Advertisement
| indexing in data warehouse ppt: OLAP Solutions Erik Thomsen, 2002-10-15 OLAP enables users to access information from multidimensional datawarehouses almost instantly, to view information in any way theylike, and to cleanly specify and carry out sophisticatedcalculations. Although many commercial OLAP tools and products arenow available, OLAP is still a difficult and complex technology tomaster. Substantially updated with expanded coverage of implementationmethods for data storage, access, and calculation; also, newchapters added to combine OLAP with data warehouse, mining, anddecision support tools Teaches the best practices for building OLAP models thatimprove business and organizational decision-making, completelyindependent of commercial tools, using revised case studies Companion Web site provides updates on OLAP standards andtools, code examples, and links to valuable resources |
| indexing in data warehouse ppt: ISE Database System Concepts Abraham Silberschatz, Henry F. Korth, S. Sudarshan, 2019-02-28 Database System Concepts by Silberschatz, Korth and Sudarshan is now in its 7th edition and is one of the cornerstone texts of database education. It presents the fundamental concepts of database management in an intuitive manner geared toward allowing students to begin working with databases as quickly as possible. The text is designed for a first course in databases at the junior/senior undergraduate level or the first year graduate level. It also contains additional material that can be used as supplements or as introductory material for an advanced course. Because the authors present concepts as intuitive descriptions, a familiarity with basic data structures, computer organization, and a high-level programming language are the only prerequisites. Important theoretical results are covered, but formal proofs are omitted. In place of proofs, figures and examples are used to suggest why a result is true. |
| indexing in data warehouse ppt: Data Mining: Concepts and Techniques Jiawei Han, Micheline Kamber, Jian Pei, 2011-06-09 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 |
| indexing in data warehouse ppt: Introduction to Data Mining Pang-Ning Tan, Michael Steinbach, Vipin Kumar, 2014 Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms. Quotes This book provides a comprehensive coverage of important data mining techniques. Numerous examples are provided to lucidly illustrate the key concepts. |
| indexing in data warehouse ppt: Data Warehousing with the Informix Dynamic Server Chuck Ballard, Veronica Gomes, Gregory Hilz, Manjula Panthagani, Claus Samuelsen, IBM Redbooks, 2009-12-10 The IBM Informix® Dynamic Server (IDS) has the tools to build a powerful data warehouse infrastructure platform to lower costs and increase profits by doing more with your existing operational data and infrastructure. The Informix Warehouse Feature simplifies the process for design and deployment of a high performance data warehouse. With a state-of-the-art extract, load, and transform (ELT) tool and an Eclipse-based GUI environment that is easy to use, this comprehensive platform provides the foundation you need to cost effectively build and deploy the data warehousing infrastructure, using the IBM Informix Dynamic Server, and needed to enable the development and use of next-generation analytic solutions . This IBM® Redbooks® publication describes the technical information and demonstrates the functions and capabilities of the Informix Dynamic Server Warehouse Feature. It can help you understand how to develop a data warehousing architecture and infrastructure to meet your particular requirements, with the Informix Dynamic Server. It can also enable you to transform and manage your operational data, and use it to populate your data warehouse. With that new data warehousing environment, you can support the data analysis and decision-making that are required as you monitor and manage your business processes, and help you meet your business performance management goals, objectives, and measurements. |
| indexing in data warehouse ppt: Data Warehousing Fundamentals Paulraj Ponniah, 2006-07 Market_Desc: · IT professionals· Undergraduate students specializing in information technology· Consultants Special Features: · Includes review questions and exercises· Filled with industry examples· The author has 25 years of experience in IT specializing in data warehousing About The Book: This book explores all topics needed by those who design and implement data warehouses. Readers will learn about planning requirements, architecture, infrastructure, data preparation, information delivery, implementation, and maintenance. This book covers the fundamentals of data warehousing specifically for the IT professionals who wants to get into the field. |
| indexing in data warehouse ppt: Business Periodicals Index , 1998 |
| indexing in data warehouse ppt: Agile Data Warehousing Ralph Hughes, 2008-07-14 Contains a six-stage plan for starting new warehouse projects and guiding programmers step-by-step until they become a world-class, Agile development team. It describes also how to avoid or contain the fierce opposition that radically new methods can encounter from the traditionally-minded IS departments found in many large companies. |
| indexing in data warehouse ppt: Data Mining and Data Warehousing Parteek Bhatia, 2019-06-27 Written in lucid language, this valuable textbook brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Naïve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The textbook is written to cater to the needs of undergraduate students of computer science, engineering and information technology for a course on data mining and data warehousing. The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models and NoSQL are discussed in detail. Pedagogical features including unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding. |
| indexing in data warehouse ppt: MySQL Troubleshooting Sveta Smirnova, 2012-02-15 Stuck with bugs, performance problems, crashes, data corruption, and puzzling output? If you’re a database programmer or DBA, they’re part of your life. The trick is knowing how to quickly recover from them. This unique, example-packed book shows you how to handle an array of vexing problems when working with MySQL. Written by a principal technical support engineer at Oracle, MySQL Troubleshooting provides the background, tools, and expert steps for solving problems from simple to complex—whether data you thought you inserted doesn’t turn up in a query, or the entire database is corrupt because of a server failure. With this book in hand, you’ll work with more confidence. Understand the source of a problem, even when the solution is simple Handle problems that occur when applications run in multiple threads Debug and fix problems caused by configuration options Discover how operating system tuning can affect your server Use troubleshooting techniques specific to replication issues Get a reference to additional troubleshooting techniques and tools, including third-party solutions Learn best practices for safe and effective troubleshooting—and for preventing problems |
| indexing in data warehouse ppt: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| indexing in data warehouse ppt: Real-Time Analytics Byron Ellis, 2014-06-23 Construct a robust end-to-end solution for analyzing and visualizing streaming data Real-time analytics is the hottest topic in data analytics today. In Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data, expert Byron Ellis teaches data analysts technologies to build an effective real-time analytics platform. This platform can then be used to make sense of the constantly changing data that is beginning to outpace traditional batch-based analysis platforms. The author is among a very few leading experts in the field. He has a prestigious background in research, development, analytics, real-time visualization, and Big Data streaming and is uniquely qualified to help you explore this revolutionary field. Moving from a description of the overall analytic architecture of real-time analytics to using specific tools to obtain targeted results, Real-Time Analytics leverages open source and modern commercial tools to construct robust, efficient systems that can provide real-time analysis in a cost-effective manner. The book includes: A deep discussion of streaming data systems and architectures Instructions for analyzing, storing, and delivering streaming data Tips on aggregating data and working with sets Information on data warehousing options and techniques Real-Time Analytics includes in-depth case studies for website analytics, Big Data, visualizing streaming and mobile data, and mining and visualizing operational data flows. The book's recipe layout lets readers quickly learn and implement different techniques. All of the code examples presented in the book, along with their related data sets, are available on the companion website. |
| indexing in data warehouse ppt: Mastering Data Warehouse Design Claudia Imhoff, Nicholas Galemmo, Jonathan G. Geiger, 2003 A cutting-edge response to Ralph Kimball's challenge to the data warehouse community that answers some tough questions about the effectiveness of the relational approach to data warehousing Written by one of the best-known exponents of the Bill Inmon approach to data warehousing Addresses head-on the tough issues raised by Kimball and explains how to choose the best modeling technique for solving common data warehouse design problems Weighs the pros and cons of relational vs. dimensional modeling techniques Focuses on tough modeling problems, including creating and maintaining keys and modeling calendars, hierarchies, transactions, and data quality |
| indexing in data warehouse ppt: The Data Warehouse Toolkit Ralph Kimball, Margy Ross, 2011-08-08 This old edition was published in 2002. The current and final edition of this book is The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition which was published in 2013 under ISBN: 9781118530801. The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including: Retail sales and e-commerce Inventory management Procurement Order management Customer relationship management (CRM) Human resources management Accounting Financial services Telecommunications and utilities Education Transportation Health care and insurance By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts. |
| indexing in data warehouse ppt: Data Mining Techniques Arun K. Pujari, 2001 This Book Addresses All The Major And Latest Techniques Of Data Mining And Data Warehousing. It Deals With The Latest Algorithms For Discussing Association Rules, Decision Trees, Clustering, Neural Networks And Genetic Algorithms. The Book Also Discusses The Mining Of Web Data, Temporal And Text Data. It Can Serve As A Textbook For Students Of Compuer Science, Mathematical Science And Management Science, And Also Be An Excellent Handbook For Researchers In The Area Of Data Mining And Warehousing. |
| indexing in data warehouse ppt: Data Analysis Using SQL and Excel Gordon S. Linoff, 2010-09-16 Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like. |
| indexing in data warehouse ppt: FUNDAMENTALS OF BUSINESS ANALYTICS (With CD ) R. N. Prasad, Seema Acharya, 2011-08 Market_Desc: Primary MarketEngineering (BE/BTech)/ME/MTech students who are interested to develop conceptual level subject knowledge with examples of industrial strength applications.Secondary MarketMCA/MBA/Business users/business analysts Special Features: · Foreword by Prof R Natarajan, Former Chairman, AICTE, Former Director, IIT Madras.· Excellent authorship.· Single source of introductory knowledge on business intelligence (BI).· Provides a good start for first-time learners typically from the engineering and management discipline.· Covers the complete life cycle of BI/Analytics Application development project.· Helps develop deeper understanding of the subject with an enterprise context, and discusses its application in businesses.· Explains concepts with the help of illustrations, application to real-life scenarios and provides opportunities to test understanding.· States the pre-requisites for each chapter and different reference sources available.· In addition the book also has the following pedagogical features:· Industrial application case studies.· Crossword puzzles/do it yourself exercises/assignments to help with self-assessment. The solutions to these have also been provided. · Glossary of terms.· References/web links/bibliography - generally at the end of every concept.CD Companion:To ensure that concepts can be practiced for deeper understanding at low cost, the book is accompanied with a CD containing:· Step-by-step Hands-On manual on:ü An open source tool, Pentaho Data Integrator (PDI) to explain the process of extraction of data from multiple varied sources.ü MS Excel to explain the concept of analysis.ü MS Access to generate reports on the analyzed data.· An integrated project that encompasses the complete life cycle of a BI project. About The Book: The book promises to be a single source of introductory knowledge on business intelligence which can be taught in one semester. It will provide a good start for first time learners typically from the engineering and management discipline. Business Intelligence subject cannot be studied in isolation. The book provides a holistic coverage beginning with an enterprise context, developing deeper understanding through the use of tools, touching a few domains where BI is embraced and discussing the problems that BI can help solve. It covers the complete life cycle of BI/Analytics project: Covering operational/transactional data sources, data transformation, data mart/warehouse design-build, analytical reporting, and dashboards. To ensure that concepts can be practiced for deeper understanding at low cost, the book is accompanied with step-by-step hands-on manual in the CD. |
| indexing in data warehouse ppt: Data Mining and Data Warehousing S. K. Mourya, Shalu Gupta, 2013 Data mining (if you haven't heard of it before), is the Automated Extraction of Hidden Predictive Information from Databases. This book discusses in a step by step approach instructions for the entire data modeling process, with special emphasis on the business knowledge necessary for effective results giving quick introductions to database and data mining concepts with particular emphasis on data analysis followed by concepts and techniques that underlie classification, prediction, association, and clustering. These topics are presented with examples and algorithms for each problem. The Socratic presentation style is both very readable and very informative. The purpose of this book is to serve as a handbook for analysts, data miners, and marketing managers at all levels. |
| indexing in data warehouse ppt: Human Dimension and Interior Space Julius Panero, Martin Zelnik, 2014-01-21 The study of human body measurements on a comparative basis is known as anthropometrics. Its applicability to the design process is seen in the physical fit, or interface, between the human body and the various components of interior space. Human Dimension and Interior Space is the first major anthropometrically based reference book of design standards for use by all those involved with the physical planning and detailing of interiors, including interior designers, architects, furniture designers, builders, industrial designers, and students of design. The use of anthropometric data, although no substitute for good design or sound professional judgment should be viewed as one of the many tools required in the design process. This comprehensive overview of anthropometrics consists of three parts. The first part deals with the theory and application of anthropometrics and includes a special section dealing with physically disabled and elderly people. It provides the designer with the fundamentals of anthropometrics and a basic understanding of how interior design standards are established. The second part contains easy-to-read, illustrated anthropometric tables, which provide the most current data available on human body size, organized by age and percentile groupings. Also included is data relative to the range of joint motion and body sizes of children. The third part contains hundreds of dimensioned drawings, illustrating in plan and section the proper anthropometrically based relationship between user and space. The types of spaces range from residential and commercial to recreational and institutional, and all dimensions include metric conversions. In the Epilogue, the authors challenge the interior design profession, the building industry, and the furniture manufacturer to seriously explore the problem of adjustability in design. They expose the fallacy of designing to accommodate the so-called average man, who, in fact, does not exist. Using government data, including studies prepared by Dr. Howard Stoudt, Dr. Albert Damon, and Dr. Ross McFarland, formerly of the Harvard School of Public Health, and Jean Roberts of the U.S. Public Health Service, Panero and Zelnik have devised a system of interior design reference standards, easily understood through a series of charts and situation drawings. With Human Dimension and Interior Space, these standards are now accessible to all designers of interior environments. |
| indexing in data warehouse ppt: Mining of Massive Datasets Jure Leskovec, Jurij Leskovec, Anand Rajaraman, Jeffrey David Ullman, 2014-11-13 Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets. |
| indexing in data warehouse ppt: Database Administration Craig Mullins, 2002 Giving comprehensive, soup-to-nuts coverage of database administration, this guide is written from a platform-independent viewpoint, emphasizing best practices. |
| indexing in data warehouse ppt: Knowledge Management: Awad, 2003 Knowledge Management is a subset of content taught in the Decision Support Systems course. Knowledge Management is about knowledge and how to capture it, transfer it, share it, and how to manage it. The authors take students through a process-oriented examination of the topic, striking a balance between the behavioral and technical aspects of knowledge management and use it. |
| indexing in data warehouse ppt: INTRODUCTION TO DATA MINING WITH CASE STUDIES GUPTA, G.K., 2014-06-28 The field of data mining provides techniques for automated discovery of valuable information from the accumulated data of computerized operations of enterprises. This book offers a clear and comprehensive introduction to both data mining theory and practice. It is written primarily as a textbook for the students of computer science, management, computer applications, and information technology. The book ensures that the students learn the major data mining techniques even if they do not have a strong mathematical background. The techniques include data pre-processing, association rule mining, supervised classification, cluster analysis, web data mining, search engine query mining, data warehousing and OLAP. To enhance the understanding of the concepts introduced, and to show how the techniques described in the book are used in practice, each chapter is followed by one or two case studies that have been published in scholarly journals. Most case studies deal with real business problems (for example, marketing, e-commerce, CRM). Studying the case studies provides the reader with a greater insight into the data mining techniques. The book also provides many examples, review questions, multiple choice questions, chapter-end exercises and a good list of references and Web resources especially those which are easy to understand and useful for students. A number of class projects have also been included. |
| indexing in data warehouse ppt: Relational Database Index Design and the Optimizers Tapio Lahdenmaki, Mike Leach, 2005-09-15 Improve the performance of relational databases with indexes designed for today's hardware Over the last few years, hardware and software have advanced beyond all recognition, so it's hardly surprising that relational database performance now receives much less attention. Unfortunately, the reality is that the improved hardware hasn't kept pace with the ever-increasing quantity of data processed today. Although disk packing densities have increased enormously, making storage costs extremely low and sequential read very fast, random reads are still painfully slow. Many of the old design recommendations are therefore no longer valid-the optimal point of indexing has come a long way. Consequently many of the old problems haven't actually gone away-they have simply changed their appearance. This book provides an easy but effective approach to the design of indexes and tables. Using lots of examples and case studies, the authors describe how the DB2, Oracle, and SQL Server optimizers determine how to access data, and how CPU and response times for the resulting access paths can be quickly estimated. This enables comparisons to be made of the various designs, and helps you choose available choices for the most appropriate design. This book is intended for anyone who wants to understand the issues of SQL performance or how to design tables and indexes effectively. With this title, readers with many years of experience of relational systems will be able to better grasp the implications that have been brought into play by the introduction of new hardware. |
| indexing in data warehouse ppt: The Purchasing Chessboard Christian Schuh, Joseph L. Raudabaugh, Robert Kromoser, Michael F. Strohmer, Alenka Triplat, 2011-11-27 The approach used on a given spend item should largely depend on the balance between supply power and demand power. That is the logic behind the bestselling Purchasing Chessboard®, used by hundreds of corporations worldwide to reduce costs and increase value with suppliers. The 64 squares in the Purchasing Chessboard provide a rich reservoir of methods that can be applied either individually or combined. And because many of these methods are not customarily used by procurement, the Purchasing Chessboard is also the perfect tool for helping buyers to think and act outside the box and find new solutions. A well-proven concept that works across all industries and all categories in any given situation, it is little wonder that business leaders and procurement professionals alike are excited by, and enjoy strategizing around, the Purchasing Chessboard. This second edition of The Purchasing Chessboard addresses the new realities of a highly volatile economic environment and describes the many—sometimes surprising—ways in which the Purchasing Chessboard is being used in today's business world. Yet despite all of the great achievements of procurement executives and their teams, they do not always receive the recognition they deserve. In response, the authors have developed and outlined within the book an unequivocal approach to measure procurement’s impact on a company’s performance—Return on Supply Management Assets (ROSMA®). |
| indexing in data warehouse ppt: Introduction to Data Mining Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar, 2018-04-13 Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms. |
| indexing in data warehouse ppt: Subsystem and Transaction Monitoring and Tuning with DB2 11 for z/OS Paolo Bruni, Felipe Bortoletto, Adrian Burke, Cathy Drummond, Yasuhiro Ohmori, IBM Redbooks, 2022-08-31 This IBM® Redbooks® publication discusses in detail the facilities of DB2® for z/OS®, which allow complete monitoring of a DB2 environment. It focuses on the use of the DB2 instrumentation facility component (IFC) to provide monitoring of DB2 data and events and includes suggestions for related tuning. We discuss the collection of statistics for the verification of performance of the various components of the DB2 system and accounting for tracking the behavior of the applications. We have intentionally omitted considerations for query optimization; they are worth a separate document. Use this book to activate the right traces to help you monitor the performance of your DB2 system and to tune the various aspects of subsystem and application performance. |
| indexing in data warehouse ppt: SQL Server Query Performance Tuning Distilled Sajal Dam, 2007-03-01 * A completely revised edition of a book that is highly-regarded in the community (as evidenced by Amazon reviews and other customer feedback). * The only comprehensive, practical guide to performance optimization techniques for SQL Server applications. * Essential reading for any DBA or developer resposible for the eprformance of an exisiting SQL Server system, or the design of a new one. |
| indexing in data warehouse ppt: Practical Statistics for Data Scientists Peter Bruce, Andrew Bruce, 2017-05-10 Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data |
| indexing in data warehouse ppt: Smarter Modeling of IBM InfoSphere Master Data Management Solutions Jan-Bernd Bracht, Joerg Rehr, Markus Siebert, Rouven Thimm, IBM Redbooks, 2012-08-09 This IBM® Redbooks® publication presents a development approach for master data management projects, and in particular, those projects based on IBM InfoSphere® MDM Server. The target audience for this book includes Enterprise Architects, Information, Integration and Solution Architects and Designers, Developers, and Product Managers. Master data management combines a set of processes and tools that defines and manages the non-transactional data entities of an organization. Master data management can provide processes for collecting, consolidating, persisting, and distributing this data throughout an organization. IBM InfoSphere Master Data Management Server creates trusted views of master data that can improve applications and business processes. You can use it to gain control over business information by managing and maintaining a complete and accurate view of master data. You also can use InfoSphere MDM Server to extract maximum value from master data by centralizing multiple data domains. InfoSphere MDM Server provides a comprehensive set of prebuilt business services that support a full range of master data management functionality. |
| indexing in data warehouse ppt: Smarter Business: Dynamic Information with IBM InfoSphere Data Replication CDC Chuck Ballard, Alec Beaton, Mark Ketchie, Frank Ketelaars, Anzar Noor, Judy Parkes, Deepak Rangarao, Bill Shubin, Wim Van Tichelen, IBM Redbooks, 2012-03-12 To make better informed business decisions, better serve clients, and increase operational efficiencies, you must be aware of changes to key data as they occur. In addition, you must enable the immediate delivery of this information to the people and processes that need to act upon it. This ability to sense and respond to data changes is fundamental to dynamic warehousing, master data management, and many other key initiatives. A major challenge in providing this type of environment is determining how to tie all the independent systems together and process the immense data flow requirements. IBM® InfoSphere® Change Data Capture (InfoSphere CDC) can respond to that challenge, providing programming-free data integration, and eliminating redundant data transfer, to minimize the impact on production systems. In this IBM Redbooks® publication, we show you examples of how InfoSphere CDC can be used to implement integrated systems, to keep those systems updated immediately as changes occur, and to use your existing infrastructure and scale up as your workload grows. InfoSphere CDC can also enhance your investment in other software, such as IBM DataStage® and IBM QualityStage®, IBM InfoSphere Warehouse, and IBM InfoSphere Master Data Management Server, enabling real-time and event-driven processes. Enable the integration of your critical data and make it immediately available as your business needs it. |
| indexing in data warehouse ppt: Data Science from Scratch Joel Grus, 2015-04-14 This is a first-principles-based, practical introduction to the fundamentals of data science aimed at the mathematically-comfortable reader with some programming skills. The book covers: The important parts of Python to know The important parts of Math / Probability / Statistics to know The basics of data science How commonly-used data science techniques work (learning by implementing them) What is Map-Reduce and how to do it in Python Other applications such as NLP, Network Analysis, and more. |
| indexing in data warehouse ppt: F&S Index United States Annual , 1999 |
| indexing in data warehouse ppt: Valuepack Thomas Connolly, 2005-08-01 |
| indexing in data warehouse ppt: Data Mining: Introductory and Advanced Topics Dunham, Sridhar, In this book the author provides the reader with a comprehensive coverage of data mining topics and algorithms. Data base perspective is maintained throughout the book which provides students with a focused discussion of algorithms, data structures, data types and complexity of algorithms and space. It also emphasizes the use of data mining concepts in real-world applications with large database components. |
| indexing in data warehouse ppt: Data Mining with Rattle and R Graham Williams, 2011-02-25 Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings. |
| indexing in data warehouse ppt: An Introduction to Database Systems C. J. Date, 2000 For over 25 years, C. J. Dates An Introduction to Database Systems has been the authoritative resource for readers interested in gaining insight into and understanding of the principles of database systems. This exciting revision continues to provide a solid grounding in the foundations of database technology and to provide some ideas as to how the field is likely to develop in the future. The material is organized into six major parts. Part I provides a broad introduction to the concepts of database systems in general and relational systems in particular. Part II consists of a careful description of the relational model, which is the theoretical foundation for the database field as a whole. Part III discusses the general theory of database design. Part IV is concerned with transaction management. Part V shows how relational concepts are relevant to a variety of further aspects of database technology-security, distributed databases, temporal data, decision support, and so on. Finally, Part VI describes the impact of object technology on database systems. This Seventh Edition of An Introduction to Database Systems features widely rewritten material to improve and amplify treatment o |
| indexing in data warehouse ppt: Database Management Systems Raghu Ramakrishnan, Johannes Gehrke, 2017 Database Management Systems (DBMS) is a must for any course in database systems or file organization. DBMS provides a hands-on approach to relational database systems, with an emphasis on practical topics such as indexing methods, SQL, and database design. New to this edition are the early coverage of the ER model, new chapters on Internet databases, data mining, and spatial databases, and a new supplement on practical SQL assignments (with solutions for instructors' use). Many other chapters have been reorganized or expanded to provide up-to-date coverage.--Jacket. |
| indexing in data warehouse ppt: Data Integration for Statewide Transportation Planning Jessica Guo, Sasanka Gandavarapu, 2010 |
| indexing in data warehouse ppt: Modern Database Management Fred R. McFadden, Jeffrey A. Hoffer, Mary B. Prescott, 1998 The fifth edition of Modern Database Management has been updated to reflect the most current database content available. It provides sound, clear, and current coverage of the concepts, skills, and issues needed to cope with an expanding organizational resource. While sufficient technical detail is provided, the emphasis remains on management and implementation issues pertinent in a business information systems curriculum. Modern Database Management, 5e is the ideal book for your database management course. *Includes coverage of today's leading database technologies: Oracle and Microsoft Access replace dBase and paradox. *Now organized to create a modern framework for a range of databases and the database development of information systems. *Expanded coverage of object-oriented techniques in two full chapters. Covers conceptual object-oriented modelling using the new Unified Modelling Language and object-oriented database development and querying using the latest ODMG standards. *Restructured to emphasize unique database issues that arise during the design of client/server applications. *Updated to reflect current developments in client/server issues including three-tiered architect |
sql - How does database indexing work? - Stack Overflow
Indexing on a field with a cardinality of 2 would split the data in half, whereas a cardinality of 1,000 would return approximately 1,000 records. With such a low cardinality the effectiveness is …
Enable or Disable Advanced Indexing Options in Windows
Aug 28, 2020 · How to Turn On or Off Indexing Contents and Properties of Files on a Drive in Windows; Disable Adding Locations on Removable Drives to Index and Libraries in Windows …
indexing - What is an index in SQL? - Stack Overflow
Jun 2, 2010 · So, How indexing actually works? Well, first off, the database table does not reorder itself when we put index on a column to optimize the query performance. An index is a data …
Enable or Disable Search Indexing in Windows | Tutorials - Ten …
Oct 6, 2020 · How to Enable or Disable Search Indexing in Windows Indexing the contents of your PC helps you get faster results when you're searching it for files and other things. Indexing is …
sql - How to use index in select statement? - Stack Overflow
Jul 6, 2011 · Indexing for SQL statement. 0. Indexing in SQL Select Statement. 0. How to optimize select query with ...
MySQL indexes - what are the best practices? - Stack Overflow
So, in general, indexing creates a tradeoff between read efficiency and write efficiency. With no indexes, inserts can be very fast -- the database engine just adds a row to the table. As you …
python - How to index into a dictionary? - Stack Overflow
Dictionaries are unordered in Python versions up to and including Python 3.6. If you do not care about the order of the entries and want to access the keys or values by index anyway, you can …
indexing - Why are array indexes zero-based in most …
Jul 16, 2024 · @nawfal : in Mercury there is no indexing in usual sense so you are free to decide the index base yourself. In the context of the question it makes therefore sense to indicate the …
indexing - How do MySQL indexes work? - Stack Overflow
Aug 25, 2010 · Indexing adds a data structure with columns for the search conditions and a pointer. The pointer is the address on the memory disk of the row with the rest of the …
indexing - How do I find the index of a character within a string in …
Jul 10, 2010 · Suppose I have a string "qwerty" and I wish to find the index position of the e character in it. (In this case the index would be 2) How do I do it in C? I found the strchr …
sql - How does database indexing work? - Stack Overflow
Indexing on a field with a cardinality of 2 would split the data in half, whereas a cardinality of 1,000 would return approximately 1,000 records. With such a low cardinality the effectiveness is …
Enable or Disable Advanced Indexing Options in Windows
Aug 28, 2020 · How to Turn On or Off Indexing Contents and Properties of Files on a Drive in Windows; Disable Adding Locations on Removable Drives to Index and Libraries in Windows 10; …
indexing - What is an index in SQL? - Stack Overflow
Jun 2, 2010 · So, How indexing actually works? Well, first off, the database table does not reorder itself when we put index on a column to optimize the query performance. An index is a data …
Enable or Disable Search Indexing in Windows | Tutorials - Ten …
Oct 6, 2020 · How to Enable or Disable Search Indexing in Windows Indexing the contents of your PC helps you get faster results when you're searching it for files and other things. Indexing is the …
sql - How to use index in select statement? - Stack Overflow
Jul 6, 2011 · Indexing for SQL statement. 0. Indexing in SQL Select Statement. 0. How to optimize select query with ...
MySQL indexes - what are the best practices? - Stack Overflow
So, in general, indexing creates a tradeoff between read efficiency and write efficiency. With no indexes, inserts can be very fast -- the database engine just adds a row to the table. As you add …
python - How to index into a dictionary? - Stack Overflow
Dictionaries are unordered in Python versions up to and including Python 3.6. If you do not care about the order of the entries and want to access the keys or values by index anyway, you can …
indexing - Why are array indexes zero-based in most programming ...
Jul 16, 2024 · @nawfal : in Mercury there is no indexing in usual sense so you are free to decide the index base yourself. In the context of the question it makes therefore sense to indicate the …
indexing - How do MySQL indexes work? - Stack Overflow
Aug 25, 2010 · Indexing adds a data structure with columns for the search conditions and a pointer. The pointer is the address on the memory disk of the row with the rest of the information. The …
indexing - How do I find the index of a character within a string in C ...
Jul 10, 2010 · Suppose I have a string "qwerty" and I wish to find the index position of the e character in it. (In this case the index would be 2) How do I do it in C? I found the strchr function …
