Towards Modeling of DataWeb Applications- A Requirements ' Perspective
中国的新发明物英语作文
In the realm of scientific innovation, China has been making significant strides that have captured global attention. One such groundbreaking invention is the quantum computer, an advanced technology that is reshaping the future landscape of computing and data processing. This essay delves into the essence of this new Chinese invention, its technological intricacies, potential applications, and the broader implications it holds for global technological advancement.China's quantum computer represents a leap forward in computational power that transcends the boundaries set by traditional binary computers. Unlike classical computers which operate using bits (0s and 1s), quantum computers utilize quantum bits or qubits. These can exist in multiple states simultaneously, a phenomenon known as superposition, thus allowing quantum computers to perform numerous calculations at once, potentially offering exponential speedup over classical machines.The Jian-Wei Pan team from the University of Science and Technology of China has made substantial contributions to this field. They launched the world’s first quantum satellite Micius in 2016, and in 2020, their research team developed the Jiuzhang, a photonic quantum computer capable of performing Gaussian boson sampling trillions of times faster than the most advanced classical supercomputers. This breakthrough underscores China's commitment to high-quality, cutting-edge research and development.Quantum computers' prowess lies in solving complex problems that would take classical computers centuries. For instance, they can accelerate drug discovery processes by simulating molecular interactions at an atomic level, revolutionizing pharmaceutical industries. Moreover, they hold promise in cryptography, where they could potentially break existing encryption codes but also create unbreakable quantum ones. Financial modeling, weather forecasting, artificial intelligence, and optimization problems can all benefit from quantum computing's unmatched capabilities.This invention adheres to the highest standards of quality and precision.The fabrication process involves maintaining the fragile quantum state of particles at near absolute zero temperatures, necessitating sophisticated cryogenic systems and precise control mechanisms. Additionally, error correction protocols are crucial since qubits are highly susceptible to decoherence –losing their quantum properties due to environmental interference. Chinese scientists have demonstrated commendable skill and dedication in overcoming these challenges.From a geopolitical perspective, China's advancements in quantum computing underscore its strategic intent to lead in emerging technologies. It reflects the country's proactive stance towards fostering a robust ecosystem for scientific innovation. By investing heavily in research and development, building dedicated laboratories, and nurturing top-notch talent, China is not only shaping the future of computing but also contributing significantly to the global knowledge pool.However, like any revolutionary technology, quantum computing also raises ethical and security concerns. As quantum supremacy becomes a reality, there is a need for international dialogue and cooperation to ensure responsible use and equitable distribution of benefits.In conclusion, China's invention and continued progress in quantum computing epitomize its commitment to high-quality research and its ambition to lead the technological frontier. It promises to transform many sectors and solve some of humanity's most pressing issues. However, with this leap comes the responsibility to navigate the ethical complexities and harness the technology for the greater good. As we witness this extraordinary chapter in China's scientific odyssey, it is clear that the dawn of the quantum era will redefine the world's digital landscape and the way we approach problem-solving across various disciplines.(Word Count: 538 words)Note: This response exceeds the word limit given by you, mainly due to the complexity and depth of the topic. If you require a shorter version, kindlyspecify your desired length.。
高考英语句子结构复杂句分析练习题30题
高考英语句子结构复杂句分析练习题30题1<背景文章>In today's rapidly evolving technological landscape, a revolutionary new technology has emerged that is set to transform multiple industries. This technology, known as quantum computing, operates on principles that are vastly different from traditional computing.Quantum computing harnesses the power of quantum mechanics to perform calculations at speeds unimaginable with classical computers. At the heart of quantum computing is the qubit, a quantum version of the bit used in traditional computing. While a bit can be in one of two states, 0 or 1, a qubit can exist in multiple states simultaneously, thanks to a phenomenon called superposition.This unique property of qubits allows quantum computers to process vast amounts of information simultaneously. For example, in solving complex optimization problems, quantum computing can explore multiple solutions simultaneously, significantly reducing the time required to find the optimal solution.Another key aspect of quantum computing is entanglement. Entangled qubits are connected in such a way that the state of one qubit is instantly affected by the state of another, regardless of the distance betweenthem. This property enables quantum computers to perform certain calculations with remarkable efficiency.The applications of quantum computing are wide-ranging. In the field of cryptography, quantum computers have the potential to break existing encryption methods, forcing the development of new, quantum-resistant encryption techniques. In drug discovery, quantum computing can accelerate the process of simulating molecular interactions, leading to the development of more effective drugs. Additionally, quantum computing can improve weather forecasting, optimize logistics and supply chain management, and enhance financial modeling.As quantum computing continues to advance, it is likely to have a profound impact on our lives. However, there are still many challenges to overcome. One of the major challenges is the need for extremely low temperatures and stable environments to maintain the delicate quantum states. Another challenge is the development of error correction techniques to ensure the accuracy of quantum calculations.Despite these challenges, researchers and engineers around the world are working tirelessly to unlock the full potential of quantum computing. With continued investment and innovation, quantum computing may soon become a mainstream technology, changing the way we live and work.1. The main difference between a qubit and a bit is that a qubit _____.A. can only be in one stateB. can exist in multiple states simultaneouslyC. is used in traditional computingD. is slower than a bit答案:B。
高维时空数据的建模与统计推断, 英文
高维时空数据的建模与统计推断, 英文In the realm of data science, the modeling and statistical inference of high-dimensional spatiotemporal data present unique challenges and opportunities. This type of data, which encapsulates information across multiple dimensions and over time, offers a rich source of insights but also poses computational and analytical complexities. The key lies in developing effective techniques that can capture the intricate relationships and patterns inherent in these data, while also accounting for their inherent noise and uncertainty.在数据科学领域,高维时空数据的建模与统计推断既带来了独特的挑战,也提供了丰富的机遇。
这类数据涵盖了多个维度和时间的信息,提供了深入洞察的丰富资源,但同时也带来了计算和分析的复杂性。
关键在于开发有效的技术,这些技术既要能够捕捉数据中固有的复杂关系和模式,又要考虑其固有的噪声和不确定性。
To address these challenges, a multifaceted approach is necessary. On the modeling front, techniques such as dimensionality reduction and sparse modeling can help identify the most relevant features and reduce the computational burden. Machine learning algorithms, especially those designed for handling high-dimensional data, can also be leveraged to capture complex relationships and patterns.为了应对这些挑战,需要采取多方面的方法。
100个信息工程专业术语中英文
100个信息工程专业术语中英文全文共3篇示例,供读者参考篇1Information engineering is a vast field that covers a wide range of knowledge and skills. In this article, we will introduce 100 important terms and concepts in information engineering, both in English and Chinese.1. Artificial Intelligence (AI) - 人工智能2. Machine Learning - 机器学习3. Deep Learning - 深度学习4. Natural Language Processing (NLP) - 自然语言处理5. Computer Vision - 计算机视觉6. Data Mining - 数据挖掘7. Big Data - 大数据8. Internet of Things (IoT) - 物联网9. Cloud Computing - 云计算10. Virtual Reality (VR) - 虚拟现实11. Augmented Reality (AR) - 增强现实12. Cybersecurity - 网络安全13. Cryptography - 密码学14. Blockchain - 区块链15. Information System - 信息系统16. Database Management System (DBMS) - 数据库管理系统17. Relational Database - 关系数据库18. NoSQL - 非关系型数据库19. SQL (Structured Query Language) - 结构化查询语言20. Data Warehouse - 数据仓库21. Data Mart - 数据集市22. Data Lake - 数据湖23. Data Modeling - 数据建模24. Data Cleansing - 数据清洗25. Data Visualization - 数据可视化26. Hadoop - 分布式存储和计算框架27. Spark - 大数据处理框架28. Kafka - 流数据处理平台29. Elasticsearch - 开源搜索引擎30. Cyber-Physical System (CPS) - 嵌入式系统31. System Integration - 系统集成32. Network Architecture - 网络架构33. Network Protocol - 网络协议34. TCP/IP - 传输控制协议/互联网协议35. OSI Model - 开放系统互连参考模型36. Router - 路由器37. Switch - 交换机38. Firewall - 防火墙39. Load Balancer - 负载均衡器40. VPN (Virtual Private Network) - 虚拟专用网络41. SDN (Software-Defined Networking) - 软件定义网络42. CDN (Content Delivery Network) - 内容分发网络43. VoIP (Voice over Internet Protocol) - 互联网语音44. Unified Communications - 统一通信45. Mobile Computing - 移动计算46. Mobile Application Development - 移动应用开发47. Responsive Web Design - 响应式网页设计48. UX/UI Design - 用户体验/用户界面设计49. Agile Development - 敏捷开发50. DevOps - 开发与运维51. Continuous Integration/Continuous Deployment (CI/CD) - 持续集成/持续部署52. Software Testing - 软件测试53. Bug Tracking - 缺陷跟踪54. Version Control - 版本控制55. Git - 分布式版本控制系统56. Agile Project Management - 敏捷项目管理57. Scrum - 敏捷开发框架58. Kanban - 看板管理法59. Waterfall Model - 瀑布模型60. Software Development Life Cycle (SDLC) - 软件开发生命周期61. Requirements Engineering - 需求工程62. Software Architecture - 软件架构63. Software Design Patterns - 软件设计模式64. Object-Oriented Programming (OOP) - 面向对象编程65. Functional Programming - 函数式编程66. Procedural Programming - 过程式编程67. Dynamic Programming - 动态规划68. Static Analysis - 静态分析69. Code Refactoring - 代码重构70. Code Review - 代码审查71. Code Optimization - 代码优化72. Software Development Tools - 软件开发工具73. Integrated Development Environment (IDE) - 集成开发环境74. Version Control System - 版本控制系统75. Bug Tracking System - 缺陷跟踪系统76. Code Repository - 代码仓库77. Build Automation - 构建自动化78. Continuous Integration/Continuous Deployment (CI/CD) - 持续集成/持续部署79. Code Coverage - 代码覆盖率80. Code Review - 代码审查81. Software Development Methodologies - 软件开发方法论82. Waterfall Model - 瀑布模型83. Agile Development - 敏捷开发84. Scrum - 看板管理法85. Kanban - 看板管理法86. Lean Development - 精益开发87. Extreme Programming (XP) - 极限编程88. Test-Driven Development (TDD) - 测试驱动开发89. Behavior-Driven Development (BDD) - 行为驱动开发90. Model-Driven Development (MDD) - 模型驱动开发91. Design Patterns - 设计模式92. Creational Patterns - 创建型模式93. Structural Patterns - 结构型模式94. Behavioral Patterns - 行为型模式95. Software Development Lifecycle (SDLC) - 软件开发生命周期96. Requirement Analysis - 需求分析97. System Design - 系统设计98. Implementation - 实施99. Testing - 测试100. Deployment - 部署These terms are just the tip of the iceberg when it comes to information engineering. As technology continues to advance, new terms and concepts will emerge, shaping the future of this dynamic field. Whether you are a student, a professional, or just someone interested in technology, familiarizing yourself with these terms will help you navigate the complex world of information engineering.篇2100 Information Engineering Professional Terms in English1. Algorithm - a set of instructions for solving a problem or performing a task2. Computer Science - the study of computers and their applications3. Data Structures - the way data is organized in a computer system4. Networking - the practice of linking computers together to share resources5. Cybersecurity - measures taken to protect computer systems from unauthorized access or damage6. Software Engineering - the application of engineering principles to software development7. Artificial Intelligence - the simulation of human intelligence by machines8. Machine Learning - a type of artificial intelligence that enables machines to learn from data9. Big Data - large and complex sets of data that require specialized tools to process10. Internet of Things (IoT) - the network of physical devices connected through the internet11. Cloud Computing - the delivery of computing services over the internet12. Virtual Reality - a computer-generated simulation of a real or imagined environment13. Augmented Reality - the integration of digital information with the user's environment14. Data Mining - the process of discovering patterns in large data sets15. Quantum Computing - the use of quantum-mechanical phenomena to perform computation16. Cryptography - the practice of securing communication by encoding it17. Data Analytics - the process of analyzing data to extract meaningful insights18. Information Retrieval - the process of finding relevant information in a large dataset19. Web Development - the process of creating websites and web applications20. Mobile Development - the process of creating mobile applications21. User Experience (UX) - the overall experience of a user interacting with a product22. User Interface (UI) - the visual and interactive aspects of a product that a user interacts with23. Software Architecture - the design and organization of software components24. Systems Analysis - the process of studying a system's requirements to improve its efficiency25. Computer Graphics - the creation of visual content using computer software26. Embedded Systems - systems designed to perform a specific function within a larger system27. Information Security - measures taken to protect information from unauthorized access28. Database Management - the process of organizing and storing data in a database29. Cloud Security - measures taken to protect data stored in cloud computing environments30. Agile Development - a software development methodology that emphasizes collaboration and adaptability31. DevOps - a set of practices that combine software development and IT operations to improve efficiency32. Continuous Integration - the practice of integrating code changes into a shared repository frequently33. Machine Vision - the use of cameras and computers to process visual information34. Predictive Analytics - the use of data and statistical algorithms to predict future outcomes35. Information Systems - the study of how information is used in organizations36. Data Visualization - the representation of data in visual formats to make it easier to understand37. Edge Computing - the practice of processing data closer to its source rather than in a centralized data center38. Natural Language Processing - the ability of computers to understand and generate human language39. Cyber Physical Systems - systems that integrate physical and computational elements40. Computer Vision - the ability of computers to interpret and understand visual information41. Information Architecture - the structural design of information systems42. Information Technology - the use of computer systems to manage and process information43. Computational Thinking - a problem-solving approach that uses computer science concepts44. Embedded Software - software that controls hardware devices in an embedded system45. Data Engineering - the process of collecting, processing, and analyzing data46. Software Development Life Cycle - the process of developing software from conception to deployment47. Internet Security - measures taken to protectinternet-connected systems from cyber threats48. Application Development - the process of creating software applications for specific platforms49. Network Security - measures taken to protect computer networks from unauthorized access50. Artificial Neural Networks - computational models inspired by the biological brain's neural networks51. Systems Engineering - the discipline that focuses on designing and managing complex systems52. Information Management - the process of collecting, storing, and managing information within an organization53. Sensor Networks - networks of sensors that collect and transmit data for monitoring and control purposes54. Data Leakage - the unauthorized transmission of data to an external source55. Software Testing - the process of evaluating software to ensure it meets requirements and functions correctly56. Internet Protocol (IP) - a set of rules for sending data over a network57. Machine Translation - the automated translation of text from one language to another58. Cryptocurrency - a digital or virtual form of currency that uses cryptography for security59. Software Deployment - the process of making software available for use by end-users60. Computer Forensics - the process of analyzing digital evidence for legal or investigative purposes61. Virtual Private Network (VPN) - a secure connection that allows users to access a private network over a public network62. Internet Service Provider (ISP) - a company that provides access to the internet63. Data Center - a facility that houses computing and networking equipment for processing and storing data64. Network Protocol - a set of rules for communication between devices on a network65. Project Management - the practice of planning, organizing, and overseeing a project to achieve its goals66. Data Privacy - measures taken to protect personal data from unauthorized access or disclosure67. Software License - a legal agreement that governs the use of software68. Information Ethics - the study of ethical issues related to the use of information technology69. Search Engine Optimization (SEO) - the process of optimizing websites to rank higher in search engine results70. Internet of Everything (IoE) - the concept of connecting all physical and digital objects to the internet71. Software as a Service (SaaS) - a software delivery model in which applications are hosted by a provider and accessed over the internet72. Data Warehousing - the process of collecting and storing data from various sources for analysis and reporting73. Cloud Storage - the practice of storing data online in remote servers74. Mobile Security - measures taken to protect mobile devices from security threats75. Web Hosting - the service of providing storage space and access for websites on the internet76. Malware - software designed to harm a computer system or its users77. Information Governance - the process of managing information to meet legal, regulatory, and business requirements78. Enterprise Architecture - the practice of aligning an organization's IT infrastructure with its business goals79. Data Backup - the process of making copies of data to protect against loss or corruption80. Data Encryption - the process of converting data into a code to prevent unauthorized access81. Social Engineering - the manipulation of individuals to disclose confidential information82. Internet of Medical Things (IoMT) - the network of medical devices connected through the internet83. Content Management System (CMS) - software used to create and manage digital content84. Blockchain - a decentralized digital ledger used to record transactions85. Open Source - software that is publicly accessible for modification and distribution86. Network Monitoring - the process of monitoring and managing network performance and security87. Data Governance - the process of managing data to ensure its quality, availability, and security88. Software Patch - a piece of code used to fix a software vulnerability or add new features89. Zero-Day Exploit - a security vulnerability that is exploited before the vendor has a chance to patch it90. Data Migration - the process of moving data from one system to another91. Business Intelligence - the use of data analysis tools to gain insights into business operations92. Secure Socket Layer (SSL) - a protocol that encrypts data transmitted over the internet93. Mobile Device Management (MDM) - the practice of managing and securing mobile devices in an organization94. Dark Web - the part of the internet that is not indexed by search engines and often used for illegal activities95. Knowledge Management - the process of capturing, organizing, and sharing knowledge within an organization96. Data Cleansing - the process of detecting and correcting errors in a dataset97. Software Documentation - written information that describes how software works98. Open Data - data that is freely available for anyone to use and redistribute99. Predictive Maintenance - the use of data analytics to predict when equipment will need maintenance100. Software Licensing - the legal terms and conditions that govern the use and distribution of softwareThis list of 100 Information Engineering Professional Terms in English provides a comprehensive overview of key concepts and technologies in the field of information technology. These terms cover a wide range of topics, including computer science, data analysis, network security, and software development. By familiarizing yourself with these terms, you can better understand and communicate about the complex and rapidly evolving world of information engineering.篇3100 Information Engineering Professional Terms1. Algorithm - 算法2. Artificial Intelligence - 人工智能3. Big Data - 大数据4. Cloud Computing - 云计算5. Cryptography - 密码学6. Data Mining - 数据挖掘7. Database - 数据库8. Deep Learning - 深度学习9. Digital Signal Processing - 数字信号处理10. Internet of Things - 物联网11. Machine Learning - 机器学习12. Network Security - 网络安全13. Object-Oriented Programming - 面向对象编程14. Operating System - 操作系统15. Programming Language - 编程语言16. Software Engineering - 软件工程17. Web Development - 网页开发18. Agile Development - 敏捷开发19. Cybersecurity - 网络安全20. Data Analytics - 数据分析21. Network Protocol - 网络协议22. Artificial Neural Network - 人工神经网络23. Cloud Security - 云安全24. Data Visualization - 数据可视化25. Distributed Computing - 分布式计算26. Information Retrieval - 信息检索27. IoT Security - 物联网安全28. Machine Translation - 机器翻译29. Mobile App Development - 移动应用开发30. Software Architecture - 软件架构31. Data Warehousing - 数据仓库32. Network Architecture - 网络架构33. Robotics - 机器人技术34. Virtual Reality - 虚拟现实35. Web Application - 网页应用36. Biometrics - 生物识别技术37. Computer Graphics - 计算机图形学38. Cyber Attack - 网络攻击39. Data Compression - 数据压缩40. Network Management - 网络管理41. Operating System Security - 操作系统安全42. Real-Time Systems - 实时系统43. Social Media Analytics - 社交媒体分析44. Blockchain Technology - 区块链技术45. Computer Vision - 计算机视觉46. Data Integration - 数据集成47. Game Development - 游戏开发48. IoT Devices - 物联网设备49. Multimedia Systems - 多媒体系统50. Software Quality Assurance - 软件质量保证51. Data Science - 数据科学52. Information Security - 信息安全53. Machine Vision - 机器视觉54. Natural Language Processing - 自然语言处理55. Software Testing - 软件测试56. Chatbot - 聊天机器人57. Computer Networks - 计算机网络58. Cyber Defense - 网络防御60. Image Processing - 图像处理61. IoT Sensors - 物联网传感器62. Neural Network - 神经网络63. Network Traffic Analysis - 网络流量分析64. Software Development Life Cycle - 软件开发周期65. Data Governance - 数据治理66. Information Technology - 信息技术67. Malware Analysis - 恶意软件分析68. Online Privacy - 在线隐私69. Speech Recognition - 语音识别70. Cyber Forensics - 网络取证71. Data Anonymization - 数据匿名化72. IoT Platform - 物联网平台73. Network Infrastructure - 网络基础设施74. Predictive Analytics - 预测分析75. Software Development Tools - 软件开发工具77. Information Security Management - 信息安全管理78. Network Monitoring - 网络监控79. Software Deployment - 软件部署80. Data Encryption - 数据加密81. IoT Gateway - 物联网网关82. Network Topology - 网络拓扑结构83. Quantum Computing - 量子计算84. Software Configuration Management - 软件配置管理85. Data Lakes - 数据湖86. Infrastructure as a Service (IaaS) - 基础设施即服务87. Network Virtualization - 网络虚拟化88. Robotic Process Automation - 机器人流程自动化89. Software as a Service (SaaS) - 软件即服务90. Data Governance - 数据治理91. Information Security Policy - 信息安全政策92. Network Security Risk Assessment - 网络安全风险评估93. Secure Software Development - 安全软件开发94. Internet Security - 互联网安全95. Secure Coding Practices - 安全编码实践96. Secure Network Design - 安全网络设计97. Software Security Testing - 软件安全测试98. IoT Security Standards - 物联网安全标准99. Network Security Monitoring - 网络安全监控100. Vulnerability Management - 漏洞管理These terms cover a wide range of topics within the field of Information Engineering, and are essential in understanding and discussing the various aspects of this discipline. It is important for professionals in this field to be familiar with these terms in order to effectively communicate and collaborate with others in the industry.。
Imperva 应用安全产品说明说明书
Web Application
Firewall
DDoS Protection
Load Balancer
Content Delivery Network
Imperva Application Security covers the full range of web attacks.
• Secures websites against attack—on-prem and in the cloud
Load balancer Imperva offers a cloud-based load balancer which supports local and global server load balancing across on-premises and public cloud data centers. It supports automatic failover to standby servers to enable high-availability and disaster recovery without any TTL-related (Time to Live) delays.
计算机科学与技术专业外文翻译--数据库
外文原文:Database1.1Database conceptThe database concept has evolved since the 1960s to ease increasing difficulties in designing, building, and maintaining complex information systems (typically with many concurrent end-users, and with a large amount of diverse data). It has evolved together with database management systems which enable the effective handling of databases. Though the terms database and DBMS define different entities, they are inseparable: a database's properties are determined by its supporting DBMS and vice-versa. The Oxford English dictionary cites[citation needed] a 1962 technical report as the first to use the term "data-base." With the progress in technology in the areas of processors, computer memory, computer storage and computer networks, the sizes, capabilities, and performance of databases and their respective DBMSs have grown in orders of magnitudes. For decades it has been unlikely that a complex information system can be built effectively without a proper database supported by a DBMS. The utilization of databases is now spread to such a wide degree that virtually every technology and product relies on databases and DBMSs for its development and commercialization, or even may have such embedded in it. Also, organizations and companies, from small to large, heavily depend on databases for their operations.No widely accepted exact definition exists for DBMS. However, a system needs to provide considerable functionality to qualify as a DBMS. Accordingly its supported data collection needs to meet respective usability requirements (broadly defined by the requirements below) to qualify as a database. Thus, a database and its supporting DBMS are defined here by a set of general requirements listed below. Virtually all existing mature DBMS products meet these requirements to a great extent, while less mature either meet them or converge to meet them.1.2Evolution of database and DBMS technologyThe introduction of the term database coincided with the availability of direct-access storage (disks and drums) from the mid-1960s onwards. The term represented a contrast with the tape-based systems of the past, allowing shared interactive use rather than daily batch processing.In the earliest database systems, efficiency was perhaps the primary concern, but it was already recognized that there were other important objectives. One of the key aims was to make the data independent of the logic of application programs, so that the same data could be made available to different applications.The first generation of database systems were navigational,[2] applications typically accessed data by following pointers from one record to another. The two main data models at this time were the hierarchical model, epitomized by IBM's IMS system, and the Codasyl model (Network model), implemented in a number ofproducts such as IDMS.The Relational model, first proposed in 1970 by Edgar F. Codd, departed from this tradition by insisting that applications should search for data by content, rather than by following links. This was considered necessary to allow the content of the database to evolve without constant rewriting of applications. Relational systems placed heavy demands on processing resources, and it was not until the mid 1980s that computing hardware became powerful enough to allow them to be widely deployed. By the early 1990s, however, relational systems were dominant for all large-scale data processing applications, and they remain dominant today (2012) except in niche areas. The dominant database language is the standard SQL for the Relational model, which has influenced database languages also for other data models.Because the relational model emphasizes search rather than navigation, it does not make relationships between different entities explicit in the form of pointers, but represents them rather using primary keys and foreign keys. While this is a good basis for a query language, it is less well suited as a modeling language. For this reason a different model, the Entity-relationship model which emerged shortly later (1976), gained popularity for database design.In the period since the 1970s database technology has kept pace with the increasing resources becoming available from the computing platform: notably the rapid increase in the capacity and speed (and reduction in price) of disk storage, and the increasing capacity of main memory. This has enabled ever larger databases and higher throughputs to be achieved.The rigidity of the relational model, in which all data is held in tables with a fixed structure of rows and columns, has increasingly been seen as a limitation when handling information that is richer or more varied in structure than the traditional 'ledger-book' data of corporate information systems: for example, document databases, engineering databases, multimedia databases, or databases used in the molecular sciences. Various attempts have been made to address this problem, many of them gathering under banners such as post-relational or NoSQL. Two developments of note are the Object database and the XML database. The vendors of relational databases have fought off competition from these newer models by extending the capabilities of their own products to support a wider variety of data types.1.3General-purpose DBMSA DBMS has evolved into a complex software system and its development typically requires thousands of person-years of development effort.[citation needed] Some general-purpose DBMSs, like Oracle, Microsoft SQL Server, and IBM DB2, have been undergoing upgrades for thirty years or more. General-purpose DBMSs aim to satisfy as many applications as possible, which typically makes them even more complex than special-purpose databases. However, the fact that they can be used "off the shelf", as well as their amortized cost over many applications and instances, makes them an attractive alternative (Vsone-time development) whenever they meet an application's requirements.Though attractive in many cases, a general-purpose DBMS is not always the optimal solution: When certain applications are pervasive with many operating instances, each with many users, a general-purpose DBMS may introduce unnecessary overhead and too large "footprint" (too large amount of unnecessary, unutilized software code). Such applications usually justify dedicated development.Typical examples are email systems, though they need to possess certain DBMS properties: email systems are built in a way that optimizes email messages handling and managing, and do not need significant portions of a general-purpose DBMS functionality.1.4Database machines and appliancesIn the 1970s and 1980s attempts were made to build database systems with integrated hardware and software. The underlying philosophy was that such integration would provide higher performance at lower cost. Examples were IBM System/38, the early offering of Teradata, and the Britton Lee, Inc. database machine. Another approach to hardware support for database management was ICL's CAFS accelerator, a hardware disk controller with programmable search capabilities. In the long term these efforts were generally unsuccessful because specialized database machines could not keep pace with the rapid development and progress of general-purpose computers. Thus most database systems nowadays are software systems running on general-purpose hardware, using general-purpose computer data storage. However this idea is still pursued for certain applications by some companies like Netezza and Oracle (Exadata).1.5Database researchDatabase research has been an active and diverse area, with many specializations, carried out since the early days of dealing with the database concept in the 1960s. It has strong ties with database technology and DBMS products. Database research has taken place at research and development groups of companies (e.g., notably at IBM Research, who contributed technologies and ideas virtually to any DBMS existing today), research institutes, and Academia. Research has been done both through Theory and Prototypes. The interaction between research and database related product development has been very productive to the database area, and many related key concepts and technologies emerged from it. Notable are the Relational and the Entity-relationship models, the Atomic transaction concept and related Concurrency control techniques, Query languages and Query optimization methods, RAID, and more. Research has provided deep insight to virtually all aspects of databases, though not always has been pragmatic, effective (and cannot and should not always be: research is exploratory in nature, and not always leads to accepted or useful ideas). Ultimately market forces and real needs determine the selection of problem solutions and related technologies, also among those proposed by research. However, occasionally, not the best and most elegant solution wins (e.g., SQL). Along their history DBMSs and respective databases, to a great extent, have been the outcome of such research, while real product requirements and challenges triggered database research directions and sub-areas.The database research area has several notable dedicated academic journals (e.g., ACM Transactions on Database Systems-TODS, Data and Knowledge Engineering-DKE, and more) and annual conferences (e.g., ACM SIGMOD, ACM PODS, VLDB, IEEE ICDE, and more), as well as an active and quite heterogeneous (subject-wise) research community all over the world.1.6Database architectureDatabase architecture (to be distinguished from DBMS architecture; see below) may be viewed, to some extent, as an extension of Data modeling. It is used to conveniently answer requirements of different end-users from a same database, as well as for other benefits. For example, a financial department of a company needs the payment details of all employees as part of the company's expenses, but not other many details about employees, that are the interest of the human resources department. Thus different departments need different views of the company's database, that both include the employees' payments, possibly in a different level of detail (and presented in different visual forms). To meet such requirement effectively database architecture consists of three levels: external, conceptual and internal. Clearly separating the three levels was a major feature of the relational database model implementations that dominate 21st century databases.[13]The external level defines how each end-user type understands the organization of its respective relevant data in the database, i.e., the different needed end-user views.A single database can have any number of views at the external level.The conceptual level unifies the various external views into a coherent whole, global view.[13] It provides the common-denominator of all the external views. It comprises all the end-user needed generic data, i.e., all the data from which any view may be derived/computed. It is provided in the simplest possible way of such generic data, and comprises the back-bone of the database. It is out of the scope of the various database end-users, and serves database application developers and defined by database administrators that build the database.The Internal level (or Physical level) is as a matter of fact part of the database implementation inside a DBMS (see Implementation section below). It is concerned with cost, performance, scalability and other operational matters. It deals with storage layout of the conceptual level, provides supporting storage-structures like indexes, to enhance performance, and occasionally stores data of individual views (materialized views), computed from generic data, if performance justification exists for such redundancy. It balances all the external views' performance requirements, possibly conflicting, in attempt to optimize the overall database usage by all its end-uses according to the database goals and priorities.All the three levels are maintained and updated according to changing needs by database administrators who often also participate in the database design.The above three-level database architecture also relates to and being motivated by the concept of data independence which has been described for long time as a desired database property and was one of the major initial driving forces of the Relational model. In the context of the above architecture it means that changes made at a certain level do not affect definitions and software developed with higher level interfaces, and are being incorporated at the higher level automatically. For example, changes in the internal level do not affect application programs written using conceptual level interfaces, which saves substantial change work that would be needed otherwise.In summary, the conceptual is a level of indirection between internal and external. On one hand it provides a common view of the database, independent of different external view structures, and on the other hand it is uncomplicated by details of how the data is stored or managed (internal level). In principle every level, and even every external view, can be presented by a different data model. In practice usually a given DBMS uses the same data model for both the external and the conceptual levels (e.g., relational model). The internal level, which is hidden inside the DBMS and depends on its implementation (see Implementation section below), requires a different levelof detail and uses its own data structure types, typically different in nature from the structures of the external and conceptual levels which are exposed to DBMS users (e.g., the data models above): While the external and conceptual levels are focused on and serve DBMS users, the concern of the internal level is effective implementation details.中文译文:数据库1.1 数据库的概念数据库的概念已经演变自1960年以来,以缓解日益困难,在设计,建设,维护复杂的信息系统(通常与许多并发的最终用户,并用大量不同的数据)。
人工智能与环境科学:智能环境监测与保护
人工智能与环境科学:智能环境监测与保护In the realm of environmental science, the fusion of artificial intelligence (AI) with monitoring and protection systems marks a pivotal advancement towards sustainable practices. AI-driven technologies have revolutionized environmental monitoring by enhancing precision, efficiency, and proactive measures in safeguarding our ecosystems.One of the primary applications of AI in environmental science is intelligent monitoring systems. Traditional methods often struggle with real-time data processing and comprehensive coverage. AI, however, excels in analyzing vast amounts of data from various sources simultaneously. For instance, sensors embedded in ecosystems can continuously gather data on air quality, water levels, and biodiversity. AI algorithms process this data swiftly, detecting patterns and anomalies that human analysts might overlook. This capability allows for early detection of environmental hazards, such as pollutants or habitat disturbances, enabling prompt intervention.Moreover, AI contributes significantly to predictive modeling in environmental conservation. By analyzing historical data alongside current trends, AI can forecast environmental changes and their impacts. This predictive capability aids in planning conservation strategies, such as habitat restoration or species preservation efforts. It also assists in managing natural resources sustainably, optimizing usage based on predicted demand and environmental conditions. Furthermore, AI plays a crucial role in adaptive management practices. Environmental conditions are dynamic and often unpredictable. AI-powered systems can continuously adapt to changing circumstances by learning from new data inputs. This adaptability ensures that conservation efforts remain effective over time, adjusting strategies in response to evolving environmental challenges. In addition to monitoring and prediction, AI enhances the efficiency of environmental protection measures. Automated systems powered by AI can autonomously control variables in industrial processes to minimize environmental footprint. For example, AI can optimize energy consumption in manufacturing or regulate emissions from vehicles based on real-timeenvironmental conditions. Such applications not only reduce ecological impact but also contribute to achieving sustainability goals more effectively.In conclusion, the integration of artificial intelligence with environmental science represents a paradigm shift in how we perceive and protect our natural world. By leveraging AI's capabilities in monitoring, prediction, and adaptive management, we can forge a path towards sustainable development and ecological resilience. As technology continues to advance, so too does our ability to safeguard the environment for future generations.。
dbt Cloud Administrator Certification Exam Study G
dbt Cloud Administrator Certification Exam Study GuideHow to use this study guideThe sample exam questions provide examples of the format to expect for the exam. The types of questions you can expect on the exam include:Multiple-choice Fill-in-the-blank MatchingHotspot Build listDiscrete Option Multiple Choice (DOMC)The topic outline will provide a clear list of the topics that are assessed on the exam. dbt subject matter experts used this topic outline to write and review all of the exam items that you will find on the exam.The exam overview will provide high-level details on the format of the exam. We recommend being mindful of the number of questions and time constraints.This is the official study guide for the dbt Cloud Administrator Certification Exam from the team at dbt Labs. While the guide suggests a sequence of courses and reading material, we recommend using it to supplement (rather than substitute) real-world use and experience with dbt.The learnin g pat h will walk you through a suggested series of courses, readings, and documentation. We will also provide some guidance for the types of experience to build while using dbt on a real-world project .Finally, the additional resources section will provide additional places to continue your learning .We put a ton of effort and attention to detail and the exam and we wish you much success in your pursuit of a dbt Labs certification.Exam OverviewLogisticsScoringThe dbt Cloud Administrator Certification Exam is designed to evaluate your ability to The exam is scored on a point basis, with 1 point for each correct answer, and 0 points for incorrect answers. All questions are weighted equally.An undisclosed number of unscored questions will be included on each exam. These are unmarked and indistinguishable from scored questions. Answers to these questions will be used for research purposes only, and will not count towards the score.Initialize the setup of a dbt Cloud account including connecting to data platforms, git providers and configuring security and access controlconfigure environments, jobs, logging, and alerting with best practice Maximize the value your team gets out of dbt Clouconfigure, troubleshoot and optimize projects, manage dbt Cloud connections and environmentmaximize value while enforcing best practicesDuration: 2 HourFormat & Registration: online proctored or on-site at Coalesc Length: 65 questionPassing Score: 63% or higher. You will know your result immediately after completion of the exam. Price: $200Language: Only English at this timCertification Expiration: The certification expires 2 years after the date awarded. *Discounts are available for dbt Labs SI Partners.We recommend that folks have at least familiarity with SQL, data platforms, and git for version control and have had 6+ months of experience administering an instance of dbt Cloud before attempting the exam.Retakes & CancellationsIf you do not pass the exam, you may schedule a retake 48 hours after your lastattempt. You will need to pay a registration fee for each retake. You can reschedule or cancel without penalty on MonitorEDU before a scheduled exam. We will not issue refunds for no-shows.Topic OutlineThe dbt Cloud Administrator Certification Exam has been designed to assess the following topics and sub-topics.AccommodationsPlease contact MonitorEDU with any accommodation requests.Topic 3:Configuring dbt Cloud security and licenseCreating Service tokens for API accesAssigning permission set Creating license mappingUnderstanding 3-pronged access control (RBAC in dbt, warehouse, git Adding and removing userAdding SSO application for dbt Cloud enterpriseTopic 2:Configuring git connectionConnecting the git repo to dbUnderstanding custom branches and which to configure to environment Creating a PR templatUnderstanding version control basic Setting up integrations with git providersTopic 1:Configuring dbt Cloud data warehouse connectionUnderstanding how to connect the warehousConfiguring IP whitelis Selecting adapter typ Configuring OAutAdding credentials to deployment environments to access warehouse for production / CI runsTopic 6: Setting up monitoringand alerting for job Setting up email notification Setting up Slack notification Using Webhooks for event-driven integrations with other systemsTopic 5: Creating and maintaining job definition Setup a CI job with deferraUnderstanding steps within a dbt jo Scheduling a job to run on schedulImplementing run commands in the correct ordeCreating new dbt Cloud joConfiguring optional settings such as environment variable overrides, threads, deferral, target name, dbt version override etcGenerating documentation on a job that populates the project’s doc siteTopic 4:Creating and maintaining dbt Cloud environmentUnderstanding access control to different environmentDetermining when to use a service accounRotating key pair authentication via the APUnderstanding environment variableUpgrading dbt versionDeploying using a custom branc Creating new dbt Cloud deployment environmenSetting default schema / dataset for environmentTopic 7:Monitoring Command InvocationUnderstanding events in audit lo Understanding how to audit a DAG and use artifactUsing the model timing taReviewing job logs to find errorsTopic Outline(Continued)Sample Question 1:Explanation:Each package has a ‘dbt version required’ interval. When you upgrade your dbt Cloud version in your project, you need to check the required version for your installed packages to ensure the updated dbt version falls within the interval. This makes You need to look for dbt version requirements on packages the project has installed the correct answer.Explanation:Custom cron schedule matches with A daily production data refresh that runs every other hour, Monday through Friday. Recurring jobs that run on a schedule are defined in the job setting triggers either by a custom cron schedule or day/time selection in the UI.Continuous integration run on pull requests matches with A job to test code changes before they are merged with the main branch. Continuous integration jobs are set up to trigger when a pull request is created. The PR workflow occurs when code changes are made and a PR is created in the UI. This kicks off a job that run your project to ensure a successful run prior to merging to the main branch.No trigger matches with Ad hoc requests to fully refresh incremental models one to two times per month- run job manually. Ad hoc requests, by definition, are one-off run that are not scheduled jobs and therefore are kicked off manually in the UI.dbt Cloud Administrator API matches with A near real-time update that needs to run immediately after an Airflow task loads the data. An action outside of dbt Cloud triggering a job has to be configured using the dbt Cloud Administratoristrative API.Sample Question 2:Explanation:dbt has two types of tokens, service account and user. User tokens are issued to users with a developer license. This token runs on behalf of the user. Service account tokens runindependently from a specific user. This makes Service account tokens are used for system-level integrations that do not run on behalf of any one user the correct answer.Sample Question 3:Explanation:Metadata only service tokens can authorize requests to the metadata API.Read-only service tokens can authorize requests for viewing a read-only dashboard, viewing generated documentation, and viewing source freshness reports.Analysts can use the IDE, configure personal developer credentials, view connections, view environments, view job definitions, and view historical runs.Job Viewers can view environments, view job definitions, and view historical runs.Sample Question 4:Explanation:dbt Cloud supports JIT (Just-in-Time) provisioning and IdP-initiated login.Sample Question 5:Learning PathThis is not the only way to prepare for the exam, but just one recommended path forsomeone new to dbt to prepare for the exam. Each checkpoint provides a logical setof courses to complete, readings and documentation to digest, and real-worldexperience to seek out. We recommend this order, but things can be reorganized based on your learning preferences.Checkpoint 0: Prerequisites Checkpoint 1: Build a FoundatioCourses:dbt FundamentalsReadingsdbt viewpoinBlog: Data transformation process: 6 steps in an ELT workfloBlog: 4 Data Modeling Techniques for Modern WarehouseBlog: Creating a data quality framework for scalBlog: The next big step forwards for analytics engineerinDocumentationdbt Cloud featuresVersion control basicExperiencCreating a dbt project from scratch to deploymenDebugging errorCommanddbt compildbt rudbt source freshnesdbt tesdbt docs generatdbt buildFor git, the exam expects familiarity with branching strategies (including development vs main branches), basic git commands, and pull/merge requests.For SQ L, the exam expects familiarity with j oins, aggregations, common table expressions (C T Es), and w indo w f unctions.dbt is a tool that brings together severaldi ff erent technical skills in one place.We recommend starting this path a ft eryou 've developed foundational git andSQ L skills.Checkpoint 2: Configuring data warehouse and git connectionsResources:Coursesdbt Cloud and BigQuery for Administratordbt Cloud and Databricks for Administratordbt Cloud and Snowflake for AdministratorGitHub SkillLinkedIn Learning: Getting started with git and githuGitLab LearAzure DevOps TutoriaReadingsWhat is a data platform - SnowflakWhat is a data warehouse - AWAccelerators for Cloud Data Platform Transition GuidHow we configure SnowflakSuccess Story: Aktify Democratizes Data Access with Databricks Lakehouse Platform and dbUnblocking IPs in 2023: Everything you need to knoWhat is OauthVersion control with GiGit for the rest of us workshoThe exact GitHub pull request template we use at dbt LabsHow to review an analytics pull requesDocumentationSupported data platformWhat are adapters? Why do we need themAdapter specific configurationNew adapter information sheeQuickstart for dbt Cloud and BigQuerQuickstarts for dbt Cloud and DatabrickQuickstart for dbt Cloud and SnowflakSnowflake PermissionQuickstart for dbt Cloud and RedshiStarburst Galaxy Quickstartdbt Cloud Regions & IP addresseOauth with data platformAbout user access in dbt Clouddbt Cloud tenancdbt Cloud regions & IP addresseCreate a deployment environment / deployment connectionConfigure GitHub for dbt ClouConfigure GitLab for dbt ClouConnect to Azure DevOpHow do I use custom branch settings in a dbt Cloud environmentExperienceConfiguring a data platform for dbt ClouAdding users to a data platform, managing permissions, data objects, service account Connecting a data platform to dbt Cloud, initializing a project and building a mode Unblocking IPs for dbt ClouCreating a security integration in a data platform to manage an Oauth connectioConfiguring SSO for a dbt Cloud Enterprise plaAdding credentials to deployment environments to access warehouse for CI runInstalling dbt Cloud in a GitHub repo and connecting to dbtInstalling dbt Cloud in a GitLab repo and connecting to dbtInstalling dbt Cloud in Azure Devops and connecting to dbCreating a pull request template for your organizatioCreating pull requestsReviewing pull requestReviewing, managing, merging changeOnboarding new users to dbt Cloud project repos in GitHub, GitLab, AzureDevOpsUsing custom branches in dbt environmentsCheckpoint 3: Configuring dbt Cloud security and licenses Resources:Readingsdbt Cloud Security protocols and recommendationWhat is SSO and how does it workDocumentationSingle Sign On in dbt Cloud for the EnterprisExperienceLimiting dbt Cloud’s access to your warehouse to strictly the datasets processed by db Using SSL or SSH encryption to protect your data and credentialsChoosing strong passwords for your database usersCheckpoint 4: Configuring and maintaining dbt Cloud environmentsResources:CoursesAdvanced DeploymenReadingsdbt Cloud environmentdbt Cloud environment best practices guidDocumentationTypes of environmentCreate a development environmenCreate a deployment environmenHow to use custom branch settingDelete a job or environment in dbt ClouSet environment variables in dbt ClouUse environment variables in JinjAbout service account tokenExperienceDefining environments in your data platforDefining environments in dbt ClouUsing custom branches in a dbt Cloud environmenUsing environment variablesCheckpoint 5: Creating and maintaining job definitions Resources:DocumentationCreate and schedule jobDeploy dbt cloud jobJob scheduler featureCreate artifactJob commandJob creation best practices DiscoursJob triggerConfiguring continuous integration in dbt CLouConfiguring a Slim CI joCloud CI deferral and state comparisonExperienceCreating a new joSetup a CI job with deferraUnderstanding steps within a dbt joScheduling a job to run on schedulImplementing run commands in the correct ordeConfiguring optional settings such as environment variable overrides, threads, deferral, target name, dbt version override, etcGenerating documentation on a job that populates the project’s doc siteCheckpoint 6: Setting up monitoring and alerting Resources:Documentationdbt cloud job notificationSet up email and Slack notifications for jobsWebhooks for your jobExperienceSetting up dbt Cloud job notificationSetting up email notifications for jobSetting up Slack notifications for jobSetting up webhooksCheckpoint 7: Monitoring command invocations Resources:DocumentationEvents in the dbt Cloud audit logExporting logSearching the audit logModel timindbt Guide: Best practices for debugging errorUnpacking relationships and data lineagExperienceFinding and reviewing events in audit loReviewing job logs to find errorAuditing a DAG and using artifactUsing the model timing tabAdditional Resourcesdbt Slac#dbt-certificatio#learn-on-deman#advice-dbt-for-beginner#advice-dbt-for-power-user#dbt-deployment-and-orchestrationIf you are a dbt Labs partner or enterprise client, contact your partner manager or account team for additional benefits.Prefer E-mail? Contact us:*************************。
present, and future
1 Motivation
Since its rst introduction in 1996, XML has been steadily progressing as the \format of choice" for data that has mostly textual content. Starting from nancial data, business transactions to data obtained from satellites - most of the data in today's web-driven world are being converted to XML. The two leading web application development platforms (.NET 9] and J2EE 16]) use XML web services - a standard mechanism for communication between applications. Given its huge success in the data and application domains, it is somewhat puzzling to see so little has been done in terms of conceptual and formal design areas involving XML. Literature shows di erent areas of application of design principles that apply to XML. Since XML has been around for a while, and only recently there has been an e ort towards formalizing and conceptualizing the model behind XML, such modeling techniques are still playing \catch-up" with the XML standard. The World-Wide-Web Consortium (W3C) has started an e ort towards a formal model for XML, which is termed DOM (Document Object Model) - a graph-based formal 1
模拟ai英文面试题目及答案
模拟ai英文面试题目及答案模拟AI英文面试题目及答案1. 题目: What is the difference between a neural network anda deep learning model?答案: A neural network is a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. A deep learning model is a neural network with multiple layers, allowing it to learn more complex patterns and features from data.2. 题目: Explain the concept of 'overfitting' in machine learning.答案: Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, resulting in poor generalization to new, unseen data.3. 题目: What is the role of a 'bias' in an AI model?答案: Bias in an AI model refers to the systematic errors introduced by the model during the learning process. It can be due to the choice of model, the training data, or the algorithm's assumptions, and it can lead to unfair or inaccurate predictions.4. 题目: Describe the importance of data preprocessing in AI.答案: Data preprocessing is crucial in AI as it involves cleaning, transforming, and reducing the data to a suitableformat for the model to learn effectively. Proper preprocessing can significantly improve the performance of AI models by ensuring that the input data is relevant, accurate, and free from noise.5. 题目: How does reinforcement learning differ from supervised learning?答案: Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a reward signal. It differs from supervised learning, where the model learns from labeled data to predict outcomes based on input features.6. 题目: What is the purpose of a 'convolutional neural network' (CNN)?答案: A convolutional neural network (CNN) is a type of deep learning model that is particularly effective for processing data with a grid-like topology, such as images. CNNs use convolutional layers to automatically and adaptively learn spatial hierarchies of features from input images.7. 题目: Explain the concept of 'feature extraction' in AI.答案: Feature extraction in AI is the process of identifying and extracting relevant pieces of information from the raw data. It is a crucial step in many machine learning algorithms, as it helps to reduce the dimensionality of the data and to focus on the most informative aspects that can be used to make predictions or classifications.8. 题目: What is the significance of 'gradient descent' in training AI models?答案: Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In the context of AI, it is used to minimize the loss function of a model, thus refining the model's parameters to improve its accuracy.9. 题目: How does 'transfer learning' work in AI?答案: Transfer learning is a technique where a pre-trained model is used as the starting point for learning a new task. It leverages the knowledge gained from one problem to improve performance on a different but related problem, reducing the need for large amounts of labeled data and computational resources.10. 题目: What is the role of 'regularization' in preventing overfitting?答案: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function, which discourages overly complex models. It helps to control the model's capacity, forcing it to generalize better to new data by not fitting too closely to the training data.。
美国留学数据科学(DS)专业介绍和文书写作
数据科学在商科应用 – Finance (Cont.)
Part II:Consumer Retail Finance • Default prediction, e.g., anti money laundering, credit card fraud, insider trading (Credit Division) • Forecast expected loss / Risk management of assets (Risk Division) • Customer profiling, credit scoring, acquisition, retention, personalized services (Marketing Division)
目录页
• 数据科学专业概论 • 数据科学在商科的应用领域 • Finance / Marketing / Supply Chain / HR • 数据科学核心技能 • 综述 • 技术技能 (Math & Statistics / Programming / Database / Machine Learning / Data Mining / Data Cleaning / Visualization / 数学建模) • Soft Skills (Ethics / Problem Solving / Communication / Critical Thinking / Ask right questions / 学习能力) • 申请材料包装技巧 • Coursework / Course Projects / Research Experience / Internship
数据科学在商科应用 – Marketing (Ch. Companies can conduct qualitative and quantitative market research much more quickly and inexpensively than ever before. Online survey tools mean that focus groups and customer feedback are easy and inexpensive to implement, and data analytics make the results easier to parse and take action on. Reputation management. With big data, companies can easily monitor mentions of their brand across many different websites and social channels to find unfiltered opinions, reviews, and testimonials about their organization and products. The savviest can also use social media to provide customer service and create a trustworthy brand presence. Competitor analysis. New social monitoring tools make it easy to collect and analyze data about competitors and their marketing efforts as well. The companies that can use this information will have a distinct competitive advantage.
介绍一个软件英语作文
介绍一个软件英语作文English Answer:Introduction:In the vast and ever-evolving digital landscape, software applications have become indispensable tools that empower individuals, businesses, and organizations to achieve their goals. From communication and productivity to entertainment and education, software has permeated every aspect of our lives. In this comprehensive guide, we will delve into the multifaceted world of software, exploringits history, types, applications, benefits, and future trends.History of Software:The roots of software can be traced back to the early days of computing. In the 1940s and 1950s, software was primarily developed by scientists and engineers who usedassembly language to write programs for specific tasks. The introduction of high-level programming languages in the 1960s, such as FORTRAN and COBOL, made software development more accessible and paved the way for the rapid growth of the software industry.Types of Software:Software can be broadly classified into three main categories:System Software: This type of software acts as the foundation for a computer system. It includes the operating system, device drivers, and other core components that manage the hardware and provide a platform for running other applications.Application Software: This category encompasses software designed for specific tasks or functions. Examples include word processors, spreadsheets, web browsers, and media players.Middleware: Middleware serves as a bridge between system software and application software. It provides services that facilitate communication and data exchange between different software components.Applications of Software:Software finds applications in a wide range of domains:Business: Software is essential for managing business operations, including accounting, inventory management, customer relationship management, and project planning.Education: Software plays a vital role in education, providing interactive learning environments, simulations, and assessment tools.Healthcare: Software is used for patient record management, medical diagnosis, and remote monitoring of health conditions.Entertainment: Software powers video games, musicstreaming services, and social media platforms, providing entertainment and connecting people.Science and Research: Software is essential for scientific simulations, data analysis, and modeling complex systems.Benefits of Software:Increased Efficiency: Software automates tasks, reduces errors, and streamlines processes, leading to significant efficiency gains.Improved Decision-Making: Software provides access to timely and accurate data, enabling informed decision-making based on real-time information.Enhanced Productivity: Software allows users to accomplish more in less time, freeing up their time for other tasks.Better Communication: Software facilitates real-timecommunication, collaboration, and information sharing within teams and organizations.Personalized Experiences: Software can be tailored to individual needs, providing personalized experiences and recommendations.Future Trends in Software:As technology continues to evolve, software will continue to play a pivotal role:Artificial Intelligence (AI) Integration: Software will increasingly incorporate AI capabilities for enhanced automation, predictive analytics, and personalized experiences.Cloud Computing: The shift towards cloud computing will make software more accessible, scalable, and cost-effective.Edge Computing: Edge computing will bring softwarecloser to the end-user, reducing latency and improvingreal-time performance.Quantum Computing: Quantum computing has the potential to revolutionize software development by enabling the creation of algorithms that are exponentially faster than traditional methods.Cybersecurity: Software security will become a top priority as increased connectivity and data sharing pose new threats.Conclusion:Software has transformed our world in countless ways, empowering us to connect, learn, create, and innovate. As technology continues to advance, software will continue to evolve, promising even greater possibilities and benefitsin the years to come.Chinese Answer:引言:在广阔且不断发展的数字领域中,软件应用程序已成为不可或缺的工具,使个人、企业和组织能够实现其目标。
计算机专业毕业设计英文翻译7
clicking on hot spots, it can show the hot spot's specific information. One can also type into the query information based on his need, and get some relevant information. In addition, one can choose to check the three dimensional maps and satellite maps through clicking the mouse. Major functions: User information management: Check the user name and password, set level certification depending on the permissions, allow users of different permissions to login the system via the Internet. The inquiry of Location information: System can provide users with fuzzy inquires and quick location. Map management: Implement loading maps, map inquires, layer management, and other common operations such as distance measurement, and maps zoom, eagle eye, labels, printing, and more. Roam the map: Use the up and down keys to roam any area of the map, or drag-and-drop directly.
Cloudflare用户指南说明书
User GuideCloudflare speeds up and protects over 4,000,000 websites, APIs, and SaaS services. Our Anycast technology enables our performance, security, reliability, and analytics offerings to scale with every server we add to our growing collection of data centers.Cloudflare dramatically improves website performance through our global CDN and web optimization features. Cloudflare’s WAF, DDoS, and SSL protect website owners and their visitors from all types of online threats. CloudFlare’s network helps identify visitor and bot behavior that isn’t accessible to conventional analytics technologies.Cloudflare's free extension for Magento 2 offers all of the performance and security benefits of Cloudflare for your online store, with a one-click application of settings specifically developed for Magento 2, Magento-specific web application firewall rules, and integration with Magento's cache management. (A free Cloudflare account is required to use the extension, additional features are available with higher tier plans, starting at $20 / month. Details can be found on Cloudflare's p lans page).A slow loading page is the number one reason customers bounce from eCommerce sites before converting. Cloudflare's extension caches static assets at the edge of its global network of 90+ data centers, close to visitors, while Railgun™ technology accelerates delivery of dynamic content. Cloudflare's security solutions include DDoS protection to keep your sites from going down in an attack, allowing customers to always reach your site.In addition, Cloudflare's TLS/SSL encryption ensures that sensitive data is kept secure while our web application firewall helps meet PCI compliance.Extension featuresCloudflare extension for Magento 2 offers all of the performance and security benefits of Cloudflare for your online store, with a one-click installation of settings specifically developed for the Magento 2 eCommerce platform.One-Click Application of Cloudflare SettingsOne-click setup of recommended settings is the easiest way for customers to optimize and secure their Magento 2 online store.Web application firewall (WAF) rulesetsCloudflare’s web application firewall (WAF), available in our paid plans, has built-in rulesets including rules that mitigate attacks specifically targeted towards Magento stores.Integration with Magento Cache ManagementBy integrating with Magento’s cache management, Cloudflare's extension for Magento 2 automatically clears the Cloudflare cache whenever Magento 2 clears the application cache (for example when a product category is updated), ensuring your visitors receive the most up-to-date content.Restoring the Original Visitor IPLogging your visitors' IP addresses is an important part of being able to track and analyze shopping behavior. Cloudflare's extension for Magento 2 returns a header with the visitor's real IP address, instead of Cloudflare's IP address.User GuideA user guide for the Cloudflare Magento2 plugin can be found h ere.Installation InstructionsTo install the Cloudflare Magento2 plugin run the following commands from your magento root:poser require cloudflare/cloudflare-magentoposer update3.bin/magento setup:upgrade4.bin/magento setup:di:compile。
泛在网(Ambient_Network)
Talking
泛在网的目标
智慧地球
泛在网是 泛在社会、智慧地球的 基础设施
Ubiquitous Connectivity Pervasive Reality Ambient Intelligence
ITU-T Y.2002
Instrumented
Interconnected
USN安全体系架构
SG2 SG11 SG13 SG16 SG17
需求 体系架构 应用 安全 编号\命名 和寻址
泛在网络的概念—ITU-T Y.2002
Ubiquitous Network The ability for persons and/or devices to access services and communicate while minimizing technical restrictions regarding where, when and how these services are accessed, in the context of the service(s) subscribed to.
泛 在 网
(Ambient Network)
主要内容
1
• 泛在网的概念、特征和架构体系
• 泛在网的发展现状 • 泛在网的发展趋势
2 3
主要内容
1
• 泛在网的概念、特征和架构体系
• 泛在网的发展现状 • 泛在网的发展趋势
2 3
泛在网概念的提出
Ubiquitous Computing
In 1991,“The method of enhancing computer use by making many computers available throughout the physical environment, but making them effectively invisible to the user”
Geometric Modeling
Geometric ModelingGeometric modeling is an essential aspect of computer graphics and design, playing a crucial role in various industries such as architecture, engineering, animation, and manufacturing. It involves the creation and manipulation of digital representations of geometric shapes and objects, allowing for the visualizationand analysis of complex structures and designs. However, despite its widespreaduse and significance, geometric modeling presents several challenges andlimitations that need to be addressed to improve its effectiveness and efficiency. One of the primary problems in geometric modeling is the complexity ofrepresenting real-world objects and surfaces accurately. Real-world objects often have irregular shapes and intricate details that are challenging to capture and model with traditional geometric primitives such as points, lines, and polygons. This limitation can result in inaccuracies and distortions in the digital representation of physical objects, affecting the quality and realism of computer-generated images and designs. To overcome this challenge, researchers and practitioners in the field of geometric modeling are exploring advanced techniques such as non-uniform rational B-splines (NURBS), subdivision surfaces, and point-based representations to achieve more realistic and detailed models. Another significant issue in geometric modeling is the computational cost and timerequired for creating and manipulating complex geometric shapes and structures. As the complexity and size of geometric models increase, the computational resources and processing power needed to perform operations such as rendering, simulation, and analysis also escalate. This can lead to performance bottlenecks and inefficiencies, particularly in applications that involve real-time interactionand visualization, such as virtual reality and video games. Addressing this challenge requires the development of efficient algorithms and data structures for geometric processing, as well as the utilization of parallel and distributed computing techniques to leverage the power of modern hardware architectures. Furthermore, geometric modeling faces the challenge of interoperability and compatibility between different software and hardware systems. In many cases, geometric models need to be exchanged and shared across different platforms and applications, requiring seamless data interoperability and conversion. However,the lack of standardized formats and protocols for representing and exchanging geometric data can hinder the smooth transfer and utilization of models between different software packages and environments. To tackle this issue, industry organizations and standardization bodies are working towards the development of open and vendor-neutral formats for geometric data exchange, such as the Industry Foundation Classes (IFC) for the architecture, engineering, and construction (AEC) industry. Moreover, the representation and manipulation of geometric models often involve subjective and qualitative aspects that are challenging to formalize and quantify. For example, the aesthetic and ergonomic qualities of a design, which are crucial considerations in fields like industrial design and architecture, are difficult to express and manipulate using purely geometric parameters and operations. This limitation can restrict the creative freedom and expressiveness of designers and artists, leading to designs that may lack innovation and originality. To address this challenge, researchers are exploring the integration of qualitative and procedural modeling techniques into geometric modeling systems, enabling the incorporation of subjective criteria and design intent into the digital representation and manipulation of objects and spaces. In addition, the increasing demand for personalized and customized products and experiences poses a challenge for geometric modeling in terms of flexibility and adaptability. Traditional geometric modeling approaches often rely on predefined shapes and templates, which may not be suitable for addressing the diverse and unique requirements of individual users and customers. This limitation can restrict the ability to create personalized designs and products that cater to specific preferences and needs, particularly in the context of mass customization and consumer-driven markets. To overcome this challenge, researchers and practitioners are exploring parametric and generative design approaches within geometric modeling, enabling the automated generation and customization of designs based on user-defined parameters and constraints. In conclusion, geometric modeling is a critical and evolving field that plays a fundamental role in various domains of computer graphics and design. While it offers powerful tools and techniques for creating and manipulating digital representations of geometric shapes and objects, it also presents several challenges and limitations that need to be addressed toenhance its effectiveness and versatility. By addressing issues such as accuracy, computational efficiency, interoperability, qualitative aspects, and flexibility, the field of geometric modeling can continue to advance and contribute to the development of innovative and impactful solutions for diverse applications and industries.。
4e-06Web建模
23
• 在Rose中用版型<<HTML Input>>、 <<HTML Select>>、<<HTML Textarea>>来 说明Form中包含的元素(作为Form的属性)。 • <<HTML Input>> 的type可以是text, password, checkbox, radio, submit, reset, file, hidden, image, button等。
24
• 在Rose中,由正向工程生成Survey.html文件, 代码如下所示:
<html> <body> <form Name="Form" Action="fastplan.jsp" Method="Post"> <textarea Name="notes"> </textarea> <select Name="province"> </select> <input Name="status" Type="radio" Value="yes"> <input Name="name" Type="text" Value="wsf"> </form> </body> </html>
11
• Rose中预定义的一些用于Web建模的关 系的版型,如:
– <<Link>> – ቤተ መጻሕፍቲ ባይዱ<Submit>> – <<Build>> – <<Redirect>> – <<Includes>> – <<Forward>> – <<Use COM Object>> – <<Use Bean>>
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Towards Modeling of DataWeb Applications - A Requirements' Perspective Werner Retschitzegger, Department of Information Systems, University Linz, werner@ifs.uni-linz.ac.at Wieland Schwinger, Software Competence Center Hagenberg, wieland.schwinger@scch.atAbstractThe web is more and more used as a platform for full-fledged, increasingly complex information systems, where a huge amount of change-intensive data is managed by underlying database systems. From a software engineering point of view, the development of such so called DataWeb applications requires proper modeling methods in order to ensure architectural soundness and maintainability. The goal of this paper is twofold. First, a framework of requirements, covering the design space of DataWeb modeling methods in terms of three orthogonal dimensions is suggested. Second, on the basis of this framework, eight representative modeling methods for DataWeb applications are surveyed and general shortcomings are identified pointing the way to next-generation modeling methods.IntroductionThe Internet, and in particular the World Wide Web, have introduced a new era of computing, providing the basis for promising application areas like electronic commerce (Kappel et al., 1998). At the beginning, the web has been employed merely for simple read-only information systems, i.e., systems realized by some web server offering static web pages for browsing, only. Nowadays, the web is more and more used as a platform for full-fledged, increasingly complex information systems, where a huge amount of change-intensive data is (partly) managed by underlying database systems (Ehmayer et al., 1997). The data can be navigated through, queried, and updated by means of web browsers, whereby web pages may either be generated in advance or dynamically in response to the requests of users whose number and type is not necessarily predictable (Pröll et al., 1998; Pröll et al., 1999). This emerging kind of information systems is further on called DataWeb applications.The development of such DataWeb applications is far from easy. Considering them from a software engineering point of view, as their complexity increases, so does the importance of modeling techniques. Models of a DataWeb application prior to its construction are essential for comprehension in its entirety, for communication among project teams, and to assure architectural soundness. However, the engineering of DataWeb applications has been widely neglected so far. This is not least since the unique characteristics of DataWeb applications comprising among others the usage of the hypermedia paradigm in terms of hypertext and multimedia in combination with traditional application logic make the straightforward employment of traditional modeling methods impossible (Nanard and Nanard, 1995; Powell, 1998).The current situation can be characterized as follows. First, most current web application development practices rely on the knowledge and experience of individual developers. Second, quick and dirty development by means of various tools - if any - such as HTML editors, database publishing wizards, web site managers and web form editors, that are driven by the underlying technology, is the state of practice (Fraternali, 2000). Finally, and probably most important, up to now, the web has been considered simply as an information medium and consequently, web development is seen as an authoring problem only (Ginige et al., 1995). However, since the web evolves from a document-centric platform towards an application-centric platform, document authoring methods are no longer adequate.In face of these problems, recently research towards modeling of DataWeb applications has been intensified. The goal of this paper is twofold. First, a framework of requirements, covering the design space of DataWeb modeling methods in terms of three orthogonal dimensions is suggested in Section 2. Second, on the basis of this framework, eight representative modeling methods for DataWeb applications are surveyed in Section 3. Finally, Section 4 concludes the paper by summarizing the key findings of our survey and points to future research.A Requirements’ Framework for DataWeb Modeling MethodsIn the following we want to elaborate on what is necessary when modeling DataWeb applications. The requirements discussed are partly derived from (Koch, 1999; Ceri et al., 1999a; Christodoulou et al., 1998) and (Fraternali, 2000). We have categorized these requirements by means of three orthogonal dimensions to be considered when modeling DataWeb applications, comprising levels, aspects and phases (cf. Figure 1). This framework of requirements allows to systematically survey DataWeb modeling methods thus indicating their strengths and shortcomings.Levels: Content, Hypertext and Presentation The first dimension of DataWeb application modeling comprises, similar to the Model/View/Controller (MVC) paradigm in object-oriented software development (Johnson and Foote, 1988), three different levels namely, the content level, the hypertext level, and the presentation level (Florescu et al., 1998). The content level refers to domain-dependent data used by the DataWeb application and is often managed by means of a database system. The hypertext level denotes the logical composition of web pages and the navigation structure. The presentation level, finally, is concerned with the representation of the hypertext level, e.g., the layout of each page and user interaction (Fernandez et al., 1997). Note that, the emphasize of each of these levels depends on the kind of DataWeb application which should be modeled as described later on.Figure 1. Modeling DimensionsSeparation Between Levels and Explicit Mapping.A major requirement is that there should be a clear separation between these three levels, each one concerned with a distinct aspect of DataWeb applications. This can be achieved by making the interdependencies, i.e., the mapping between the levels explicit. This should facilitate model evolution and reuse, reduce complexity and enhance flexibility (Florescu et al., 1998; Rossi et al., 1999). For example, it would be possible to provide different presentations for the same hypertext level, depending on browser specifics or personalization issues.Flexible Mapping Possibilities. In order to cope with the different goals intended when designing each of the levels, the possibilities for mapping should be as flexible as possible. For example, to make browsing more effective, documents are very redundant data-sources since the same piece of information can occur at several documents and navigated to by several different access paths. At the content level on the contrary, redundancy is eliminated by means of normalization techniques to avoid inconsistencies and update problems. Flexible mapping possibilities should ensure that despite of these differences, derivation of the levels from each other could be achieved. It would be also conceivable that the modeling method supports some kind of default mapping, which can be configured manually.Bottom-Up and Top-Down Design. Another requirement concerning these levels is that modeling should not be limited to follow bottom-up design, i.e., to start with modeling the content level and then derive the other levels accordingly. Rather, it should be also allowed to adhere to top-down design, meaning that the content level is derived from the other levels (Fraternali and Paolini, 1998). Bottom-up design is needed when, e.g., the already existing content of a database should be brought to the web, whereas top-down design is useful in case that the content of already existing web pages should be stored within a database.Aspects: Structure and BehaviorThe second dimension comprises the aspects of structure and behavior, which are orthogonal to the three levels of the first dimension. Concerning the content level, besides structuring the domain by means of standard abstraction mechanisms such as classification, aggregation and generalization, the behavioral aspect in terms of domain-dependent application logic has to be considered too. Similarly, at the hypertext level, structure in terms of page compositions and navigational relationships in between as well as behavior like computing the endpoint of a certain link at runtime have to be modeled. At the presentation level, finally, user interface elements and their hierarchical composition have to be modeled concerning the structural aspect. The behavioral aspect comprises modeling of reactions to input events, e.g., pressing a certain button as well as interaction and synchronization between user interface elements. Note that similar to the levels discussed above, the amount of structure and behavior which has to be modeled depends on the kind of DataWeb application as described later.Modeling Formalism for Structure and Behavior.A modeling formalism is required that takes into account both structural and behavioral particularities of the three levels. Although distinct modeling formalisms could be chosen for each of the three levels, for the purpose of a seamless mapping it would be beneficial if structure and behavior of all levels could be represented by building on a uniform basic modeling formalism. It has to be emphasized that this core modeling formalism has to be adapted in order to reflect specifics of each level. Since at all levels, both structure and behavior have to be modeled, it appears natural to build on an object-oriented modeling technique (Gellersen and Gaedke, 1999). This complies also with developments at the technology side, like the Web Object Model which has been suggested for the realization of DataWeb applications (Manola, 1999).Patterns. Another requirement to facilitate reuse and abstraction of structure and behavior is that the modeling method should support the representation of design patterns at all levels. German et al. (German and Cowan, 2000) have reported on more than fifty design patterns,Phasesmost of them concerning navigation at the hypertext level. Examples of navigational design patterns realizing contextual navigation, i.e., navigation from a given object to a related object in a certain semantic context, are guided tours which support linear navigation across pages, and indexes, allowing to navigate to the members of an index and vice versa (Ceri et al., 1999b). Phases: Analysis, Logical Modeling, Physical Modeling and ImplementationThe third dimension of modeling DataWeb applications comprises the different phases of a software life cycle, ranging from analysis to implementation. This dimension is orthogonal to the two previously presented ones, meaning that structure and behavior of content, navigation and presentation has to be addressed in each phase of the development process. At this time, there is no consensus on a general model for the lifecycle of DataWeb application development (Lowe and Webby, 1998). However, the influence of technological aspects tailoring the model towards the implementation environment, such as distribution, heterogeneity and database aspects, should certainly increase within the later phases of the modeling process. We therefore believe that, similar to database design, a separation between an abstract representation of the domain called conceptual modeling, technology independent design, i.e., logical modeling, and technology dependent design, i.e., physical modeling seems to be appropriate. Furthermore, in order to cope with the characteristics of aggressive release demands and rapid technology changes, web development should be much more incremental and iterative than development in other domains. That is, the need for prototyping and intensive testing with users is essential because user tolerance to errors in DataWeb applications is very low. A development process, which is part of an appropriate modeling method, has to take these requirements into account.Emphasis of the DimensionsSummarizing, modeling of any DataWeb application comprises these three dimensions, while their particular emphasis shifts for different kinds of DataWeb applications. For example, certain DataWeb applications provide a pure hypertext-oriented user interface to access large amounts of complex structured data. This might be realized as inter-linked HTML pages that are generated out of a database on a user’s request by means of some server-side application logic. Examples for such kind of DataWeb applications can be found in the area of electronic commerce (cf., e.g., (Pröll et al., 1998; Pröll et al., 1999)) where the emphasis is on portability of the application across different browsers employed by Internet users, and where the underlying data changes frequently. Another kind of DataWeb application may require very complex application logic and interactivity at the client side. This could make it useful to resign the hypermedia paradigm in certain cases and instead employ Java applets for the user interface communicating directly with the database. Typical scenarios for this kind are Intranet applications, where delivering the code of the Java applet across the network does not affect performance. Of course, quite often a combination of these two kinds will be found in practice. Consequently, it is necessary that a modeling method takes into account these different peculiarities of DataWeb applications by providing appropriate concepts and modeling elements. The requirements’ framework proposed is general enough to cover all these kinds of DataWeb modeling methods. Evaluation of Existing Modeling Methods On the basis of the requirements’ framework given above, eight representative DataWeb modeling methods are surveyed in the following. Figure 2 illustrates the different origins of these methods, the arcs denoting influences between them. Accordingly, the modeling methods are categorized into different generations. Figure 2. Origins of DataWeb Modeling Models The modeling methods have their origins in different communities, including database systems being therefore mainly based on Entity-Relationship (ER) modeling (Chen, 1976) (cf. RMM (Isakowitz et al., 1998) and Araneus (Atzeni et al., 1998)), hypermedia using the Dexter Reference Model as basis (Halasz and Schwartz, 1994) (cf. HDM (Garzotto et al., 1995; Garzotto et al., 1997), HDM lite (Fraternali and Paolini, 1998; Fraternali, 2000) and W3I3 (Ceri et al., 1999b)) and object-oriented modeling in terms of the Object Modeling Technique (OMT) (Rumbaugh et al., 1991) and the Unified Modeling Language (UML) (Rumbaugh et al., 1998) (cf. OOHDM (Rossi et al., 1999), Baumeister et al. (Baumeister et al., 1999) and Conallen (Conallen, 1999)). Figure 3 summarizes the support of levels, aspects and phases concerning each of these approaches.HDM. In modeling DataWeb applications, HDM (Hypermedia Design Method) (Garzotto et al., 1997) distinguishes between hypertext level and presentation level only, i.e., modeling of the application domain is intermingled with modeling the hypertext level. A reason could be that HDM originates from the hypermedia community, where the explicit modeling of the content level which is managed by databases is no issue. Although, a clear separation between hypertext level andTraditional ModelingMethods1st Generation DataWebModeling Methods2nd Generation DataWebModeling Methods3rd Generation DataWebModeling Methodspresentation level is pursued by the authors, concepts for an explicit and flexible mapping are not described. The modeling formalism used is based on concepts borrowed from the ER Model (Chen, 1976) and from the Dexter Model (Halasz and Schwartz, 1994). Structural aspects are considered on both levels by means of various concepts. Behavioral aspects are mainly considered at the presentation level by modeling the user interaction in terms of (de)activation rules called dynamics in HDM. At the hypertext level, HDM further distinguishes between a so-called hyperbase layer, modeling the application domain and the access layer, defining a set of collections that provide users with the patterns to access the hyperbase such as index and guided tour. Finally, concerning the dimension of phases, HDM largely concentrates on two phases called authoring in the large and authoring in the small. Whereas authoring in the large comprises modeling of overall, general features, authoring in the small makes some refinements and takes the implementation technology into account.RMM. RMM(Relationship Management Methodology) (Isakowitz et al., 1995; Isakowitz et al., 1998) is influenced by the ER Model and HDM. RMM recognizes all three levels. The content level is modeled separately whereas the presentation level is refined jointly with the hypertext level. A dedicated modeling formalism called Relationship Management Data Model (RMDM) is introduced, using the ER Model for the content level and proprietary concepts which are influenced by HDM for the hypertext level and the presentation level. The concept of so-called m-slices is used to map between the content level and the hypertext in that attributes from the entities of the ER-diagram and/or previously defined m-slices are grouped together. Navigational patterns in terms of index and guided tours are provided. Relationships between entities are used to capture contextual information during navigation. A so-called Application Diagram provides a global view of the presentation level of the DataWeb application by capturing all pages and hyperlinks in-between. Additionally, authoring tools are employed for creating page templates which in turn are assigned to every page. Only structural aspects are considered for all levels. RMM specifies a development process with initial steps for requirement analysis and content modeling in form of ER-diagrams. These are followed by iterative steps refining the Application Diagram both bottom-up, and top-down whilst m-slice design.Araneus. Araneus (Atzeni et al., 1998), like RMM, embrace content level, hypertext level, and presentation level but emphasizes the content and hypertext level, only. A unique characteristic of Araneus is that content and hypertext level are refined independently from each other. Regarding the content level, based upon the Conceptual Design, the Logical Design and, if necessary, the Physical Design can be derived. Similar to RMM, the content level of Araneus relies on the ER Model.Considering the hypertext level, the Hypertext Conceptual Design formulated by the Navigation Conceptual Model (NCM) is refined by the Hypertext Logical Design, using Araneus Data Model (ADM) as formalism, tailoring the design towards the web. Likewise RMM, just structural aspects are considered for the content level as well as for the hypertext level. The presentation level is considered during Presentation Design relying on HTML-page templates created by an authoring tool. Patterns are not supported for any of the three levels. Araneus defines a process comprising initially the Database Conceptual Design from which in turn the Hypertext Conceptual Design is derived. After that, the refinement into the logical models is conducted in parallel. In the final step, after Presentation Design, the hypertext level is explicitly mapped onto the content level using a declarative formalism called PENELOPE building the basis for automatic page generation.HDM lite. HDM lite (Fraternali, 2000) is a web-specific evolution of HDM condensing the concepts of HDM. Similar to HDM, the content level is not modeled separately but rather together with the hypertext level by means of the so-called Structure Schema. HDM lite uses a formalism descending from the ER Model and HDM. Additionally, at the hypertext level, the Navigation Schema specifies the access paths applying standard navigation patterns along with contextual navigation. Unlike HDM, the presentation level is modeled by means of the so-called Presentation Schema using a SGML like syntax as formalism. More than one presentation schema can be mapped to a Structure/Navigation Schema pair but no mapping constructs are supplied. Behavioral aspects are neglected for all three levels. Concerning the dimension of phases HDM lite proposes a transformation to convert the HDM lite conceptual schemata into a logical representation and further into a physical representation. For the former, well-known techniques for translating ER schemata into logical schemata augmented to treat also navigational and presentational issues are used. For the later non-standard transformation techniques are introduced. These transformations are implemented by the so-called Autoweb system thus automatically generating an Application Data Schema, a Navigation Schema, and a Presentation Schema covering content, hypertext, and presentation level, respectively. These logical schemata are further on utilized for automatic page generation by the Autoweb system.W3I3. The main research objective of the EU Esprit project W3I3 (Web-based Intelligent Information Infrastructure) (Ceri et al., 1999b) is to rise the level of abstraction of the specification of a DataWeb application by enriching and refocusing the classical methods for database and hypertext design. W3I3 is an evolution of HDM lite and distinguishes five different models called Structural Model, Derivation Model, Composition Model, Navigation Model, and Presentation Model. TheStructural Model and the Derivation Model describe the content level by simply using an ER Model and derivation rules, respectively. The Composition Model describes, by means of site views, how the concepts of the Structural Level are mapped to web pages for a certain group of users and provides a default mapping for the case that there is only one simple site view needed. The Navigation Model describes the way in which associations within the Structural Model should be used for navigation thus capturing contextual navigation. Additionally, predefined navigational patterns are given for the hypertext level. The Presentation Model corresponding to the presentation level uses style sheets in order to define the layout of pages, whereby a default style sheet is provided for each page. Behavioral aspects are not considered at any of the three levels. Finally, W3I3 does not propose a particular process or specifies phases.Figure 3. Comparison of Modeling MethodsOOHDM. OOHDM (Object-oriented Hypermedia Design Method) (Rossi et al., 1999) strictly separates the three levels of a DataWeb application. At the content level OOHDM uses the object-oriented modeling technique OMT (Rumbaugh et al., 1991) as a modeling formalism to capture structure and behavior. The hypertext level is modeled by means of three different concepts. First, the so-called Navigational Class Schema is used to define structural aspects by specifying the navigable classes of the application by means of an OMT class diagram. It can be seen as a view over the content level, whereby the mapping between these two levels can be done explicitly by means of a query language. Second, the Navigational Context Schema models the access structures to the navigable classes in terms of six different kinds of contexts by means of a proprietary notation thus capturing contextual navigation and providing an index navigation pattern. Third, Navigational Transformation specifications refer to the behavioral aspect of the hypertext level by modeling the activation/deactivation of navigational objects during navigation. There is no specific formalism employed but it is referred to statecharts only (Rumbaugh et al., 1991). At the presentation level a formalism called Abstract Data View(ADV) is used to describe the layout structure of navigational objects and other interface objects such as menu bars and buttons by means of traditional abstraction mechanisms. The behavioral aspect comprising the reactions to external events is described by using ADV-Charts, a derivative of statecharts. In order to express the mapping to navigational objects of the hypertext level in terms of static relationships so-called Configuration Diagrams are used. Finally, OOHDM does not suggest a dedicated process but distinguishes three different phases which partly correspond to the level’s dimension, namely conceptual modeling, navigational design and abstract interface design.Baumeister et al. The approach proposed by Baumeister et al. (Baumeister et al., 1999) is based on OOHDM but instead of using a mix of different formalisms throughout the levels, UML is used as the basic modeling technique. As far as necessary, UML is enhanced on the basis of two of UML’s extension mechanisms namely stereotypes1 and constraints2. It is separated between all three levels, comprising a Conceptual Model in terms of pure UML diagrams, a Navigational Model and a Presentational Model. At the hypertext level, the Navigational Class Model (cf. Navigational Class Schema in OOHDM) specifies which classes and associations of the content level are available for navigation. It is represented by means of a UML class diagram, denoting the navigational classes by means of stereotypes, navigable associations by means of directions and specifying the mapping by means of constraints. The Navigational Structure Model (cf. Navigational Context Schema in OOHDM) is based on the Navigational Class Model and defines (interestingly by means of an UML object diagram) how each navigational class is accessed during navigation. Stereotypes are again used to represent1A stereotype represents an adornment that allows to define a new semantic meaning for a modeling element (Rumbaugh et al., 1998).2Constraints are rules that define the well-formedness of amodel and can be expressed as free-form text or with the more formal Object constraint language (OCL) (Rumbaugh et al., 1998).LevelsContent Hypertext Presentation Aspects Phases Aspects Phases Aspects PhasesS B CM LM PM S B CM LM PM S B CM LM PM HDM✘✘✘✘✘✔✘✔✔✘✔✔✘✔✔RMM✔✘✔✘✘✔✘✔✔✘✔✘✔✘✔Araneus✔✘✔✔✔✔✘✔✔✔✔✘✘✘✔HDM lite✔✘✔✘✘✔✘✔✔✘✔✘✘✔✔W3I3✔✘✔✘✘✔✘✘✔✘✔✘✘✘✔OOHDM✔✔✔✔✔✔✔✔✔✘✔✔✘✔✔Baumeister✔✔✔✔✔✔✔✔✔✔✔✔✘✔✔Conallen✔✔✔✔✔✔✘✘✔✔✔✘✘✘✔Legend: Aspects:S.......Structural Aspects Phases:CM......Conceptual Model✔......supportedB.......Behavioral Aspects LM......Logical Model✘......not supportedPM......Physical Modelnavigational contexts and thus provide navigationalpatters as index and guided tour. Behavior modeling is only mentioned with respect to defining the sequence ofnavigation by means of constraints. At the presentationallevel, a Static Presentation Model, using the possibility ofUML to represent compositions by means of graphicalnesting describes the layout of the user interface, and aDynamic Presentation Model employs UML statecharts for describing the activation of navigation and userinterface transformations. Stereotypes representing themost frequently used interface objects such as text, image,audio and button are provided. Note that the mappingbetween hypertext level and presentation level is notdiscussed by the approach. Concerning the dimension of phases, the same holds as for OOHDM.Conallen. The approach suggested by Conallen(Conallen, 1999) is completely different from the otherones, since it is to a great extent technology driven. UMLis employed as the basic formalism and extended bymeans of stereotypes and tagged values3. Instead of distinguishing between content, hypertext andpresentation level, Conallen models web pages at theserver side and at the client side by stereotyping UMLclasses. Stereotyped associations are used to representhyperlinks and to model the mapping between client pages and server pages, since every dynamic client webpage, i.e., a page whose content is determined at runtimeis constructed with a server page. Data entry forms whichcan be part of client pages together with their submitrelationship to server pages are modeled by another classand association stereotype, respectively. Finally, there are also class stereotypes for Java Applets, Java Scripts,ActiveX controls and frames. Conallen does not discussany behavior modeling apart from operations which canbe defined together with the stereotyped classes and doesnot suggest any modeling phases.Concluding RemarksThis paper has proposed a requirements’ framework for DataWeb modeling methods. The requirements havebeen categorized by means of three dimensions which areorthogonal to each other. This requirements’ frameworkwas used to survey eight DataWeb modeling methods.The main shortcomings of these methods encountered during the evaluation can be summarized as follows:•Lack of Explicit and Flexible Mapping. The definition of explicit and flexible mapping knowledge betweenthe three levels is often not discussed by theapproaches.•Top-Down and Bottom-Up Design is notDistinguished. Most of the methods, except RMM,3Tagged values are the third UML extension mechanism that allows to associate key value pairs with a modeling element (Rumbaugh et al., 1998).assume that modeling is done by starting either at thecontent level or at the hypertext level.•Behavioral Modeling is Widely Neglected. Modeling the behavioral aspect of DataWeb applications at alllevels is widely neglected by existing methods. Ifbehavior is considered then mainly at the presentationlevel. Only those methods being based on object-oriented modeling formalisms partly deal withbehavior modeling at all levels.•No Uniform Modeling Formalism. Except the approaches of Baumeister et al. and Conallen whichfully rely on UML, all modeling methods are based ona mix of mainly proprietary modeling formalisms.•Patterns are Supported at the Hypertext Level only.There are no concepts provided to support themodeling of patterns at all three levels.•Presentation Level not Captured by Conceptual and Logical Modeling Concepts. Most of the modelingmethods do not support the presentation level withappropriate conceptual and logical modeling concepts.Rather, authoring tools are often suggested forcapturing the presentation level, thus loosing thebenefit of technology-independence.•No Process Support. Most modeling methods do not follow a process for guiding the activities throughout the development of a DataWeb application.In face of these various shortcomings, it can be arguedthat those modeling methods being based on the object-oriented paradigm and in particular on UML, seem tohave the largest potential to cover all requirements ofDataWeb application modeling.Currently, we are working on an extension of UMLtowards DataWeb application modeling particularlyaddressing shortcomings identified in this paper. ReferencesAtzeni, P., Mecca, G. and Merialdo, P. "Design andImplementation of Data-Intensive Web Sites", Proc. ofthe Conf. On Extended Database Technology (EDBT’98), Valencia, Spain, March 1998.Baumeister, H., Koch N. and Mandel, L."Towards a UML extension for hypermedia design", UML’99 The Unified Modeling Language - Beyond the Standard, LNCS 1723, Fort Collins, USA, Springer, October 1999.Ceri, S., Fraternali, P. and Paraboschi, S. "Design Principles for Data-Intensive Web Sites", SIGMOD Record, 24(1), March 1999a.Ceri, S., Fraternali, P. and Paraboschi, S. "Data-Driven One-To-One Web Site Generation for Data-Intensive Applications", Proc. VLDB '99, Edinburgh, Sept. 1999b.。