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| data warehouse optimization techniques: Data Warehousing Rob Botwright, 101-01-01 Introducing the Data Warehousing: Optimizing Data Storage and Retrieval for Business Success bundle! Unlock the full potential of your data with this comprehensive collection of four essential books: 1. Data Warehousing Fundamentals: A Beginner's Guide · Dive into the foundational principles of data warehousing and learn how to build a solid framework for storing and managing your organization's data. · Understand the importance of data modeling and gain insights into the extraction, transformation, and loading (ETL) processes essential for efficient data management. 2. Mastering Data Modeling for Data Warehousing · Take your data modeling skills to the next level with advanced techniques for conceptual, logical, and dimensional modeling. · Learn how to design scalable and efficient data warehouses that meet the evolving needs of your organization. 3. Advanced ETL Techniques for Data Warehousing Optimization · Optimize your ETL processes and streamline data extraction, transformation, and loading for maximum efficiency. · Explore advanced techniques such as incremental loading and change data capture (CDC) to ensure the smooth operation of your data warehouse. 4. Big Data Analytics: Harnessing the Power of Data Warehousing for Experts · Unlock the transformative potential of big data analytics and gain actionable insights to drive informed decision-making. · Discover how to leverage your data warehouse for real-time data processing, predictive modeling, and more. With this bundle, you'll gain the knowledge and skills needed to optimize your data storage and retrieval processes, empowering you to harness the power of data for business success. Whether you're a beginner looking to build a solid foundation or an expert seeking advanced strategies, this bundle has something for everyone. Don't miss out on this opportunity to revolutionize your approach to data warehousing and take your business to new heights! |
| data warehouse optimization techniques: ADVANCED DATA WAREHOUSING STRATEGIES: BUILDING SCALABLE AND HIGH-PERFORMANCE DATA STORAGE SOLUTIONS ROHAN VISWANATHA PRASAD SWATHI GARUDASU SMITA RAGHAVENDRA BHAT PROF. (DR) PUNIT GOEL, 2024-11-09 In the ever-evolving landscape of the modern world, the synergy between technology and management has become a cornerstone of innovation and progress. This book, Advanced Data Warehousing Strategies: Building Scalable and High-Performance Data Storage Solutions, is conceived to bridge the gap between emerging technological advancements in data warehousing and their strategic application in building efficient, scalable, and high-performance data storage systems. Our objective is to equip readers with the tools and insights necessary to excel in this dynamic intersection of fields. This book is structured to provide a comprehensive exploration of the methodologies and strategies that define the innovation of data warehousing technologies, particularly focusing on techniques and applications relevant to modern data storage solutions. From foundational theories to advanced applications, we delve into the critical aspects that drive successful innovation in large-scale data systems. We have made a concerted effort to present complex concepts in a clear and accessible manner, making this work suitable for a diverse audience, including students, developers, and industry professionals. In authoring this book, we have drawn upon the latest research and best practices to ensure that readers not only gain a robust theoretical understanding but also acquire practical skills that can be applied in real-world data warehousing scenarios. The chapters are designed to strike a balance between depth and breadth, covering topics ranging from data warehousing fundamentals and optimization techniques to the strategic management of scalable storage systems. Additionally, we emphasize the importance of high performance and data integrity, dedicating sections to the art of developing data solutions that deliver efficiency, scalability, and resilience. The inspiration for this book arises from a recognition of the crucial role that data warehousing systems play in shaping the future of digital interactions and business intelligence. We are profoundly grateful to Chancellor Shri Shiv Kumar Gupta of Maharaja Agrasen Himalayan Garhwal University for his unwavering support and vision. His dedication to fostering academic excellence and promoting a culture of innovation has been instrumental in bringing this project to fruition. We hope this book will serve as a valuable resource and inspiration for those eager to deepen their understanding of how data warehousing strategies can be harnessed to drive innovation. We believe that the knowledge and insights contained within these pages will empower readers to lead the way in creating high-performance data storage solutions that will define the future of enterprise data management. Thank you for joining us on this journey. Authors |
| data warehouse optimization techniques: Enterprise Data Warehouse Optimization with Hadoop on IBM Power Systems Servers Scott Vetter, Helen Lu, Maciej Olejniczak, IBM Redbooks, 2018-01-31 Data warehouses were developed for many good reasons, such as providing quick query and reporting for business operations, and business performance. However, over the years, due to the explosion of applications and data volume, many existing data warehouses have become difficult to manage. Extract, Transform, and Load (ETL) processes are taking longer, missing their allocated batch windows. In addition, data types that are required for business analysis have expanded from structured data to unstructured data. The Apache open source Hadoop platform provides a great alternative for solving these problems. IBM® has committed to open source since the early years of open Linux. IBM and Hortonworks together are committed to Apache open source software more than any other company. IBM Power SystemsTM servers are built with open technologies and are designed for mission-critical data applications. Power Systems servers use technology from the OpenPOWER Foundation, an open technology infrastructure that uses the IBM POWER® architecture to help meet the evolving needs of big data applications. The combination of Power Systems with Hortonworks Data Platform (HDP) provides users with a highly efficient platform that provides leadership performance for big data workloads such as Hadoop and Spark. This IBM RedpaperTM publication provides details about Enterprise Data Warehouse (EDW) optimization with Hadoop on Power Systems. Many people know Power Systems from the IBM AIX® platform, but might not be familiar with IBM PowerLinuxTM, so part of this paper provides a Power Systems overview. A quick introduction to Hadoop is provided for those not familiar with the topic. Details of HDP on Power Reference architecture are included that will help both software architects and infrastructure architects understand the design. In the optimization chapter, we describe various topics: traditional EDW offload, sizing guidelines, performance tuning, IBM Elastic StorageTM Server (ESS) for data-intensive workload, IBM Big SQL as the common structured query language (SQL) engine for Hadoop platform, and tools that are available on Power Systems that are related to EDW optimization. We also dedicate some pages to the analytics components (IBM Data Science Experience (IBM DSX) and IBM SpectrumTM Conductor for Spark workload) for the Hadoop infrastructure. |
| data warehouse optimization techniques: Encyclopedia of Database Systems Ling Liu, M. Tamer Özsu, |
| data warehouse optimization techniques: Fundamentals of Data Warehouses Matthias Jarke, Maurizio Lenzerini, Yannis Vassiliou, Panos Vassiliadis, 2013-03-09 This book presents the first comparative review of the state of the art and the best current practices of data warehouses. It covers source and data integration, multidimensional aggregation, query optimization, metadata management, quality assessment, and design optimization. A conceptual framework is presented by which the architecture and quality of a data warehouse can be assessed and improved using enriched metadata management combined with advanced techniques from databases, business modeling, and artificial intelligence. |
| data warehouse optimization techniques: New Trends in Data Warehousing and Data Analysis Stanislaw Kozielski, Robert Wrembel, 2008-10-23 Most of modern enterprises, institutions, and organizations rely on knowledge-based management systems. In these systems, knowledge is gained from data analysis. Today, knowledge-based management systems include data warehouses as their core components. Data integrated in a data warehouse are analyzed by the so-called On-Line Analytical Processing (OLAP) applications designed to discover trends, patterns of behavior, and anomalies as well as finding dependencies between data. Massive amounts of integrated data and the complexity of integrated data coming from many different sources make data integration and processing challenging. New Trends in Data Warehousing and Data Analysis brings together the most recent research and practical achievements in the DW and OLAP technologies. It provides an up-to-date bibliography of published works and the resource of research achievements. Finally, the book assists in the dissemination of knowledge in the field of advanced DW and OLAP. |
| data warehouse optimization techniques: Data Warehouses and OLAP Robert Wrembel, Christian Koncilia, 2007-01-01 Data warehouses and online analytical processing (OLAP) are emerging key technologies for enterprise decision support systems. They provide sophisticated technologies from data integration, data collection and retrieval, query optimization, and data analysis to advanced user interfaces. New research and technological achievements in the area of data warehousing are implemented in commercial database management systems, and organizations are developing data warehouse systems into their information system infrastructures. Data Warehouses and OLAP: Concepts, Architectures and Solutions covers a wide range of technical, technological, and research issues. It provides theoretical frameworks, presents challenges and their possible solutions, and examines the latest empirical research findings in the area. It is a resource of possible solutions and technologies that can be applied when designing, implementing, and deploying a data warehouse, and assists in the dissemination of knowledge in this field. |
| data warehouse optimization techniques: Business Intelligence Marie-Aude Aufaure, Esteban Zimányi, 2012-01-16 Business Intelligence (BI) promises an organization the capability of collecting and analyzing internal and external data to generate knowledge and value, providing decision support at the strategic, tactical, and operational levels. Business Intelligence is now impacted by the Big Data phenomena and the evolution of society and users, and needs to take into account high-level semantics, reasoning about unstructured and structured data, and to provide a simplified access and better understanding of diverse BI tools accessible trough mobile devices. In particular, BI applications must cope with additional heterogeneous (often Web-based) sources, e.g., from social networks, blogs, competitors’, suppliers’, or distributors’ data, governmental or NGO-based analysis and papers, or from research publications. The lectures held at the First European Business Intelligence Summer School (eBISS), which are presented here in an extended and refined format, cover not only established BI technologies like data warehouses, OLAP query processing, or performance issues, but extend into new aspects that are important in this new environment and for novel applications, e.g., semantic technologies, social network analysis and graphs, services, large-scale management, or collaborative decision making. Combining papers by leading researchers in the field, this volume will equip the reader with the state-of-the-art background necessary for inventing the future of BI. It will also provide the reader with an excellent basis and many pointers for further research in this growing field. |
| data warehouse optimization techniques: Progressive Methods in Data Warehousing and Business Intelligence: Concepts and Competitive Analytics Taniar, David, 2009-02-28 Provides developments and research, as well as current innovative activities in data warehousing and mining, focusing on the intersection of data warehousing and business intelligence. |
| data warehouse optimization techniques: Fundamentals of Data Warehouses Matthias Jarke, Maurizio Lenzerini, Yannis Vassiliou, Panos Vassiliadis, 2013-03-09 The first comparative review of the state of the art and best current practice in data warehousing. It covers source and data integration, multidimensional aggregation, query optimisation, update propagation, metadata management, quality assessment, and design optimisation. Also, based on results of the European DWQ project, it offers a conceptual framework by which the architecture and quality of data warehousing efforts can be assessed and improved using enriched metadata management combined with advanced techniques from databases, business modelling, and artificial intelligence. An excellent introduction to the issues of quality and metadata usage for researchers and database professionals in academia and industry. XXXXXXX Neuer Text This book presents the first comparative review of the state-of-the-art and the best current practices of data warehouses. It covers source and data integration, multidimensional aggregation, query optimization, metadata management, quality assessment, and design optimization. A conceptual framework is presented by which the architecture and quality of a data warehouse can be assessed and improved using enriched metadata management combined with advanced techniques from databases, business modeling, and artificial intelligence. |
| data warehouse optimization techniques: Mastering Data Warehousing Cybellium Ltd, Architect, Build, and Optimize Your Data Warehouse Are you ready to revolutionize the way your organization stores and accesses data? Mastering Data Warehousing is your definitive guide to architecting, building, and optimizing data warehouses that facilitate efficient data storage and retrieval. Whether you're a data architect designing robust warehouse structures or a business leader aiming to glean insights from your data, this book equips you with the knowledge and strategies to master the art of data warehousing. Key Features: 1. Architecting Data Warehouses: Immerse yourself in the world of data warehousing, understanding its significance, challenges, and opportunities. Build a strong foundation that empowers you to design data warehouses that cater to your organization's needs. 2. Data Warehouse Models: Master various data warehouse models. Learn about star schema, snowflake schema, and other dimensional modeling techniques for organizing data for efficient querying and analysis. 3. Data ETL (Extract, Transform, Load): Uncover the power of ETL processes in data warehousing. Explore techniques for extracting data from diverse sources, transforming it for analysis, and loading it into your warehouse. 4. Data Quality and Governance: Delve into data quality and governance within data warehousing. Learn how to ensure data accuracy, consistency, and compliance within your warehouse. 5. Optimizing Query Performance: Master techniques for optimizing query performance. Learn about indexing, partitioning, and materialized views to enhance query speed and responsiveness. 6. Scalability and High Availability: Explore strategies for scaling and ensuring high availability of your data warehouse. Learn how to handle growing data volumes and ensure uninterrupted access to critical information. 7. Cloud Data Warehousing: Discover the world of cloud data warehousing. Learn about designing and migrating data warehouses to cloud platforms, enabling scalability and cost-efficiency. 8. Data Warehousing Tools and Platforms: Uncover a range of tools and platforms for data warehousing. Explore traditional solutions as well as modern technologies like columnar databases and data lakes. 9. Real-Time Data Warehousing: Dive into real-time data warehousing techniques. Learn how to capture and process streaming data for instant insights and decision-making. 10. Real-World Applications: Gain insights into real-world use cases of data warehousing across industries. From business intelligence to customer analytics, discover how organizations leverage data warehouses for strategic advantage. Who This Book Is For: Mastering Data Warehousing is an essential resource for data architects, analysts, and business professionals aiming to excel in designing and managing data warehouses. Whether you're enhancing your technical skills or transforming data into actionable insights, this book will guide you through the intricacies and empower you to harness the full potential of data warehousing. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com |
| data warehouse optimization techniques: Mastering Data Warehouse Design Claudia Imhoff, Nicholas Galemmo, Jonathan G. Geiger, 2003-08-19 A cutting-edge response to Ralph Kimball's challenge to thedata warehouse community that answers some tough questions aboutthe effectiveness of the relational approach to datawarehousing Written by one of the best-known exponents of the Bill Inmonapproach to data warehousing Addresses head-on the tough issues raised by Kimball andexplains how to choose the best modeling technique for solvingcommon data warehouse design problems Weighs the pros and cons of relational vs. dimensional modelingtechniques Focuses on tough modeling problems, including creating andmaintaining keys and modeling calendars, hierarchies, transactions,and data quality |
| data warehouse optimization techniques: Data Warehousing For Dummies Thomas C. Hammergren, 2009-04-13 Data warehousing is one of the hottest business topics, and there’s more to understanding data warehousing technologies than you might think. Find out the basics of data warehousing and how it facilitates data mining and business intelligence with Data Warehousing For Dummies, 2nd Edition. Data is probably your company’s most important asset, so your data warehouse should serve your needs. The fully updated Second Edition of Data Warehousing For Dummies helps you understand, develop, implement, and use data warehouses, and offers a sneak peek into their future. You’ll learn to: Analyze top-down and bottom-up data warehouse designs Understand the structure and technologies of data warehouses, operational data stores, and data marts Choose your project team and apply best development practices to your data warehousing projects Implement a data warehouse, step by step, and involve end-users in the process Review and upgrade existing data storage to make it serve your needs Comprehend OLAP, column-wise databases, hardware assisted databases, and middleware Use data mining intelligently and find what you need Make informed choices about consultants and data warehousing products Data Warehousing For Dummies, 2nd Edition also shows you how to involve users in the testing process and gain valuable feedback, what it takes to successfully manage a data warehouse project, and how to tell if your project is on track. You’ll find it’s the most useful source of data on the topic! |
| data warehouse optimization techniques: Data Warehousing and Knowledge Discovery A Min Tjoa, 2005-09-09 For more than a decade, data warehousing and knowledge discovery technologies have been developing into key technologies for decision-making processes in com- nies. Since 1999, due to the relevant role of these technologies in academia and ind- try, the Data Warehousing and Knowledge Discovery (DaWaK) conference series have become an international forum where both practitioners and researchers share their findings, publish their relevant results and dispute in depth research issues and experiences on data warehousing and knowledge discovery systems and applications. The 7th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2005) continued series of successful conferences dedicated to these topics. In this edition, the conference tried to provide the right, logical balance between data warehousing and knowledge discovery. Regarding data warehousing, papers cover different relevant and still unsolved research problems, such as the modelling of ETL processes and integration problems, designing OLAP technologies from XML do- ments, modelling data warehouses and data mining applications together, impro- ments in query processing, partitioning and implementations. With regard to data mining, a variety of papers were presented on subjects including data mining te- niques, clustering, classification, text documents and classification, and patterns. These proceedings contain the technical papers that were selected for presentation at the conference. We received 196 abstracts, and finally received 162 papers from 38 countries, and the Program Committee eventually selected 51 papers, making an acceptance rate of 31.4 % of submitted papers. |
| data warehouse optimization techniques: Data Warehouse Requirements Engineering Naveen Prakash, Deepika Prakash, 2018-01-29 As the first to focus on the issue of Data Warehouse Requirements Engineering, this book introduces a model-driven requirements process used to identify requirements granules and incrementally develop data warehouse fragments. In addition, it presents an approach to the pair-wise integration of requirements granules for consolidating multiple data warehouse fragments. The process is systematic and does away with the fuzziness associated with existing techniques. Thus, consolidation is treated as a requirements engineering issue. The notion of a decision occupies a central position in the decision-based approach. On one hand, information relevant to a decision must be elicited from stakeholders; modeled; and transformed into multi-dimensional form. On the other, decisions themselves are to be obtained from decision applications. For the former, the authors introduce a suite of information elicitation techniques specific to data warehousing. This information is subsequently converted into multi-dimensional form. For the latter, not only are decisions obtained from decision applications for managing operational businesses, but also from applications for formulating business policies and for defining rules for enforcing policies, respectively. In this context, the book presents a broad range of models, tools and techniques. For readers from academia, the book identifies the scientific/technological problems it addresses and provides cogent arguments for the proposed solutions; for readers from industry, it presents an approach for ensuring that the product meets its requirements while ensuring low lead times in delivery. |
| data warehouse optimization techniques: Encyclopedia of Data Warehousing and Mining, Second Edition Wang, John, 2008-08-31 There are more than one billion documents on the Web, with the count continually rising at a pace of over one million new documents per day. As information increases, the motivation and interest in data warehousing and mining research and practice remains high in organizational interest. The Encyclopedia of Data Warehousing and Mining, Second Edition, offers thorough exposure to the issues of importance in the rapidly changing field of data warehousing and mining. This essential reference source informs decision makers, problem solvers, and data mining specialists in business, academia, government, and other settings with over 300 entries on theories, methodologies, functionalities, and applications. |
| data warehouse optimization techniques: Data Warehousing and Knowledge Discovery Yahiko Kambayashi, Mukesh Mohania, Wolfram Wöß, 2004-08-18 This book constitutes the refereed proceedings of the 6th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2004, held in Zaragoza, Spain, in September 2004. The 40 revised full papers presented were carefully reviewed and selected from over 100 submissions. The papers are organized in topical sections on data warehouse design; knowledge discovery framework and XML data mining, data cubes and queries; multidimensional schema and data aggregation; inductive databases and temporal rules; industrial applications; data clustering; data visualization and exploration; data classification, extraction, and interpretation; data semantics, association rule mining; event sequence mining; and pattern mining. |
| data warehouse optimization techniques: Data Warehouse Performance W. H. Inmon, 1998-11-13 Reduce operating and maintenance costs while substantially improving the performance of new and existing data warehouses and data marts Data Warehouse Performance This book tells you what you need to know to design, build, and manage data warehouses and data marts for optimum performance. Written by an all-star team of data warehouse pioneers and innovators-including Bill Inmon, the father of the data warehouse, and Ken Rudin, one of the leading experts on performance-the book describes the layers of a high-performance data warehouse environment and guides the reader through their implementation and management. It also supplies proven techniques for supercharging the performance of existing environments. Crucial topics covered include: * Mitigating the impact of dormant data on performance * Data cleansing and implementation techniques * Implementing platform components like data marts to support scalability * Database design, sizing, and optimization techniques, including star schema and indexing * Hardware assessment, selection, and sizing * The role of monitors in balancing workload and assessing performance * Creating a service management contract to meet user expectations |
| data warehouse optimization techniques: Data Warehousing and Knowledge Discovery Mukesh K. Mohania, A Min Tjoa, 2009-08-28 Data warehousing and knowledge discovery are increasingly becoming mission-critical technologies for most organizations, both commercial and public, as it becomes incre- ingly important to derive important knowledge from both internal and external data sources. With the ever growing amount and complexity of the data and information available for decision making, the process of data integration, analysis, and knowledge discovery continues to meet new challenges, leading to a wealth of new and exciting research challenges within the area. Over the last decade, the International Conference on Data Warehousing and Knowledge Discovery (DaWaK) has established itself as one of the most important international scientific events within data warehousing and knowledge discovery. DaWaK brings together a wide range of researchers and practitioners working on these topics. The DaWaK conference series thus serves as a leading forum for discu- ing novel research results and experiences within data warehousing and knowledge th discovery. This year’s conference, the 11 International Conference on Data Wa- housing and Knowledge Discovery (DaWaK 2009), continued the tradition by d- seminating and discussing innovative models, methods, algorithms, and solutions to the challenges faced by data warehousing and knowledge discovery technologies. |
| data warehouse optimization techniques: International Conference on Applications and Techniques in Cyber Security and Intelligence Jemal Abawajy, Kim-Kwang Raymond Choo, Rafiqul Islam, 2017-10-20 This book presents the outcomes of the 2017 International Conference on Applications and Techniques in Cyber Security and Intelligence, which focused on all aspects of techniques and applications in cyber and electronic security and intelligence research. The conference provides a forum for presenting and discussing innovative ideas, cutting-edge research findings, and novel techniques, methods and applications on all aspects of cyber and electronic security and intelligence. |
| data warehouse optimization techniques: Pro SQL Server Internals Dmitri Korotkevitch, 2016-11-29 Improve your ability to develop, manage, and troubleshoot SQL Server solutions by learning how different components work “under the hood,” and how they communicate with each other. The detailed knowledge helps in implementing and maintaining high-throughput databases critical to your business and its customers. You’ll learn how to identify the root cause of each problem and understand how different design and implementation decisions affect performance of your systems. New in this second edition is coverage of SQL Server 2016 Internals, including In-Memory OLTP, columnstore enhancements, Operational Analytics support, Query Store, JSON, temporal tables, stretch databases, security features, and other improvements in the new SQL Server version. The knowledge also can be applied to Microsoft Azure SQL Databases that share the same code with SQL Server 2016. Pro SQL Server Internals is a book for developers and database administrators, and it covers multiple SQL Server versions starting with SQL Server 2005 and going all the way up to the recently released SQL Server 2016. The book provides a solid road map for understanding the depth and power of the SQL Server database server and teaches how to get the most from the platform and keep your databases running at the level needed to support your business. The book: • Provides detailed knowledge of new SQL Server 2016 features and enhancements • Includes revamped coverage of columnstore indexes and In-Memory OLTP • Covers indexing and transaction strategies • Shows how various database objects and technologies are implemented internally, and when they should or should not be used • Demonstrates how SQL Server executes queries and works with data and transaction log What You Will Learn Design and develop database solutions with SQL Server. Troubleshoot design, concurrency, and performance issues. Choose the right database objects and technologies for the job. Reduce costs and improve availability and manageability. Design disaster recovery and high-availability strategies. Improve performance of OLTP and data warehouse systems through in-memory OLTP and Columnstore indexes. Who This Book Is For Developers and database administrators who want to design, develop, and maintain systems in a way that gets the most from SQL Server. This book is an excellent choice for people who prefer to understand and fix the root cause of a problem rather than applying a 'band aid' to it. |
| data warehouse optimization techniques: Oracle Data Warehousing and Business Intelligence Solutions Robert Stackowiak, Joseph Rayman, Rick Greenwald, 2007-01-06 Up-to-date, comprehensive coverage of the Oracle database and business intelligence tools Written by a team of Oracle insiders, this authoritative book provides you with the most current coverage of the Oracle data warehousing platform as well as the full suite of business intelligence tools. You'll learn how to leverage Oracle features and how those features can be used to provide solutions to a variety of needs and demands. Plus, you'll get valuable tips and insight based on the authors' real-world experiences and their own implementations. Avoid many common pitfalls while learning best practices for: Leveraging Oracle technologies to design, build, and manage data warehouses Integrating specific database and business intelligence solutions from other vendors Using the new suite of Oracle business intelligence tools to analyze data for marketing, sales, and more Handling typical data warehouse performance challenges Uncovering initiatives by your business community, security business sponsorship, project staffing, and managing risk |
| data warehouse optimization techniques: Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications Nguyen, Tho Manh, 2009-07-31 Recently, researchers have focused on challenging problems facing the development of data warehousing, knowledge discovery, and data mining applications. |
| data warehouse optimization techniques: DATABASE MANAGEMENT SYSTEM ORACLE SQL AND PL/SQL PRANAB KUMAR DAS GUPTA, P. RADHA KRISHNA, 2013-10-18 Database Management System (DBMS) and Oracle are essentially a part of the curriculum for undergraduate and postgraduate courses in Computer Science, Computer Applications, Computer Science and Engineering, Information Technology and Management. The book is organized into three parts to introduce the theoretical and programming concepts of DBMS. Part I (Basic Concepts and Oracle SQL) deals with DBMS basic, software analysis and design, data flow diagram, ER model, relational algebra, normal forms, SQL queries, functions, subqueries, different types of joins, DCL, DDL, DML, object constraints and security in Oracle. Part II (Application Using Oracle PL/SQL) explains PL/SQL basics, functions, procedures, packages, exception handling, triggers, implicit, explicit and advanced cursors using suitable examples. This part also covers advanced concepts related to PL/SQL, such as collection, records, objects, dynamic SQL and performance tuning. Part III (Advanced Concepts and Technologies) elaborates on advanced database concepts such as query processing, file organization, distributed architecture, backup, recovery, data warehousing, online analytical processing and data mining concepts and their techniques. All the chapters include a large number of examples. To further reinforce the concepts, numerous objective type questions and workouts are provided at the end of each chapter. Key Features • Explains each topic in a step-by-step detail.• Includes about 300 examples to illustrate the concepts. • Offers about 400 objective type questions to quiz students on key points.• Provides about 100 challenging workouts that invite deeper analysis and interpretation of the subject matter. New to the Second Edition • The book reorganized into three parts for better understanding of DBMS concepts.• All the existing chapters thoroughly revised and eight new chapters added.• New chapters discuss Oracle PL/SQL advanced programming concepts, data warehousing, OLTP, OLAP and data mining concepts.• Additional examples, questions and workouts in each chapter. TEACHING AID MATERIAL Teaching Aid Material for all the chapters is provided on the website of PHI Learning, which can be used by the faculties/teachers for delivering lectures. Visit www.phindia.com/gupta to explore the contents. |
| data warehouse optimization techniques: Hyper-lattice Algebraic Model for Data Warehousing Soumya Sen, Agostino Cortesi, Nabendu Chaki, 2016-01-05 This book presents Hyper-lattice, a new algebraic model for partially ordered sets, and an alternative to lattice. The authors analyze some of the shortcomings of conventional lattice structure and propose a novel algebraic structure in the form of Hyper-lattice to overcome problems with lattice. They establish how Hyper-lattice supports dynamic insertion of elements in a partial order set with a partial hierarchy between the set members. The authors present the characteristics and the different properties, showing how propositions and lemmas formalize Hyper-lattice as a new algebraic structure. |
| data warehouse optimization techniques: Flexible Query Answering Systems Henrik L. Larsen, Januss Kacprzyk, Slawomir Zadrozny, Troels Andreasen, Henning Christiansen, 2012-08-27 This volume constitutes the proceedings of the Fourth International Conference on Flexible Query Answering Systems, FQAS'2000, held in Warsaw, Poland on October 25 - 28, 2000. The FQAS conference has been the premier conference focusing on one of key issues that the information society faces, namely that of providing easy, flexible, intuitive access to information for everybody. In targeting this issue, the conference draws on several research areas, such as databases, querying, information retrieval, knowledge representation, soft computing, cyberspace, multimedia systems, human-computer interaction, etc. FQAS'2000 has been preceded by the extremely successful FQAS'94, FQAS'96 and FQAS'98 conferences all held in Roskilde, Denmark. The present conference provides a unique opportunity for researchers, developers and practitioners to explore new ideas and approaches in a multidisciplinary forum. As a metaphor for flexible query answering we may consider a human intermediary who has expertise in the topic of the query, and is experienced in identifying the user's information needs and answering the needs from the available information resources. The use of knowledge on relevant contexts, available information resources, etc. , enables the expert to respond rather precisely to the needs, though the query, per se, may be imprecise, incomplete, etc. Thus, a key issue for flexible query answering system is to obtain, maintain, represent, and utilize such knowledge. This comprises domain knowledge and metaknowledge, its representation and organization in ontologies, terminologies, etc. |
| data warehouse optimization techniques: Database Design Michael Mannino, 2018-09-15 Formerly published by Chicago Business Press, now published by Sage Database Design, Application Development, and Administration, Seventh Edition, offers a comprehensive understanding of database technology. Author Michael Mannino equips students with the necessary tools to grasp the fundamental concepts of database management, and then guides them in honing their skills to solve both basic and advanced challenges in query formulation, data modeling, and database application development. |
| data warehouse optimization techniques: Computational Intelligence, Communications, and Business Analytics Jyotsna Kumar Mandal, Somnath Mukhopadhyay, Paramartha Dutta, Kousik Dasgupta, 2019-06-25 The two volume set CCIS 1030 and 1031 constitutes the refereed proceedings of the Second International Conference on Computational Intelligence, Communications, and Business Analytics, CICBA 2018, held in Kalyani, India, in July 2018. The 76 revised full papers presented in the two volumes were carefully reviewed and selected from 240 submissions. The papers are organized in topical sections on computational intelligence; signal processing and communications; microelectronics, sensors, and intelligent networks; data science & advanced data analytics; intelligent data mining & data warehousing; and computational forensics (privacy and security). |
| data warehouse optimization techniques: The Definitive Guide to Data Integration Pierre-Yves BONNEFOY, Emeric CHAIZE, Raphaël MANSUY, Mehdi TAZI, 2024-03-29 Learn the essentials of data integration with this comprehensive guide, covering everything from sources to solutions, and discover the key to making the most of your data stack Key Features Learn how to leverage modern data stack tools and technologies for effective data integration Design and implement data integration solutions with practical advice and best practices Focus on modern technologies such as cloud-based architectures, real-time data processing, and open-source tools and technologies Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe Definitive Guide to Data Integration is an indispensable resource for navigating the complexities of modern data integration. Focusing on the latest tools, techniques, and best practices, this guide helps you master data integration and unleash the full potential of your data. This comprehensive guide begins by examining the challenges and key concepts of data integration, such as managing huge volumes of data and dealing with the different data types. You’ll gain a deep understanding of the modern data stack and its architecture, as well as the pivotal role of open-source technologies in shaping the data landscape. Delving into the layers of the modern data stack, you’ll cover data sources, types, storage, integration techniques, transformation, and processing. The book also offers insights into data exposition and APIs, ingestion and storage strategies, data preparation and analysis, workflow management, monitoring, data quality, and governance. Packed with practical use cases, real-world examples, and a glimpse into the future of data integration, The Definitive Guide to Data Integration is an essential resource for data eclectics. By the end of this book, you’ll have the gained the knowledge and skills needed to optimize your data usage and excel in the ever-evolving world of data.What you will learn Discover the evolving architecture and technologies shaping data integration Process large data volumes efficiently with data warehousing Tackle the complexities of integrating large datasets from diverse sources Harness the power of data warehousing for efficient data storage and processing Design and optimize effective data integration solutions Explore data governance principles and compliance requirements Who this book is for This book is perfect for data engineers, data architects, data analysts, and IT professionals looking to gain a comprehensive understanding of data integration in the modern era. Whether you’re a beginner or an experienced professional enhancing your knowledge of the modern data stack, this definitive guide will help you navigate the data integration landscape. |
| data warehouse optimization techniques: Handbook of Research on Innovations in Database Technologies and Applications Viviana E. Ferraggine, Jorge H. Doorn, Laura C. Rivero, 2009-01-01 This book provides a wide compendium of references to topics in the field of the databases systems and applications--Provided by publisher. |
| data warehouse optimization techniques: Database Technologies: Concepts, Methodologies, Tools, and Applications Erickson, John, 2009-02-28 This reference expands the field of database technologies through four-volumes of in-depth, advanced research articles from nearly 300 of the world's leading professionals--Provided by publisher. |
| data warehouse optimization techniques: Agent and Multi-Agent Systems: Technologies and Applications Ngoc Thanh Nguyen, Adam Grzech, 2007-05-24 This book constitutes the refereed proceedings of the First International Symposium on Agent and Multi-Agent Systems: Technologies and Applications, KES-AMSTA 2007, held in Wroclaw, Poland in May/June 2007. Coverage includes agent-oriented Web applications, mobility aspects of agent systems, agents for network management, agent approaches to robotic systems, as well as intelligent and secure agents for digital content management. |
| data warehouse optimization techniques: Data Virtualization for Business Intelligence Systems Rick van der Lans, 2012-07-25 Annotation In this book, Rick van der Lans explains how data virtualization servers work, what techniques to use to optimize access to various data sources and how these products can be applied in different projects. |
| data warehouse optimization techniques: Deep Learning Models on Cloud Platforms Vijay Ramamoorthi, 2024-07-25 Deep Learning Models on Cloud Platforms provides an in-depth exploration of the integration of deep learning techniques with cloud computing environments. Architectures, and frameworks for developing and deploying deep learning models at scale. It addresses practical considerations, including data management, computational resources, and cost-efficiency, while highlighting popular cloud platforms like AWS, Google Cloud, and Azure. Through real-world examples and case studies, readers will gain insights into best practices for leveraging cloud infrastructure to enhance deep learning capabilities and drive innovation across various industries. |
| data warehouse optimization techniques: Intelligent Optimization Techniques for Business Analytics Bansal, Sanjeev, Kumar, Nitendra, Agarwal, Priyanka, 2024-04-15 Today, the convergence of cutting-edge algorithms and actionable insights in business is paramount for success. Scholars and practitioners grapple with the dilemma of optimizing data to drive efficiency, innovation, and competitiveness. The formidable challenge of effectively harnessing the immense power of intelligent optimization techniques and business analytics only increases as the volume of data grows exponentially, and the complexities of navigating the intricate landscape of business analytics becomes more daunting. This pressing issue underscores the critical need for a comprehensive solution, and Intelligent Optimization Techniques for Business Analytics is poised to provide much-needed answers. This groundbreaking book offers an all-encompassing solution to the challenges that academic scholars encounter in the pursuit of mastering the interplay between learning algorithms and intelligent optimization techniques for business analytics. Through a wealth of diverse perspectives and expert case studies, it illuminates the path to effectively implementing these advanced systems in real-world business scenarios. It caters not only to the scholarly community but also to industry professionals and policymakers, equipping them with the necessary tools and insights to excel in the realm of data-driven decision-making. |
| data warehouse optimization techniques: Encyclopedia of Business Analytics and Optimization Wang, John, 2014-02-28 As the age of Big Data emerges, it becomes necessary to take the five dimensions of Big Data- volume, variety, velocity, volatility, and veracity- and focus these dimensions towards one critical emphasis - value. The Encyclopedia of Business Analytics and Optimization confronts the challenges of information retrieval in the age of Big Data by exploring recent advances in the areas of knowledge management, data visualization, interdisciplinary communication, and others. Through its critical approach and practical application, this book will be a must-have reference for any professional, leader, analyst, or manager interested in making the most of the knowledge resources at their disposal. |
| data warehouse optimization techniques: Oracle8i Data Warehousing Lilian Hobbs, Susan Hillson, 1999-11-17 Cutting-edge Oracle data warehousing from people creating the software. Oracle8i Data Warehousing explains how to design, develop, and administer powerful data warehouses and data marts on Windows NT using Oracle's major new industry-leading database. This authoritative guide helps database developers, administrators, and designers master the major new data warehousing and Internet capabilities in Oracle8i and specifically plan and implement affordable and successful data warehouses and data marts. In one exciting package, this book brings together three hot computing topics: new Oracle databases, Windows NT, and Internet technologies. Data warehouses and smaller data marts allow companies to pool and analyze large quantities of data, yielding valuable information about customers and business processes. Data warehouses have in the past been expensive and very complex. Oracle8i is an important new version of Oracle Corporation's market-leading database system. Most of the significant new features in Oracle8i are for data warehousing, Internet development, or optimizations for the Windows NT platform. Oracle8i and Windows NT together will make data warehouse projects easier and more affordable. Written by data design and warehousing experts from the Oracle8i development team Explains how to design and build data warehouses and data marts on Windows NT Explains how to establish Web/Intranet access to a data warehouse |
| data warehouse optimization techniques: Data Warehouse Systems Alejandro Vaisman, Esteban Zimányi, 2022-08-16 With this textbook, Vaisman and Zimányi deliver excellent coverage of data warehousing and business intelligence technologies ranging from the most basic principles to recent findings and applications. To this end, their work is structured into three parts. Part I describes “Fundamental Concepts” including conceptual and logical data warehouse design, as well as querying using MDX, DAX and SQL/OLAP. This part also covers data analytics using Power BI and Analysis Services. Part II details “Implementation and Deployment,” including physical design, ETL and data warehouse design methodologies. Part III covers “Advanced Topics” and it is almost completely new in this second edition. This part includes chapters with an in-depth coverage of temporal, spatial, and mobility data warehousing. Graph data warehouses are also covered in detail using Neo4j. The last chapter extensively studies big data management and the usage of Hadoop, Spark, distributed, in-memory, columnar, NoSQL and NewSQL database systems, and data lakes in the context of analytical data processing. As a key characteristic of the book, most of the topics are presented and illustrated using application tools. Specifically, a case study based on the well-known Northwind database illustrates how the concepts presented in the book can be implemented using Microsoft Analysis Services and Power BI. All chapters have been revised and updated to the latest versions of the software tools used. KPIs and Dashboards are now also developed using DAX and Power BI, and the chapter on ETL has been expanded with the implementation of ETL processes in PostgreSQL. Review questions and exercises complement each chapter to support comprehensive student learning. Supplemental material to assist instructors using this book as a course text is available online and includes electronic versions of the figures, solutions to all exercises, and a set of slides accompanying each chapter. Overall, students, practitioners and researchers alike will find this book the most comprehensive reference work on data warehouses, with key topics described in a clear and educational style. “I can only invite you to dive into the contents of the book, feeling certain that once you have completed its reading (or maybe, targeted parts of it), you will join me in expressing our gratitude to Alejandro and Esteban, for providing such a comprehensive textbook for the field of data warehousing in the first place, and for keeping it up to date with the recent developments, in this current second edition.” From the foreword by Panos Vassiliadis, University of Ioannina, Greece. |
| data warehouse optimization techniques: Modeling Approaches and Algorithms for Advanced Computer Applications Abdelmalek Amine, Ait Mohamed Otmane, Ladjel Bellatreche, 2013-08-23 During the last decades Computational Intelligence has emerged and showed its contributions in various broad research communities (computer science, engineering, finance, economic, decision making, etc.). This was done by proposing approaches and algorithms based either on turnkey techniques belonging to the large panoply of solutions offered by computational intelligence such as data mining, genetic algorithms, bio-inspired methods, Bayesian networks, machine learning, fuzzy logic, artificial neural networks, etc. or inspired by computational intelligence techniques to develop new ad-hoc algorithms for the problem under consideration. This volume is a comprehensive collection of extended contributions from the 4th International Conference on Computer Science and Its Applications (CIIA’2013) organized into four main tracks: Track 1: Computational Intelligence, Track 2: Security & Network Technologies, Track 3: Information Technology and Track 4: Computer Systems and Applications. This book presents recent advances in the use and exploitation of computational intelligence in several real world hard problems covering these tracks such as image processing, Arab text processing, sensor and mobile networks, physical design of advanced databases, model matching, etc. that require advanced approaches and algorithms borrowed from computational intelligence for solving them. |
| data warehouse optimization techniques: Metaheuristics and Optimization in Computer and Electrical Engineering Navid Razmjooy, Mohsen Ashourian, Zahra Foroozandeh, 2020-11-16 The use of artificial intelligence, especially in the field of optimization is increasing day by day. The purpose of this book is to explore the possibility of using different kinds of optimization algorithms to advance and enhance the tools used for computer and electrical engineering purposes. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with minimum time …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, released in …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process from …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical barriers …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be collected, …
