointventurecompany合资公司14Applicant求职者164Keyperformanceindicator,KPI关键业绩指标15Application申请165Laborrelationsprocess劳动关系进程企业人力资源管理师（二级）专业词汇表16Appraisal评价,评估166Leaderless groupdiscussion无领导小组讨论法17Appoint任命167Learningorganization学习型组织18Arbitrary仲裁168Linemanager直线经理19Assessmentcenter评价中心169Maintenance ofmembership会员资格维持20Attitudeawareness andchangeprogram态度认知与改变计划170Management byobjectives, MBO目标管理21Attitudinalstructuring态度构建171Managementforecasts管理预测22Authority权威172Managementprocess管理过程23Audiovisualinstruction视听教学173Managerappraisal经理评估24Auditapproach审计法174Managingdiversity管理多元化25Balancedscorecard综合评价卡，平衡计分法175Manpower人力，劳动力26Bargain商谈176Materialincentive物质奖励27Behaviormodeling行为模拟177Mediation调解28Behavior-basedprogram行为改变计划178Mentor导师29Benchmarks基准179Meritguideline绩效指南30Benefits福利180Minimumwage最低工资31Bonus奖金181Morale士气32Businessplanning企业规划182Mobility流动性33Businessdivision事业部183Motivation tolearn学习的动机34Businessintegration业务整合184Needsassessment（培训）需要评价35Candidate候选人185Nightshift夜班36Careeranchor职业锚186Nonprofitorganization非营利组织37Careercounseling职业咨询187Occupation职业38Careercurves职业曲线188On-the-jobtraining, OJT在职培训39Careermanagementsystem职业管理系统189Openculture开放文化40Careerdevelopment职业发展190Opportunity toperform实践的机会41Centralization集权化191Organizationdesginanddevelopment组织设计与发展42Coach教练192Organizationalanalysis组织分析43Cognitiveability认知能力193Organizationalcapability组织能力44Cognitiveoutcomes认知性结果194Organiztionchart组织结构图45Collectivebargainingprocess劳资谈判过程195Organizationcode组织代码46Commitment承诺，义务196Orientation入职培训47Communicationskill沟通技巧197Outlay费用48Compa-ratio比较比率198Outplacementcounseling重新谋职咨询49Compensablefactors报酬要素199Output产出50Compensation报酬，补偿200Outsourcing外包51Competencyassessment能力评估201Overpay超额工资52Competency model能力模型202Panelinterview小组面试53Competitiveadvantage竞争优势203Payclaim加薪要求54Compromise妥协204Paygrade工资等级55Concentrationstrategy集中战略205Paystructure工资结构56Consultation商量，请教206Pay-for-performancestandard按绩效的报酬标准57Consumerpriceindex,CPI消费者价格指数207Pay-policyline工资政策线58Continuouslearning持续学习208Payroll职工薪水册59Coordinationtraining合作培训209Pension养老金，退休金60Corecompetencies核心竞争力210Peerappraisal同事评估61Coststructure成本结构211Pep talk鼓舞动员谈话62Criticalincidentmethod关键事件法212Performanceappraisal绩效评价63Cross-culturalpreparation跨文化准备213Performancefeedback绩效反馈64Cross-training交叉培训214Performancemanagement绩效管理65Culturalenvironment文化环境215Performanceplanningandevaluation (PPE)绩效规划与评价系统66Culturalshock文化冲击216Post岗位，职位67Customerappraisal顾客评估217Potential潜在的，可能的68CV(curriculumvitae)简历218Priority优先69Dataflowdiagram数据流程图219Probation试用70Decentralization分散化220Personcharacteristics个人特征71Decisionmaking决策221Personnelselection人员甄选72Decisionsupportsystems决策支持系统222Piecework计件工资73Deficiency缺乏223Positionanalysisquestionnaire,PAQ职位分析问卷调查74Defined-benefitplan养老金福利计划224Powerdistance权力差距75Defined-contributionplan资方养老金投入计划225Predictivevalidation预测效度76Delayering扁平化226Profitsharing利润分享77Demandforecasting需求预测227Promotion晋升78Depression沮丧228Psychologicalcontract心理契约79Developmentplanningsystem开发规划系统229Questionnaire调查问卷80Differentialpiecerate差额计件工资230Rapport和谐，亲善81Directcosts直接成本231Readability易读性82Discipline纪律232Readiness fortraining培训准备83Disparate impact差别性影响233Reasoningability推理能力84Disparatetreatment差别性对待234Reconciliation和解85Diversitytraining多元化培训235Recognition认可，承认86Dividends红利236Recruitment招募87Discrimination歧视237Redundancy冗余88Dismiss 开除，解雇238Reengineering流程再造89Downsizing精简239Reference参考90Downwardmove降级240Reject拒绝，否决91Efficiency wagetheory效率工资理论241Reinstatement复职92Egalitarian平等主义242Relationaldatabase关联数据库93Earnings 所得，收入243Reliability信度94Efficiency效率244Remuneration报酬95Employeeempowerment员工授权245Reputation声誉，名声96Employeeleasing员工租借246Retention plan(核心人员)保持计划97Employeesurveyresearch雇员调查与研究247Repatriation归国准备98Entrepreneur企业家248Replacementcharts替换表99Equalemploymentopportunity(EEO)公平就业机会249Returnoninvestment (ROI)投资回报100Ethics道德250Roleambiguity角色模糊101Exitinterview离职面谈251Roleanalysistechnique角色分析技术102Expatriate外派雇员252Roleplay角色扮演103Expertsystems专家系统253Seniormanagement高级管理层104Explicitknowledge显性知识254Settlement解决，决定105Externalanalysis外部分析255Sickleave病假106Externalgrowthstrategy外边成长战略256Simulation仿真，模拟107Externallabormarket外部劳动力市场257Self-appraisal自我评估108Face tofacediscussion当面讨论258Subcontracting转包合同109Factorcomparisonsystem因素比较法259Substantivereason客观存在因素110Feedback反馈260Successor后任111Flathourlyrate小时工资率261Supplyforecasting供给预测112Flexiblebenefitsplans(cafeteriaplans)灵活的福利计划（自助福利方案）262Survey调查113Flextime 灵活的时间263Target目标，目的114Flowchart流程图264Talent才能，才干115Followup跟随，追随265Sicknote病假条116Formaleducationprograms正规教育计划266Situationalinterview情景面试117Frame ofreference参照系267Skillinventories技能量表118Functional jobanalysis, FJA职能工作分析268Skill-basedpay技能工资119Gainsharingplans收益分享计划269Specificity明确性120Globalization全球化270Spotbonus即时奖金121Goalsandtimetables目标和时间表271Staffingtables人员配置表122Graphicrating-scalemethod图式评估法272Strategic choice战略选择123Grievance委屈273Strategiccongruence战略一致性124Groupmentoringprogram群体指导计划274Strategic humanresourcemanagement战略性人力资源管理125Guidelines指导方针275Strategyformulation战略形成126Headhunter猎头276Strategyimplementation战略执行127Healthyandsafety健康安全277Subordinate下属128Handover工作交接278Successionplanning可持续发展计划129High-performance worksystems高绩效工作系统279Tacitknowledge隐形知识130Hourlywork计时工资制280Taskanalysis任务分析131Humancapital人力资本281Teamleadertraining团队领导培训132Humanresourceinformationsystem(HRIS)人力资源信息系统282Teambuilding团队建设133Humanresourcemanagement人力资源管理283Topstratum高层134Humanresourcesplanning, HRP人力资源计划284Termination终止135Income 收入，收益285Totalqualitymanagement (TQM)全面质量管理136Indirectcosts间接成本286Trainingadministration培训管理137Individualism/collectivism个人主义/集体主义287Trainingoutcomes培训结果138Inflation通货膨胀288Transactionprocessing事务处理139Input投入289Trendanalysis趋势分析140Insurance保险290Turnover离职，流动141Intellectualasset知识资产291Utilityanalysis效用分析142Internalanalysis内部分析292Validity效度143Internalgrowthstrategy内部成长战略293Verbalcomprehension语言理解能力144Internallaborforce内部劳动力294Vesting既得利益145Internet互联网295Voicing发言146Internshipprograms实习计划296Wage andsalarysurvey薪资调查147Interview面试297Wagefreeze冻结工资增长148Industrialization产业化298Web-basedtraining网上培训149IT(InformationTechnology)信息技术299Welfaresystem福利体系150Invest投资300Workpermit/workcertificate就业许可证。
适用于轴承故障诊断的数据增强算法
2021577轴承故障诊断在制造业的故障预测和健康管理中起着十分重要的作用。
除了传统的故障诊断方法以外，学者们将改进过的机器学习[1-4]和深度学习算法[5-8]应用于故障诊断领域，其诊断效率与准确率得到了较大的提高。
在大部分应用中，这些算法有两个共同点[9]：第一、根据经验风险最小化原则（Empirical Risk Minimization，ERM）[10]训练故障诊断模型。
第二、使用此原则训练的诊断模型的性能优劣主要取决于所使用的训练样本的数量和质量。
但在工业应用中，数据集中正负样本的比例不平衡：故障数据包含着区分类别的有用信息，但是所占比例较少。
此外由于机器的载荷、转轴转速等工况的不同，所记录的数据并不服从ERM原则中的独立同分布假设。
这两点使得ERM原则不适用于训练工业实际场景中的故障诊断模型，并且文献[11]表明使用ERM原则训练的模型无法拥有较好的泛化性能。
数据增强算法是邻域风险最小化原则[12]（Vicinal Risk Minimization，VRM）的实现方式之一，能够缓解ERM原则所带来的问题。
在VRM中通过先验知识来构建每个训练样本周围的领域区域，然后可从训练样本的领域分布中获取额外的模拟样本来扩充数据集。
例如，对于图像分类来说，通过将一个图片的领域定义为其经过平移、旋转、翻转、裁剪等变化之后的集合。
但与机器学习/深度学习中的数据不同，故障诊断中的数据（例如轴承故障诊断中的振动信号）具有明显的物理意义和机理特征，适用于机器视觉的数据增强方法可能导致物理意义的改变。
因此，本文从信号处理和信号分析的角度出发，设计了一种适用于轴承故障诊断中振动信号的数据增强方法。
适用于轴承故障诊断的数据增强算法林荣来，汤冰影，陈明同济大学机械与能源工程学院，上海201804摘要：针对在轴承故障诊断中存在的故障数据较少、数据所属工况较多的问题，提出了一种基于阶次跟踪的数据增强算法。
该算法利用阶次跟踪中的角域不变性，对原始振动信号进行时域重采样从而生成模拟信号，随后重新计算信号的幅值来抵消时域重采样以及环境噪声对原始信号能量的影响，最后使用随机零填充来保证信号在变化过程中采样长度不变。
Database Design
Design decide the system quality
Design Stage
Coding Stage
Testing Stage
Production
Design Stage
Logical Design
Physical Design
Maintain Plan
I Logical Design
Accurate data Consistent data Reduced costs
Easier to implement changes in your business Business requirements can be satisfied in database design
How to Model Data
Identify entity types Identify attributes Assign keys Inversion Entries Identify relationships Normalize to Reduce Data Redundancy
They describe their data requirements to the data modeler and review the models created. They use the models for impact analysis of changes to business requirements.
Does not include any redundant data.
Does not include any derived data. Business experts drive the model.
数据库应用系统开发第二部分-数据库设计
7
GRAI/GIM方法
• GRAI （Graph with Results and Activities Interrelated）方法是 由法国Bordeaux第一大学提出的，是专门为在生产系统制定决策 而开发的。
• GRAI由一个生产系统由一个物理系统和一个生产控制系统组成， 物理系统是一组制造单元，其功能是将原材料或部件转变为完成 的部件或一个完成的产品。生产控制系统制定决策，它由一个信 息系统和一个决策系统组成。它基于诸如定货、资源和能源等方 面的信息制定决策，以便物理系统执行其功能。
2
数据库设计的特点
• 三分技术、七分管理、十二分基础数据 • 特点一：硬件、软件和管理的结合 • 特点二：结构（数据）设计和行为（处理）设计结合
3
企业建模方法
• CIM-OSA方法 • ARIS方法 • IDEF方法 • GRAI/GIM方法 • BAAN/DEM动态企业建模方法
4
CIM-OSA方法
14自Βιβλιοθήκη 化设计工具的缺点• 工具本身价格高 • 需要购置支持该工具的相关硬件设备 • 开发人员需要进行一定的培训，培训费用高 • 自动化工具并不能满足管理人员和设计人员的所有需
13
自动化设计工具的优点
• 便于设计人员制作各种图表，有助于开发小组、管理 人员以及客户了解业务和数据库模型
SilkCentral Issue Manager 2010 R2数据库模型说明书
Database ModelSilkCentral®Issue Manager 2010 R2Database ModelBorland Software Corporation4 Hutton Centre Dr., Suite 900Santa Ana, CA 92707Copyright 2009-2010 Micro Focus (IP) Limited. All Rights Reserved. Issue Manager contains derivative works of Borland Software Corporation, Copyright 2004-2010 Borland Software Corporation (a Micro Focus company).MICRO FOCUS and the Micro Focus logo, among others, are trademarks or registered trademarks of Micro Focus (IP) Limited or its subsidiaries or affiliated companies in the United States, United Kingdom and other countries.BORLAND, the Borland logo and Issue Manager are trademarks or registered trademarks of Borland Software Corporation or its subsidiaries or affiliated companies in the United States, United Kingdom and other countries.All other marks are the property of their respective owners.December 2010ContentsIntroduction1Overview . . . . . . . . . . . . . . . . . . . . . 1Chapter 1Database Schema Model3Overview . . . . . . . . . . . . . . . . . . . . . 3Issues . . . . . . . . . . . . . . . . . . . . . . . 4Archived Issues . . . . . . . . . . . . . . . . . . 5User and Group Data . . . . . . . . . . . . . . . 6GUI Configuration. . . . . . . . . . . . . . . . . 7Inboxes . . . . . . . . . . . . . . . . . . . . . . 9Workflow Routing . . . . . . . . . . . . . . . . . 9SilkCentral Issue Manager Database Model iiiiv SilkCentral Issue Manager Database ModelSilkCentral Issue Manager Database Model 1IntroductionAbout this manual This guide offers an overview of SilkCentral ® Issue Manager’s database schema.This chapter contains the following sections:OverviewThis guide describes the SilkCentral Issue Manager database schema. It is designed to assist users in creating reports and accessing SilkCentral Issue Manager via its API.Section Page Overview 1I NTRODUCTIONOverview2SilkCentral Issue Manager Database ModelSilkCentral Issue Manager Database Model 31Database Schema ModelIntroduction This guide offers an overview of SilkCentral Issue Manager’s database schema.What you will learn This chapter contains the following sections:OverviewThis guide describes the SilkCentral Issue Manager database schema. It is useful for creating reports and accessing SilkCentral Issue Manager via its API.Section Page Overview3Issues4Archived Issues5User and Group Data6GUI Configuration7Inboxes9Workflow Routing 94SilkCentral Issue Manager Database Model1 D ATABASE S CHEMA M ODEL IssuesIssuesIM_Defect - This is the base table for Issues. It contains the following data:-Main issue fields-Fields for custom tabs (Columns: Custom1 - Custom20)-Current status and inbox that each issue is assigned to. The Inboxcolumn is NULL when an issue is closed (any terminal state) and not assigned to an inbox.-Creation and change dates; stored in multiple formats for varyingquery types. DbDateCreated is the system's timestamp (timestampof the database itself) which may change more frequently than the1 D ATABASE S CHEMA M ODELArchived Issuesuser’s timestamp. It is used for system operations, for exampleupdating caches. Usually it does not make sense to present thistimestamp to the user.IM_DefectV ersion - Holds a copy of every version of the IM_Defect table. Thistable enables versioning, which is required for internal data warehousepurposes.IM_DefectHistory - This table contains the history of all actions that areperformed on issues. Each row represents a modification of an issue and lists the“before” status (StatusIN), “after” status (StatusOUT), action, inbox the issuewas assigned to initially (AssignedIN), inbox the issue was reassigned to(AssignedOUT), disposition, note of the action, release information, linkeddefectIDs, values of possible custom fields, and creation date (when the actionwas performed).IM_DefectDescript - Contains the description of an issue. Descriptions are splitinto 2,000-character sequences.IM_DefectDescriptV ersion - Holds a copy of every version of the IM_DefectDescript table. This table enables versioning, which is required forinternal data warehouse purposes.IM_DefectCase - Contains case data entered on the Customer tabIM_DefectNotify - Contains notification triggers.•IssueID_fk <> NULL means the trigger is assigned to a specific issue.•IssueID_fk = NULL means the trigger is valid for all issues in the project(i.e., a system-wide trigger).IM_DefectFile - Contains data from attached files. When entering IssueManager files, the Fullpath column is empty and the file is stored in the IM_DefectFileChunks table. When importing an Issue Manager 3.3 database and theattached file can't be accessed during the import, the value of the Fullpathcolumn is kept.IM_DefectFileChunks - The content of the attached files.IM_DefectTest - Contains data regarding testcases attached on the testcase tab. Archived IssuesTables for archived issues are copies of the tables described in the previoussection, “Issues”. When issues are archived, data is copied from IM_Defect...tables to IM_Archive… tables.SilkCentral Issue Manager Database Model56SilkCentral Issue Manager Database Model 1 D ATABASE S CHEMA M ODEL User and Group DataUser and Group DataSCC_UserGroups - Contains the available user groups.SCC_Users - Contains data about the individual users that have been set up to use SilkCentral.IM_Account - Contains Issue Manager-specific data for users who are assigned to a specific Issue Manager project. SettingWorkGroupID_fk is the ID of a group stored in IM_WorkGroup where default values for initial states are taken when a user has no specified values.IM_AccountSetting - Contains data regarding the most recently entered values of a user and preferences such as "verify own issues" and "show global notifications in notification tab".IM_WorkGroup - Contains the default initial states of groups that are assigned toa specific Issue Manager project.GUI ConfigurationNote DateCreated and DateLastMod fields hold the timestampswhen the data set was created/changed the last time from a userperspective. These values are displayed in the GUI and are commonlyused for display in reports. DbDateCreated and DbDateLastMod arethe system's timestamps (timestamp of the database itself) which maychange more frequently than the user’s timestamps. They are used forsystem operations, for example updating caches. Usually it does notmake sense to present these timestamps to the user.GUI ConfigurationIssue Manager offers some options for configuring the GUI (labels, customfields, and permissions) for each project. These settings are stored in thefollowing tables.IM_CustomGuiProjectID_fk The project this configuration is to beapplied to.GUIObjectID The ID of the object in the GUI. 200 -219 are the fields in the custom tab.GUICaption The label of the field.GUIType 1 = Checkbox2 = Textfield3 = Dropdown4 = Tab labelV alueListID_fk When an object is of type“dropdown”, this ID specifies the listthat is to be used for the values.InsertRole The mode for the field when enteringan issue.2 = Optional3 = Read-only4 = RequiredUpdateRole The mode for the field when editing anissue.2 = Optional3 = Read-only4 = RequiredGUI ConfigurationThis table contains settings that are to be applied when only certain groups are to be allowed to access certain fields. When a GuiObject has no entries in this table, access is not restricted to a specific group.CaseMappingSpecifies how text field values are to be stored 0 = as entered 1 = Upper case 2 = Lower caseUserCreated User who created the entry.DateCreated Date when the entry was erLastMod Last user who modified the entry.DateLastMod Date when the entry was last modified.DbDateCreatedTime when the entry was created on the DB server. This field is meant for internal use and may change more frequently than DateCreated .DbDateLastModTime when the entry was modified on the DB server. This field is meant for internal use and may be change more frequently than DateLastMod .IM_CustomGuiPermission ProjectID_fk The project this permission configuration is to be applied to.GUIObjectID_fk The ID of the object in the GUI.PermissionTypeThe permission type:1 = Insert 2 = UpdateWorkGroupID_fkThe ID of the group that this permission is granted to.IM_CustomGuiWorkflow RoutingInboxesIM_Inbox - This table contains the inboxes that exist in Issue Manager projects. Each inbox is assigned to a specific group.Workflow RoutingIM_Product - Contains the default routing information for a product in a certain project. This routing can be refined using entries from the IM_RoutingLogic table.When an issue changes state, this information is used to determine the inbox that the issue is to be routed to. Depending on the owner of the new workflow state,IM_CustomInboxThis table contains settings regarding inbox view (displayed columns, sorting, column header) for the users in a project.ProjectID_fk The project this view is assigned erID_fkNULL - Specifies that the default view on inboxes be applied for the project.Specific UserID - When a user has configured a custom view.ColOrder The position of the column.ColSortingThe order by which columns are to be sorted:0 = None1 = Ascending2 = DescendingColLabel The displayed column header.ColName The name of the column in the database.UserCreated DateCreated UserLastMod DateLastModWorkflow Routingthe issue will be routed to the appropriate inbox listed in the columns QAInbox, DevInbox, ENHInbox, or DOCInbox.IM_WorkflowState - The possible states for an issue. Each state has an owner that is used to determine the IM_RoutingLogic or IM_Product table inbox that is to be used.IM_WorkflowAction - Includes all actions that users can perform on an issue in a certain state. These entries specify the state that an issue will have after the action is performed, the fields that are to be shown in the Action dialog, theIM_RoutingLogicProjectID_fk The project this routing is specified for.ProductID_fk The product this routing is specified for.SortOrderSpecifies the sequence in which the entries are to be processed. The first matching entry is taken for routing the issue.DefectTypeGroup SOFT = BUG and ENHANCEMENT DOC = DOC-ISSUEReleaseMatch At least one of these must be specified.The three ...MatchID_fk columns specify foreign keys. The other columns may contain query strings such as "Windows%"PlatformMatch ComponentMatch ReleaseMatchID_fk PlatformMatchID_fk ComponentMatchID_fk QAInbox Depending on the owner of the new workflow state, the issue will be routed to the inbox specified in these columns.DEVInbox ENHInbox DOCInbox UserCreated DateCreated UserLastMod DateLastModWorkflow Routing ActionCode that is to be added to the History entry, the Action button label, and the tool tip.IM_WorkflowActionPermission - Specifies the groups that are allowed to perform a certain action.Workflow Routing。
Tall,Grande,orVentiModels
Tall,Grande,or Venti Models?David Ford,M.ASCEDavid Ford Consulting Engineers,Inc.,P.O.Box188529,Sacramento, CA95818.E-mail:***************************The Starbucks store near our office offers beverages in three sizes:tall,grande,or venti͑or small,large,and very large,for the uninitiated͒.What’s the best choice?The venti is largest,and it surely includes that extra java jolt that you need the morning after a late night in front of your workstation.That’s a jolt that the diminutive tall could never deliver.But I’ve noticed that when I purchase the venti size,a portion of the beverage often sits in my cup until it is cold,ending up in the sink or in the sad potted plant in my office.Venti really is more than I needed.Recently,I’ve wised up and scaled back, getting the grande instead.It is just barely enough:it meets my need with no waste.We should use similar logic when choosing models that we select or create to provide information for water resources plan-ning and management.We don’t always need the venti model—a model that is big,all-encompassing,complex,or cutting edge. Instead,we need models that are—to borrow a term from current software development jargon—just barely good enough.What Is“Good Enough”?Hold on a minute.Don’t get too excited by the notion of doing engineering work that is just barely good enough.I am not advo-cating the use of models that are inaccurate,incomplete,poorly done,or just plain wrong.Those are not good enough.Instead, I’m promoting using models that meet the project requirements, nothing more and nothing less.This selection strategy is not new to us in water resources planning.We use it as the standard for selecting from among alternativeflood-defense project sizes,for example.Consider the simplified case for which benefit and cost are a function of capac-ity as illustrated in Fig.1.Cost is the dashed line,and benefit is the solid one.Clearly,a bigger project yields greater benefit in this example. But selecting the biggest͑venti͒alternative is not always the best strategy,for it fails to consider the trade-off with cost.In the illustration,larger sizes have cost that exceeds the benefit.The same may be true of models.Our standard is to consider the net:the difference between benefit and cost.The optimal choice—the choice that is just good enough—is the capacity that yields the maximum net benefit.In thefigure,the optimal choice is denoted with a dotted vertical line.A project that is larger,to the right of the line,yields greater benefit,but it costs more than it returns in benefit.A project that is smaller,to the left of the line,could be made more cost-effective by an increase in capacity.But you probably understand that already.Our Current Decision Making StandardEven though we understand well the net benefit logic and apply the rules for selecting good enough projects forflood defense, we—and I include myself in thisfinger pointing—haven’t done as well applying the same logic in making decisions about models. Instead of selecting good enough models,we succumb to the temptation to select the venti model.Consider,for example,the task of selectingflow routing mod-els.Advances in open channelflow modeling software make it easy to develop unsteadyflow models of complex networks.If coupled with advanced terrain data-processing features of modern GIS tools,the models can produce detailed,accuratefloodplain maps for land-use regulation.The continued investment by FEMA in such technology is evidence that the damage reduction resulting from decision making with the improved maps offsets the cost of using the advancing technology to produce them.Now that such models are readily available,are they the opti-mal choice for allflow routing problems?For example,should we use the complex models and GIS mapping extensions in opera-tional settings to route reservoir releases to forecast downstream stages?Or is it acceptable to use simpler hydrologic routing mod-els coupled with reliable ratings?Are they good enough?We can answer that question correctly only after considering the benefits and costs of the alternatives and the characteristics of the channels modeled.We must consider that in operational set-tings,we cannot fail to make a forecast—one that is sufficiently accurate and issued early enough to permit actions that protect lives and property.Models that do not converge or that are un-stable are not acceptable in that context.Thus,if we use an unsteady-flow model,it must befine-tuned to ensure that the “hiccups”that may occur when the models are used forfloodplain mapping do not occur during real-time forecasting.Thisfine-tuning incurs a cost,a cost that may not be justified if the simpli-fied routing models yield forecasts that are within acceptable Fig.1.Optimal size is just barely good enoughJOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT©ASCE/JANUARY/FEBRUARY2006/1tolerances.By using overly complex models,we may err on the venti side of grande.However,we can err on the tall side of grande.For example,if elevations in the channel are influenced by tides downstream,a simple hydrologic routing model is not appropriate:it is not good enough.The additional cost of developing and using the unsteady flow model is justified in that case.In fact,it is required. Considerations in Finding“Good Enough”Grayson and Chiew͑1994͒suggest that tofind models that are good enough͑my words,not theirs͒,we must consider the following:•Objectives of the overall exercise.Is the model to be used by researchers for fundamental research;by modelers or technical experts to givefinal answers;by managers to evaluate the effects of management options;or by the community to assist in education,technology transfer,and ownership of solutions?Only if we are clear on the reason for using the model can we select a model that meets the requirements of its intended use, is sufficiently accurate,is sufficiently detailed,and isflexible enough to change as our concept of good enough evolves.•Access to expertise for developing and using the model.Gray-son and Chiew note that models don’t generate knowledge—they simply present what we already know.Models that are good enough present that information in a manner that is us-able by the intended audience,without going beyond that.Models developed for use by modelers or technical experts ͑geeks͒may be structured to reveal insights at lower cost than models properly developed for casual users or managers.Technical experts are satisfied with tables of numbers,whereas managers may want color-coded maps and charts.If we do not have ready access to the experts to interpret the results,good enough may require a more sophisticated interface.•Data availability.Availability of data is often the binding con-straint in model selection.As Grayson and Chiew͑1994͒point out,it is possible to simulate virtually anything with the com-puter.But whether the results bear any relationship to reality depends on the data available for calibration and application.We could,for example,select a watershed model withfine spatial resolution of infiltration and overlandflow.However,if we have no streamflow data within the watershed to permit calibration or even verification of that model,the model is not good enough.The cost of developing the higher-resolution model is not offset by the benefit—at least not by a tangible benefit that we can demonstrate clearly.•Time and money resources.A good enough model will be de-veloped and applied within the limits of the time and money available.If a model produces great results but provides them too late to have an effect on the decision making,it is not good enough.Similarly,if the model selected and used was the one in hand when the money ran out,chances are that it is not good enough.Tips for Finding“Good Enough”Here,drawn from software engineering͑Bach1997;Ambler 2004,2005;Hill2005;Bach2003͒and hard knocks,are some suggestions aboutfinding good enough when making modeling decisions:1.Develop a big picturefirst.We should develop a vision forand design of a complete decision support system͑DSS͒rather than focus narrowly on smaller aspects of the system.Don’t worry—we can still dive into the details when appro-priate.But having the“vision thing”will help identify the expertise needed,the data required,and the time and money required.2.Bring broad expertise to the task.The requirements for mostmodern DSSs in water resources planning and management are complex,since the DSSs deal with a wide variety of economic,environmental,legal,and technical issues.To be good enough,the models must address all the relevant issues.Modular design helps.3.Identify the users,engage them,and work with them to setpriorities.We must rely on the users of our DSSs to identify requirements,and we must select the models to meet those requirements͑but not more͒.And of course,when time and money are limited,we must rely on users to prioritize their requirements.Doing so enables us to work on the most im-portant tasks,finding the models and creating a DSS that is good enough.The broad vision and broad expertise are there-fore especially important.If we must leave something out now,at least we will know where it willfit—and how—if the resources are available later.4.Don’t get hung up with technology dependency,unless doingso is necessary.A successful system is not limited to one that uses only one vendor’s technology.We should focus on the needs,not on the technology that meets the needs.5.Design for changing requirements.Change r-mation users often don’t know what they want or need.If they do know,they may not know how to communicate that need,other than to tell us IKIWISI͑I’ll know it when I see it͒or IKIWINI͑I’ll know it when I need it͒.And we must recognize that the external environment changes.New de-mands for information may arise,new information technol-ogy may become available,new water users may come and old ones may go,and new modeling capabilities may be presented.6.Design or select models that require only data that are avail-able.Woolsey and Swanson͑1975͒make strong and witty arguments that,with many models,“…the data is not present,or if it is present,it is not in the right form.”To avoid this outcome,we must identify the data available and include,in our assessment of good enough,the cost to ac-quire more or different data to feed the algorithms.7.Display proposed system features.Proposed plans and de-signs for a DSS are important communication channels.We should make plans accessible.We know of a case in which the user of a DSS threatened to“jump on the chest”of the system developers if information was presented as a pro-posed table.Instead,he wanted to see a map.Here,nothing short of those GIS extensions would be good enough. ConclusionJust as we do with coffee and water-resources investment plan-ning,we must consider the trade-offs of benefits and costs in selecting models.Increased complexity commonly comes with increased cost.Increased complexity,however,is not necessarily accompanied by increased benefit in insights gained by using the models.2/JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT©ASCE/JANUARY/FEBRUARY2006We must consider the objectives of the overall exercise,our access to expertise,the data available,and our time and money resources while we make decisions about models.If grande is good enough,we should select it.And,as one reader of a draft of this editorial pointed out,we can even request and get a short coffee͑or a short model͒—even though it is not on the vendor’s menu—if it is good enough to meet our needs.ReferencesAmbler,S.W.͑2004͒.“When are models and documents good enough?”Software Development’s Agile Modeling Newsletter,͗http:// /documents/sϭ6977/sdmam1204/͘October1, 2005.Ambler,S.W.͑2005͒.Just barely good enough models and documents,͗/essays/barelyGoodEnough.html͘October1,2005.Bach,J.͑1997͒.“Good enough quality:Beyond the buzzword.”Computer,͗www.satisfi/articles.good_enough_quality.pdf͘October1,2005.Bach,J.͑2003͒.“The challenge of‘good enough’software.”͗http:/ www.satisfi͘October1,2005.Grayson,R.B,and Chiew,F.H.S.͑1994͒.“An approach to model selection.”Hydrology and Water Resources Symp.Institute of Engineers,Adelaide,Australia.Hill, D.͑2005͒.“Insanely great,or just good enough?”͗http:// /reactor/opinion_02.04.asp͘October1,2005.͘Woolsey,R.E.D,and Swanson,H.S.͑1975͒.Operations research for immediate application:A quick and dirty manual,Harper&Row, New York.JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT©ASCE/JANUARY/FEBRUARY2006/3。
地理信息系统英语主要特征及其翻译策略
地理信息系统英语主要特征及其翻译策略地理信息系统英语主要特征及其翻译策略本文关键词：英语，主要特征，地理信息系统，策略，翻译地理信息系统英语主要特征及其翻译策略本文简介：1引言地理信息系统（GIS）是由系统硬件、系统软件、空间数据、应用模型和用户界面构成的空间信息及决策支持系统，兼跨地球科学、信息科学和空间科学等学科并广泛应用于资源管理、区域规划、国土监测、定位服务、交通运输等领域。
作为国家信息产业的重要组成部分，地理信息系统的发展趋势是网络化、智能化、集成化和地理信息系统英语主要特征及其翻译策略本文内容：1 引言地理信息系统（ GIS）是由系统硬件、系统软件、空间数据、应用模型和用户界面构成的空间信息及决策支持系统，兼跨地球科学、信息科学和空间科学等学科并广泛应用于资源管理、区域规划、国土监测、定位服务、交通运输等领域。
作为国家信息产业的重要组成部分，地理信息系统的发展趋势是网络化、智能化、集成化和空间信息化，并将成为未来信息产业的中流砥柱。
地理信息系统英语描述精确、客观，内涵丰富、更新迅速，专业性、跨学科性的信息密集。
谨严系统的翻译能够及时反映地理信息系统的最新动态，促进学科和技术方面的国际交流与合作，适应全球信息化浪潮的发展趋势。
针对地理信息系统英语的特点，本文拟分析缩略语、专业术语、新词、跨学科表达、句式、图表和公式等语言单位的翻译策略，译成专业规范的汉语，力求准确无误地传递原文信息。
2 缩略语的翻译科技英语的缩略符合语言交际的＂简捷性和实用性＂原则。
新事物和现代通讯的发展促动了缩略语的迅速涌现，例如： STEP（ Standard Exchange of ProductData Model 产品数据交换标准）、ASRS（ Automated Stor-age and Retrieval System 自动仓储系统）、FMS（ FlexibleMachining System 柔性加工系统）及 FYI（ For your infor-mation 供你参考）等。