Author: David C. Hay
Data Model Patterns: A Metadata Map not only presents a conceptual model of a metadata repository but also demonstrates a true enterprise data model of the information technology industry itself. It provides a step-by-step description of the model and is organized so that different readers can benefit from different parts. It offers a view of the world being addressed by all the techniques, methods, and tools of the information processing industry (for example, object-oriented design, CASE, business process re-engineering, etc.) and presents several concepts that need to be addressed by such tools. This book is pertinent, with companies and government agencies realizing that the data they use represent a significant corporate resource recognize the need to integrate data that has traditionally only been available from disparate sources. An important component of this integration is management of the "metadata" that describe, catalogue, and provide access to the various forms of underlying business data. The "metadata repository" is essential to keep track of the various physical components of these systems and their semantics. The book is ideal for data management professionals, data modeling and design professionals, and data warehouse and database repository designers. A comprehensive work based on the Zachman Framework for information architecture—encompassing the Business Owner's, Architect's, and Designer's views, for all columns (data, activities, locations, people, timing, and motivation) Provides a step-by-step description of model and is organized so that different readers can benefit from different parts Provides a view of the world being addressed by all the techniques, methods and tools of the information processing industry (for example, object-oriented design, CASE, business process re-engineering, etc.) Presents many concepts that are not currently being addressed by such tools — and should be
Conventions of Thought
Author: David Hay
This is the digital version of the printed book (Copyright © 1996). Learning the basics of a modeling technique is not the same as learning how to use and apply it. To develop a data model of an organization is to gain insights into its nature that do not come easily. Indeed, analysts are often expected to understand subtleties of an organization's structure that may have evaded people who have worked there for years. Here's help for those analysts who have learned the basics of data modeling (or "entity/relationship modeling") but who need to obtain the insights required to prepare a good model of a real business. Structures common to many types of business are analyzed in areas such as accounting, material requirements planning, process manufacturing, contracts, laboratories, and documents. In each chapter, high-level data models are drawn from the following business areas: The Enterprise and Its World The Things of the Enterprise Procedures and Activities Contracts Accounting The Laboratory Material Requirements Planning Process Manufacturing Documents Lower-Level Conventions
A Library of Universal Data Models for All Enterprises
Author: Len Silverston
Publisher: John Wiley & Sons
A quick and reliable way to build proven databases for core business functions Industry experts raved about The Data Model Resource Book when it was first published in March 1997 because it provided a simple, cost-effective way to design databases for core business functions. Len Silverston has now revised and updated the hugely successful 1st Edition, while adding a companion volume to take care of more specific requirements of different businesses. This updated volume provides a common set of data models for specific core functions shared by most businesses like human resources management, accounting, and project management. These models are standardized and are easily replicated by developers looking for ways to make corporate database development more efficient and cost effective. This guide is the perfect complement to The Data Model Resource CD-ROM, which is sold separately and provides the powerful design templates discussed in the book in a ready-to-use electronic format. A free demonstration CD-ROM is available with each copy of the print book to allow you to try before you buy the full CD-ROM.
Author: Michael Blaha
Publisher: CRC Press
Best-selling author and database expert with more than 25 years of experience modeling application and enterprise data, Dr. Michael Blaha provides tried and tested data model patterns, to help readers avoid common modeling mistakes and unnecessary frustration on their way to building effective data models. Unlike the typical methodology book, Patterns of Data Modeling provides advanced techniques for those who have mastered the basics. Recognizing that database representation sets the path for software, determines its flexibility, affects its quality, and influences whether it succeeds or fails, the text focuses on databases rather than programming. It is one of the first books to apply the popular patterns perspective to database systems and data models. It offers practical advice on the core aspects of applications and provides authoritative coverage of mathematical templates, antipatterns, archetypes, identity, canonical models, and relational database design.
Describing the World
Author: David C. Hay
Publisher: Technics Publications
Here you’ll find one key to the development of a successful information system: Clearly capture and communicate both the abstract and concrete building blocks of data that describe your organization. In 1995, David Hay published Data Model Patterns: Conventions of Thought - the groundbreaking book on how to use standard data models to describe the standard business situations. Enterprise Model Patterns: Describing the World builds on the concepts presented there, adds 15 years of practical experience, and presents a more comprehensive view. You will learn how to apply both the abstract and concrete elements of your enterprise’s architectural data model through four levels of abstraction: Level 0: An abstract template that underlies the Level 1 model that follows, plus two meta models: • Information Resources. In addition to books, articles, and e-mail notes, it also includes photographs, videos, and sound recordings. • Accounting. Accounting is remarkable because it is itself a modeling language. It takes a very different approach than data modelers in that instead of using entities and entity classes that represent things in the world, it is concerned with accounts that represent bits of value to the organization. Level 1: An enterprise model that is generic enough to apply to any company or government agency, but concrete enough to be readily understood by all. It describes: • People and Organization. Who is involved with the business? The people involved are not only the employees within the organization, but customers, agents, and others with whom the organization comes in contact. Organizations of interest include the enterprise itself and its own internal departments, as well as customers, competitors, government agencies, and the like. • Geographic Locations. Where is business conducted? A geographic location may be either a geographic area (defined as any bounded area on the Earth), a geographic point (used to identify a particular location), or, if you are an oil company for example, a geographic solid (such as an oil reserve). • Assets. What tangible items are used to carry out the business? These are any physical things that are manipulated, sometimes as products, but also as the means to producing products and services. • Activities. How is the business carried out? This model not only covers services offered, but also projects and any other kinds of activities. In addition, the model describes the events that cause activities to happen. • Time. All data is positioned in time, but some more than others. Level 2: A more detailed model describing specific functional areas: • Facilities • Human Resources • Communications and Marketing • Contracts • Manufacturing • The Laboratory Level 3: Examples of the details a model can have to address what is truly unique in a particular industry. Here you see how to address the unique bits in areas as diverse as: • Criminal Justice. The model presented here is based on the “Global Justice XML Data Model” (GJXDM). • Microbiology • Banking. The model presented here is the result of working for four different banks and then adding some thought to come up with something different from what is currently in any of them. • Highways. The model here is derived from a project in a Canadian Provincial Highway Department, and addresses the question “what is a road?”
Author: J. Henno
Publisher: IOS Press
Information modelling and knowledge bases have become hot topics, not only in academic communities concerned with information systems and computer science, but also wherever information technology is applied in the world of business. This book presents the proceedings of the 21st European-Japanese Conference on Information Modelling and Knowledge Bases (EJC 2011), held in Tallinn, Estonia, in June 2011. The EJC conferences provide a worldwide forum for researchers and practitioners in the field to exchange results and experiences achieved in computer science and related disciplines such as conceptual analysis, design and specification of information systems, multimedia information modelling, multimedia systems, software engineering, knowledge and process management, cross cultural communication and context modelling. Attention is also paid to theoretical disciplines including cognitive science, artificial intelligence, logic, linguistics and analytical philosophy. The selected papers (16 full papers, 9 short papers, 2 papers based on panel sessions and 2 on invited presentations), cover a wide range of topics, including database semantics, knowledge representation, software engineering, www information management, context-based information retrieval, ontology, image databases, temporal and spatial databases, document data management, process management, cultural modelling and many others. Covering many aspects of system modelling and optimization, this book will be of interest to all those working in the field of information modelling and knowledge bases.
Author: Jiawei Han,Jian Pei,Micheline Kamber
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data
Practical Machine Learning Tools and Techniques
Author: Ian H. Witten,Eibe Frank,Mark A. Hall,Christopher J. Pal
Publisher: Morgan Kaufmann
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at http://www.cs.waikato.ac.nz/ml/weka/book.html It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface Includes open-access online courses that introduce practical applications of the material in the book
Author: Fon Silvers
Publisher: CRC Press
As it is with building a house, most of the work necessary to build a data warehouse is neither visible nor obvious when looking at the completed product. While it may be easy to plan for a data warehouse that incorporates all the right concepts, taking the steps needed to create a warehouse that is as functional and user-friendly as it is theoretically sound, is not especially easy. That’s the challenge that Building and Maintaininga Data Warehouse answers. Based on a foundation of industry-accepted principles, this work provides an easy-to-follow approach that is cohesive and holistic. By offering the perspective of a successful data warehouse, as well as that of a failed one, this workdetails those factors that must be accomplished and those that are best avoided. Organized to logically progress from more general to specific information, this valuable guide: Presents areas of a data warehouse individually and in sequence, showing how each piece becomes a working part of the whole Examines the concepts and principles that are at the foundation of every successful data warehouse Explains how to recognize and attend to problematic gaps in an established data warehouse Provides the big picture perspective that planners and executives require Those considering the planning and creation of a data warehouse, as well as those who’ve already built one will profit greatly from the insights garnered by the author during his years of creating and gathering information on state-of-the-art data warehouses that are accessible, convenient, and reliable.
Data Integration Best Practice Techniques and Technologies
Author: April Reeve
Managing Data in Motion describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment. The average enterprise's computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired. The management of the "data in motion" in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and "big data" applications are just a few of the challenges facing organizations and businesses today. Managing Data in Motion tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects. Presents a vendor-neutral overview of the different technologies and techniques for moving data between computer systems including the emerging solutions for unstructured as well as structured data types Explains, in non-technical terms, the architecture and components required to perform data integration Describes how to reduce the complexity of managing system interfaces and enable a scalable data architecture that can handle the dimensions of "Big Data"
Author: Charles T. Betz
Architecture and Patterns for IT Service Management, Resource Planning, and Governance: Making Shoes for the Cobbler's Children provides an independent examination of developments in Enterprise Resource Planning for Information. Major companies, research firms, and vendors are offering Enterprise Resource Planning for Information Technology, which they label as ERP for IT, IT Resource Planning and related terms. This book presents on-the-ground coverage of enabling IT governance in architectural detail, which can be used to define a strategy for immediate execution. It fills the gap between high-level guidance on IT governance and detailed discussions about specific vendor technologies. It provides a unique value chain approach to integrating the COBIT, ITIL, and CMM frameworks into a coherent, unified whole. It presents a field-tested, detailed conceptual information model with definitions and usage scenarios, mapped to both process and system architectures. This book is recommended for practitioners and managers engaged in IT support in large companies, particularly those who are information architects, enterprise architects, senior software engineers, program/project managers, and IT managers/directors.
Author: Christian Bird,Tim Menzies,Thomas Zimmermann
The Art and Science of Analyzing Software Data provides valuable information on analysis techniques often used to derive insight from software data. This book shares best practices in the field generated by leading data scientists, collected from their experience training software engineering students and practitioners to master data science. The book covers topics such as the analysis of security data, code reviews, app stores, log files, and user telemetry, among others. It covers a wide variety of techniques such as co-change analysis, text analysis, topic analysis, and concept analysis, as well as advanced topics such as release planning and generation of source code comments. It includes stories from the trenches from expert data scientists illustrating how to apply data analysis in industry and open source, present results to stakeholders, and drive decisions. Presents best practices, hints, and tips to analyze data and apply tools in data science projects Presents research methods and case studies that have emerged over the past few years to further understanding of software data Shares stories from the trenches of successful data science initiatives in industry
Using Vision to Think
Author: Stuart K. Card,Jock D. Mackinlay,Ben Shneiderman
Publisher: Morgan Kaufmann
This groundbreaking book defines the emerging field of information visualization and offers the first-ever collection of the classic papers of the discipline, with introductions and analytical discussions of each topic and paper. The authors' intention is to present papers that focus on the use of visualization to discover relationships, using interactive graphics to amplify thought. This book is intended for research professionals in academia and industry; new graduate students and professors who want to begin work in this burgeoning field; professionals involved in financial data analysis, statistics, and information design; scientific data managers; and professionals involved in medical, bioinformatics, and other areas. * Full-color reproduction throughout * Author power team - an exciting and timely collaboration between the field's pioneering, most-respected names * The only book on Information Visualization with the depth necessary for use as a text or as a reference for the information professional * Text includes the classic source papers as well as a collection of cutting edge work
Volume 3: Universal Patterns for Data Modeling
Author: Len Silverston,Paul Agnew
Publisher: John Wiley & Sons
This third volume of the best-selling "Data Model Resource Book" series revolutionizes the data modeling discipline by answering the question "How can you save significant time while improving the quality of any type of data modeling effort?" In contrast to the first two volumes, this new volume focuses on the fundamental, underlying patterns that affect over 50 percent of most data modeling efforts. These patterns can be used to considerably reduce modeling time and cost, to jump-start data modeling efforts, as standards and guidelines to increase data model consistency and quality, and as an objective source against which an enterprise can evaluate data models. Praise for The Data Model Resource Book, Volume 3 "Len and Paul look beneath the superficial issues of data modeling and have produced a work that is a must for every serious designer and manager of an IT project." —Bill Inmon, World-renowned expert, speaker, and author on data warehousing and widely recognized as the "father of data warehousing" "The Data Model Resource Book, Volume 3: Universal Patterns for Data Modeling is a great source for reusable patterns you can use to save a tremendous amount of time, effort, and cost on any data modeling effort. Len Silverston and Paul Agnewhave provided an indispensable reference of very high-quality patterns for the most foundational types of datamodel structures. This book represents a revolutionary leap in moving the data modeling profession forward." —Ron Powell, Cofounder and Editorial Director of the Business Intelligence Network "After we model a Customer, Product, or Order, there is still more about each of these that remains to be captured, such as roles they play, classifications in which they belong, or states in which they change. The Data Model Resource Book, Volume 3: Universal Patterns for Data Modeling clearly illustrates these common structures. Len Silverston and Paul Agnew have created a valuable addition to our field, allowing us to improve the consistency and quality of our models by leveraging the many common structures within this text." —Steve Hoberman, Best-Selling Author of Data Modeling Made Simple "The large national health insurance company I work at has actively used these data patterns and the (Universal Data Models) UDM, ahead of this book, through Len Silverston's UDM Jump Start engagement. The patterns have found their way into the core of our Enterprise Information Model, our data warehouse designs, and progressively into key business function databases. We are getting to reuse the patterns across projects and are reaping benefits in understanding, flexibility, and time-to-market. Thanks so much." —David Chasteen, Enterprise Information Architect "Reusing proven data modeling design patterns means exactly that. Data models become stable, but remain very flexible to accommodate changes. We have had the fortune of having Len and Paul share the patterns that are described in this book via our engagements with Universal Data Models, LLC. These data modeling design patterns have helped us to focus on the essential business issues because we have leveraged these reusable building blocks for many of the standard design problems. These design patterns have also helped us to evaluate the quality of data models for their intended purpose. Many times there are a lot of enhancements required. Too often the very specialized business-oriented data model is also implemented physically. This may have significant drawbacks to flexibility. I'm looking forward to increasing the data modeling design pattern competence within Nokia with the help of this book." —Teemu Mattelmaki, Chief Information Architect, Nokia "Once again, Len Silverston, this time together with Paul Agnew, has made a valuable contribution to the body of knowledge about datamodels, and the act of building sound data models. As a professional d
Author: M. Papazoglou,S. Spaccapietra,Zahir Tari
Publisher: MIT Press
Until recently, information systems have been designed around different business functions, such as accounts payable and inventory control. Object-oriented modeling, in contrast, structures systems around the data--the objects--that make up the various business functions. Because information about a particular function is limited to one place--to the object--the system is shielded from the effects of change. Object-oriented modeling also promotes better understanding of requirements, clear designs, and more easily maintainable systems.This book focuses on recent developments in representational and processing aspects of complex data-intensive applications. The chapters cover "hot" topics such as application behavior and consistency, reverse engineering, interoperability and collaboration between objects, and work-flow modeling. Each chapter contains a review of its subject, followed by object-oriented modeling techniques and methodologies that can be applied to real-life applications.Contributors : F. Casati, S. Ceri, R. Cicchetti, L. M. L. Delcambre, E. F. Ecklund, D. W. Embley, G. Engels, J. M. Gagnon, R. Godin, M. Gogolla, L. Groenewegen, G. S. Jensen, G. Kappel, B. J. Krämer, S. W. Liddle, R. Missaoui, M. Norrie, M. P. Papazoglou, C. Parent, B. Perniei, P. Poncelet, G. Pozzi, M. Schreft, R. T. Snodgrass, S. Spaccapietra, M. Stumptner, M. Teisseire, W. J. van den Heuevel, S. N. Woodfield.
Author: Ian H. Witten,David Bainbridge,David M. Nichols
Publisher: Morgan Kaufmann
How to Build a Digital Library reviews knowledge and tools to construct and maintain a digital library, regardless of the size or purpose. A resource for individuals, agencies, and institutions wishing to put this powerful tool to work in their burgeoning information treasuries. The Second Edition reflects developments in the field as well as in the Greenstone Digital Library open source software. In Part I, the authors have added an entire new chapter on user groups, user support, collaborative browsing, user contributions, and so on. There is also new material on content-based queries, map-based queries, cross-media queries. There is an increased emphasis placed on multimedia by adding a "digitizing" section to each major media type. A new chapter has also been added on "internationalization," which will address Unicode standards, multi-language interfaces and collections, and issues with non-European languages (Chinese, Hindi, etc.). Part II, the software tools section, has been completely rewritten to reflect the new developments in Greenstone Digital Library Software, an internationally popular open source software tool with a comprehensive graphical facility for creating and maintaining digital libraries. Outlines the history of libraries on both traditional and digital Written for both technical and non-technical audiences and covers the entire spectrum of media, including text, images, audio, video, and related XML standards Web-enhanced with software documentation, color illustrations, full-text index, source code, and more
How to clean, link and publish your metadata
Author: Seth van Hooland,Ruben Verborgh
Publisher: Facet Publishing
Category: Language Arts & Disciplines
This highly practical handbook teaches you how to unlock the value of your existing metadata through cleaning, reconciliation, enrichment and linking and how to streamline the process of new metadata creation. Libraries, archives and museums are facing up to the challenge of providing access to fast growing collections whilst managing cuts to budgets. Key to this is the creation, linking and publishing of good quality metadata as Linked Data that will allow their collections to be discovered, accessed and disseminated in a sustainable manner. This highly practical handbook teaches you how to unlock the value of your existing metadata through cleaning, reconciliation, enrichment and linking and how to streamline the process of new metadata creation. Metadata experts Seth van Hooland and Ruben Verborgh introduce the key concepts of metadata standards and Linked Data and how they can be practically applied to existing metadata, giving readers the tools and understanding to achieve maximum results with limited resources. Readers will learn how to critically assess and use (semi-)automated methods of managing metadata through hands-on exercises within the book and on the accompanying website. Each chapter is built around a case study from institutions around the world, demonstrating how freely available tools are being successfully used in different metadata contexts. This handbook delivers the necessary conceptual and practical understanding to empower practitioners to make the right decisions when making their organisations resources accessible on the Web. Key topics include: - The value of metadata Metadata creation – architecture, data models and standards - Metadata cleaning - Metadata reconciliation - Metadata enrichment through Linked Data and named-entity recognition - Importing and exporting metadata - Ensuring a sustainable publishing model. Readership: This will be an invaluable guide for metadata practitioners and researchers within all cultural heritage contexts, from library cataloguers and archivists to museum curatorial staff. It will also be of interest to students and academics within information science and digital humanities fields. IT managers with responsibility for information systems, as well as strategy heads and budget holders, at cultural heritage organisations, will find this a valuable decision-making aid.
Perception for Design
Author: Colin Ware
"This is a book about what the science of perception can tell us about visualization. There is a gold mine of information about how we see to be found in more than a century of work by vision researchers. The purpose of this book is to extract from that large body of research literature those design principles that apply to displaying information effectively"--
Author: David Loshin
The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers. Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning.