Just like the industrial society of the last century depended on natural resources, today's society depends on information and its exchange.

Staab and Stuckenschmidt structured the selected contributions into four parts: Part I, "Data Storage and Access," prepares the semantic foundation, i.e. data modelling and querying in a flexible and yet scalable manner. These foundations allow for dealing with the organization of information at the individual peers. Part II, "Querying the Network," considers the routing of queries, as well as continuous queries and personalized queries under the conditions of the permanently changing topological structure of a peer-to-peer network. Part III, "Semantic Integration," deals with the mapping of heterogeneous data representations. Finally Part IV, "Methodology and Systems," reports experiences from case studies and sample applications.

The overall result is a state-of-the-art description of the potential of Semantic Web and peer-to-peer technologies for information sharing and knowledge management when applied jointly.

Best practices and invaluable advice from world-renowned data warehouse experts

In this book, leading data warehouse experts from the Kimball Group share best practices for using the upcoming "Business Intelligence release" of SQL Server, referred to as SQL Server 2008 R2. In this new edition, the authors explain how SQL Server 2008 R2 provides a collection of powerful new tools that extend the power of its BI toolset to Excel and SharePoint users and they show how to use SQL Server to build a successful data warehouse that supports the business intelligence requirements that are common to most organizations. Covering the complete suite of data warehousing and BI tools that are part of SQL Server 2008 R2, as well as Microsoft Office, the authors walk you through a full project lifecycle, including design, development, deployment and maintenance.Features more than 50 percent new and revised material that covers the rich new feature set of the SQL Server 2008 R2 release, as well as the Office 2010 releaseIncludes brand new content that focuses on PowerPivot for Excel and SharePoint, Master Data Services, and discusses updated capabilities of SQL Server Analysis, Integration, and Reporting ServicesShares detailed case examples that clearly illustrate how to best apply the techniques described in the bookThe accompanying Web site contains all code samples as well as the sample database used throughout the case studies

"The Microsoft Data Warehouse Toolkit, Second Edition" provides you with the knowledge of how and when to use BI tools such as Analysis Services and Integration Services to accomplish your most essential data warehousing tasks.

Organization of data warehouses is a vital, but often neglected, aspect of growing an enterprise. Unlike most books on the subject that focus on either the technical aspects of building data warehouses or on business strategies, this valuable reference synthesizes technological know-how with managerial best practices to show how improved alignment between data warehouse plans and business strategies can lead to successful data warehouse adoption capable of supporting an enterprise s entire infrastructure.

Strategic Data Warehousing: Achieving Alignment with Business provides data warehouse developers, business managers, and IT professionals and administrators with an integrated approach to achieving successful and sustainable alignment of data warehouses and business goals. More complete than any other text in the field, this comprehensive reference details the joint roles and responsibilities of the data warehouse and business managers in achieving strategic alignment, business user satisfaction, technical integration, and improved flexibility.

Complete with case studies that depict real-world scenarios, the text:

• Examines the organizational, user, data, and technological factors proven to promote successful data warehousing
• Includes actionable solutions for achieving strategic alignment
• Provides a model that readers can apply in aligning their own data warehouse needs and business goals

Achieving sustainable alignment between the data warehouse and business strategies is a continuous process. Armed with this valuable reference, readers will be able to gain the solid understanding of the organizational, technical, data, and user factors needed to promote a successful data warehouse adoption and become active partners in leveraging this powerful, but often overlooked, information resource.

Discover how graph databases can help you manage and query highly connected data. With this practical book, you'll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems. This second edition includes new code samples and diagrams, using the latest Neo4j syntax, as well as information on new functionality. Learn how different organizations are using graph databases to outperform their competitors. With this book's data modeling, query, and code examples, you'll quickly be able to implement your own solution. Model data with the Cypher query language and property graph model Learn best practices and common pitfalls when modeling with graphs Plan and implement a graph database solution in test-driven fashion Explore real-world examples to learn how and why organizations use a graph database Understand common patterns and components of graph database architecture Use analytical techniques and algorithms to mine graph database information

Did you know that there is a technology inside Excel, and Power BI, that allows you to create magic in your data, avoid repetitive manual work, and save you time and money? Using Excel and Power BI, you can: Save time by eliminating the pain of copying and pasting data into workbooks and then manually cleaning that data. Gain productivity by properly preparing data yourself, rather than relying on others to do it. Gain effiiciency by reducing the time it takes to prepare data for analysis, and make informed decisions more quickly. With the data connectivity and transformative technology found in Excel and Power BI, users with basic Excel skills import data and then easily reshape and cleanse that data, using simple intuitive user interfaces. Known as "Get & Transform" in Excel 2016, as the "Power Query" separate add-in in Excel 2013 and 2010, and included in Power BI, you'll use this technology to tackle common data challenges, resolving them with simple mouse clicks and lightweight formula editing. With your new data transformation skills acquired through this book, you will be able to create an automated transformation of virtually any type of data set to mine its hidden insights.

Cutting-edge content and guidance from a data warehousing expert--now expanded to reflect field trends

Data warehousing has revolutionized the way businesses in a wide variety of industries perform analysis and make strategic decisions. Since the first edition of "Data Warehousing Fundamentals," numerous enterprises have implemented data warehouse systems and reaped enormous benefits. Many more are in the process of doing so. Now, this new, revised edition covers the essential fundamentals of data warehousing and business intelligence as well as significant recent trends in the field.

The author provides an enhanced, comprehensive overview of data warehousing together with in-depth explanations of critical issues in planning, design, deployment, and ongoing maintenance. IT professionals eager to get into the field will gain a clear understanding of techniques for data extraction from source systems, data cleansing, data transformations, data warehouse architecture and infrastructure, and the various methods for information delivery.

This practical "Second Edition" highlights the areas of data warehousing and business intelligence where high-impact technological progress has been made. Discussions on developments include data marts, real-time information delivery, data visualization, requirements gathering methods, multi-tier architecture, OLAP applications, Web clickstream analysis, data warehouse appliances, and data mining techniques. The book also contains review questions and exercises for each chapter, appropriate for self-study or classroom work, industry examples of real-world situations, and several appendices with valuable information.

Specifically written for professionals responsible for designing, implementing, or maintaining data warehousing systems, "Data Warehousing Fundamentals" presents agile, thorough, and systematic development principles for the IT professional and anyone working or researching in information management.

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. The definitive guide to dimensional design for your data warehouseLearn the best practices of dimensional design. Star Schema: The Complete Reference offers in-depth coverage of design principles and their underlying rationales. Organized around design concepts and illustrated with detailed examples, this is a step-by-step guidebook for beginners and a comprehensive resource for experts. This all-inclusive volume begins with dimensional design fundamentals and shows how they fit into diverse data warehouse architectures, including those of W.H. Inmon and Ralph Kimball. The book progresses through a series of advanced techniques that help you address real-world complexity, maximize performance, and adapt to the requirements of BI and ETL software products. You are furnished with design tasks and deliverables that can be incorporated into any project, regardless of architecture or methodology. Master the fundamentals of star schema design and slow change processing Identify situations that call for multiple stars or cubes Ensure compatibility across subject areas as your data warehouse grows Accommodate repeating attributes, recursive hierarchies, and poor data quality Support conflicting requirements for historic data Handle variation within a business process and correlation of disparate activities Boost performance using derived schemas and aggregates Learn when it's appropriate to adjust designs for BI and ETL tools

With a growing ecosystem of tools and libraries available, and the flexibility to run on many platforms (web, desktop and mobile), JavaScript is a terrific all-round environment for all data wrangling needs! Data Wrangling with JavaScript teaches readers core data munging techniques in JavaScript, along with many libraries and tools that will make their data tasks even easier. Key Features * How to handle unusual data sets * Cleaning and preparing raw data * Visualizing your results Audience Written for developers with experience using JavaScript. No prior knowledge of data analytics is needed. Author Bio Ashley Davis is a software developer, entrepreneur, writer, and a stock trader. He is the creator of Data-Forge, a data transformation and analysis toolkit for JavaScript inspired by Pandas and Microsoft LINQ.

Jede Business-Intelligence-Anwendung beruht letzten Endes auf einem Data Warehouse. Data Warehousing ist deshalb ein sehr wichtiges Gebiet der Angewandten Informatik, insbesondere im Zeitalter von Big Data. Das vorliegende Buch beleuchtet das Data Warehouse aus zwei Perspektiven: der des Entwicklers und der des Anwenders. Der zukA1/4nftige Entwickler lernt, ein Data Warehouse mit geeigneten Methoden selbst zu entwickeln. FA1/4r den zukA1/4nftigen Anwender geht der Autor auf die Themen Reporting, Online Analytical Processing und Data Mining ein. Das Lehrbuch ist auch zum Selbststudium geeignet. Kenntnisse A1/4ber Datenbanksysteme sollten allerdings vorhanden sein.

This two-volume set constitutes the refereed proceedings of the 17th International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually.The 62 full papers and 7 short papers presented were carefully reviewed and selected from 206 submissions. The papers reflect the conference sessions as follows: Optimization for Collaborate System; Optimization based on Collaborative Computing; UVA and Traffic system; Recommendation System; Recommendation System & Network and Security; Network and Security; Network and Security & IoT and Social Networks; IoT and Social Networks & Images handling and human recognition; Images handling and human recognition & Edge Computing; Edge Computing; Edge Computing & Collaborative working; Collaborative working & Deep Learning and application; Deep Learning and application; Deep Learning and application; Deep Learning and application & UVA.

This book provides readers the "big picture" and a comprehensive survey of the domain of big data processing systems. For the past decade, the Hadoop framework has dominated the world of big data processing, yet recently academia and industry have started to recognize its limitations in several application domains and thus, it is now gradually being replaced by a collection of engines that are dedicated to specific verticals (e.g. structured data, graph data, and streaming data). The book explores this new wave of systems, which it refers to as Big Data 2.0 processing systems. After Chapter 1 presents the general background of the big data phenomena, Chapter 2 provides an overview of various general-purpose big data processing systems that allow their users to develop various big data processing jobs for different application domains. In turn, Chapter 3 examines various systems that have been introduced to support the SQL flavor on top of the Hadoop infrastructure and provide competing and scalable performance in the processing of large-scale structured data. Chapter 4 discusses several systems that have been designed to tackle the problem of large-scale graph processing, while the main focus of Chapter 5 is on several systems that have been designed to provide scalable solutions for processing big data streams, and on other sets of systems that have been introduced to support the development of data pipelines between various types of big data processing jobs and systems. Next, Chapter 6 focuses on covering the emerging frameworks and systems in the domain of scalable machine learning and deep learning processing. Lastly, Chapter 7 shares conclusions and an outlook on future research challenges. This new and considerably enlarged second edition not only contains the completely new chapter 6, but also offers a refreshed content for the state-of-the-art in all domains of big data processing over the last years. Overall, the book offers a valuable reference guide for professional, students, and researchers in the domain of big data processing systems. Further, its comprehensive content will hopefully encourage readers to pursue further research on the subject.

Do your business intelligence (BI) projects take too long to deliver? Is the value of the deliverables less than satisfactory? Do these projects propagate poor data management practices? If you screamed yes to any of these questions, read this book to master a proven approach to building your enterprise data warehouse and BI initiatives. "Extreme Scoping", based on the Business Intelligence Roadmap, will show you how to build analytics applications rapidly yet not sacrifice data management and enterprise architecture. In addition, all of the roles required to deliver all seven steps of this agile methodology are explained along with many real-world examples. From Wayne Eckersons Foreword -- I've read many books about data warehousing and business intelligence (BI). This book by Larissa Moss is one of the best. I should not be surprised. Larissa has spent years refining the craft of designing, building, and delivering BI applications. Over the years, she has developed a keen insight about what works and doesnt work in BI. This book brings to light the wealth of that development experience. Best of all, this is not some dry text that laboriously steps readers through a technical methodology. Larissa expresses her ideas in a clear, concise, and persuasive manner. I highlighted so many beautifully written and insightful paragraphs in her manuscript that it became comical. I desperately wanted the final, published book rather than the manuscript so I could dog-ear it to death and place it front-and-center in my office bookshelf! From David Wells Foreword : Extreme Scoping is rich with advice and guidance for virtually every aspect of BI projects from planning and requirements to deployment and from back-end data management to front-end information and analytics services. Larissa is both a pragmatist and an independent thinker. Those qualities come through in the style of this book. This is a well-written book that is easy to absorb. It is not full of surprises. It is filled with a lot of common sense and lessons learned through experience.

Get started with Azure Synapse Analytics, Microsoft's modern data analytics platform. This book covers core components such as Synapse SQL, Synapse Spark, Synapse Pipelines, and many more, along with their architecture and implementation. The book begins with an introduction to core data and analytics concepts followed by an understanding of traditional/legacy data warehouse, modern data warehouse, and the most modern data lakehouse. You will go through the introduction and background of Azure Synapse Analytics along with its main features and key service capabilities. Core architecture is discussed, along with Synapse SQL. You will learn its main features and how to create a dedicated Synapse SQL pool and analyze your big data using Serverless Synapse SQL Pool. You also will learn Synapse Spark and Synapse Pipelines, with examples. And you will learn Synapse Workspace and Synapse Studio followed by Synapse Link and its features. You will go through use cases in Azure Synapse and understand the reference architecture for Synapse Analytics. After reading this book, you will be able to work with Azure Synapse Analytics and understand its architecture, main components, features, and capabilities. What You Will Learn Understand core data and analytics concepts and data lakehouse concepts Be familiar with overall Azure Synapse architecture and its main components Be familiar with Synapse SQL and Synapse Spark architecture components Work with integrated Apache Spark (aka Synapse Spark) and Synapse SQL engines Understand Synapse Workspace, Synapse Studio, and Synapse Pipeline Study reference architecture and use cases Who This Book Is For Azure data analysts, data engineers, data scientists, and solutions architects

Are you struggling with the formal design of your organisation's data resource? Do you find yourself forced into generic data architectures and universal data models? Do you find yourself warping the business to fit a purchased application? Do you find yourself pushed into developing physical databases without formal logical design? Do you find disparate data throughout the organisation? If the answer to any of these questions is Yes, then you need to read Data Resource Design to help guide you through a formal design process that produces a high quality data resource within a single common data architecture. Most public and private sector organisations do not consistently follow a formal data resource design process that begins with the organisation's perception of the business world, proceeds through logical data design, through physical data design, and into implementation. Most organisations charge ahead with physical database implementation, physical package implementation, and other brute-force-physical approaches. The result is a data resource that becomes disparate and does not fully support the organisation in its business endeavours. This book describes how to formally design an organisation's data resource to meet its current and future business information demand. It builds on "Data Resource Simplexity", which described how to stop the burgeoning data disparity, and on "Data Resource Integration", which described how to understand and resolve an organisation's disparate data resource. It describes the concepts, principles, and techniques for building a high quality data resource based on an organisation's perception of the business world in which they operate. Like "Data Resource Simplexity" and "Data Resource Integration", Michael Brackett draws on five decades of data management experience building and managing data resources, and resolving disparate data in both public and private sector organisations. He leverages theories, concepts, principles, and techniques from a wide variety of disciplines, such as human dynamics, mathematics, physics, chemistry, philosophy, and biology, and applies them to properly designing data as a critical resource of an organisation. He shows how to understand the business environment where an organisation operates and design a data resource that supports the organisation in that business environment.

The chapter "An Efficient Index for Reachability Queries in Public Transport Networks" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This book offers comprehensive coverage of information retrieval by considering both Text Based Information Retrieval (TBIR) and Content Based Image Retrieval (CBIR), together with new research topics. The approach to TBIR is based on creating a thesaurus, as well as event classification and detection. N-gram thesaurus generation for query refinement offers a new method for improving the precision of retrieval, while event classification and detection approaches aid in the classification and organization of information using web documents for domain-specific retrieval applications. In turn, with regard to content based image retrieval (CBIR) the book presents a histogram construction method, which is based on human visual perceptions of color. The book's overarching goal is to introduce readers to new ideas in an easy-to-follow manner.

This book constitutes the thoroughly refereed post-conference proceedings of the Third COST Action IC1302 International KEYSTONE Conference on Semantic Keyword-Based Search on Structured Data Sources, IKC 2017, held in Gdansk, Poland, in September 2017. The 13 revised full papers and 5 short papers included in the first part of the book were carefully reviewed and selected from numerous submissions. The second part contains reports that summarize the major activities and achievements that have taken place in the context of the action: the short term scientific missions, the outcome of the summer schools, and the results achieved within the following four work packages: representation of structured data sources; keyword search; user interaction and keyword query interpretation; and research integration, showcases, benchmarks and evaluations. Also included is a short report generated by the chairs of the action. The papers cover a broad range of topics in the area of keyword search combining expertise from many different related fields such as information retrieval, natural language processing, ontology management, indexing, semantic web and linked data.

Three books by the bestselling authors on Data Warehousing The most authoritative guides from the inventor of the technique all for a value price.

The Data Warehouse Toolkit, 3rd Edition (9781118530801) Ralph Kimball invented a data warehousing technique called "dimensional modeling" and popularized it in his first Wiley book, The Data Warehouse Toolkit. Since this book was first published in 1996, dimensional modeling has become the most widely accepted technique for data warehouse design. Over the past 10 years, Kimball has improved on his earlier techniques and created many new ones. In this 3rd edition, he will provide a comprehensive collection of all of these techniques, from basic to advanced.

The Data Warehouse Lifecycle Toolkit, 2nd Edition (9780470149775) Complete coverage of best practices from data warehouse project inception through on-going program management. Updates industry best practices to be in sync with current recommendations of Kimball Group. Streamlines the lifecycle methodology to be more efficient and user-friendly

The Data Warehouse ETL Toolkit (9780764567575) shows data warehouse developers how to effectively manage the ETL (Extract, Transform, Load) phase of the data warehouse development lifecycle. The authors show developers the best methods for extracting data from scattered sources throughout the enterprise, removing obsolete, redundant, and innaccurate data, transforming the remaining data into correctly formatted data structures, and then physically loading them into the data warehouse. This book provides complete coverage of proven, time-saving ETL techniques. It begins with a quick overview of ETL fundamentals and the role of the ETL development team. It then quickly moves into an overview of the ETL data structures, both relational and dimensional. The authors show how to build useful dimensional stuctures, providing practical examples of beginning through advanced techniques.

Learn essential techniques from data warehouse legend Bill Inmon on how to build the reporting environment your business needs now! Answers for many valuable business questions hide in text. How well can your existing reporting environment extract the necessary text from email, spreadsheets, and documents, and put it in a useful format for analytics and reporting? Transforming the traditional data warehouse into an efficient unstructured data warehouse requires additional skills from the analyst, architect, designer, and developer. This book will prepare you to successfully implement an unstructured data warehouse and, through clear explanations, examples, and case studies, you will learn new techniques and tips to successfully obtain and analyse text. Master these ten objectives: Build an unstructured data warehouse using the 11-step approach; Integrate text and describe it in terms of homogeneity, relevance, medium, volume, and structure; Overcome challenges including blather, the Tower of Babel, and lack of natural relationships; Avoid the Data Junkyard and combat the "Spiders Web"; Reuse techniques perfected in the traditional data warehouse and Data Warehouse 2.0 , including iterative development; Apply essential techniques for textual Extract, Transform, and Load (ETL) such as phrase recognition, stop word filtering, and synonym replacement; Design the Document Inventory system and link unstructured text to structured data; Leverage indexes for efficient text analysis and taxonomies for useful external categorisation; Manage large volumes of data using advanced techniques such as backward pointers; Evaluate technology choices suitable for unstructured data processing, such as data warehouse appliances.

Most of modern enterprises, institutions, and organizations rely on knowledge-based management systems. In these systems, knowledge is gained from data analysis. Nowadays, knowledge-based management systems include data warehouses as their core components. The purpose of building a data warehouse is twofold. Firstly, to integrate multiple heterogeneous, autonomous, and distributed data sources within an enterprise. Secondly, to provide a platform for advanced, complex, and efficient data analysis. Data integrated in a data warehouse are analyzed by the so-called On-Line Analytical Processing (OLAP) applications designed among others for discovering trends, patterns of behavior, and anomalies as well as for finding dependencies between data. Massive amounts of integrated data and the complexity of integrated data that more and more often come from WEB-based, XML-based, spatio-temporal, object, and multimedia systems, make data integration and processing challenging.

The objective of NEW TRENDS IN DATA WAREHOUSING AND DATA ANALYSIS is fourfold: First, to bring together the most recent research and practical achievements in the DW and OLAP technologies. Second, to open and discuss new, just emerging areas of further development. Third, to provide the up-to-date bibliography of published works and the resource of research achievements for anyone interested in up-to-date data warehouse issues. And, finally, to assist in the dissemination of knowledge in the field of advanced DW and OLAP.

Until recently, many people thought big data was a passing fad. "Data science" was an enigmatic term. Today, big data is taken seriously, and data science is considered downright sexy. With this anthology of reports from award-winning journalist Mike Barlow, you'll appreciate how data science is fundamentally altering our world, for better and for worse. Barlow paints a picture of the emerging data space in broad strokes. From new techniques and tools to the use of data for social good, you'll find out how far data science reaches. With this anthology, you'll learn how: Analysts can now get results from their data queries in near real time Indie manufacturers are blurring the lines between hardware and software Companies try to balance their desire for rapid innovation with the need to tighten data security Advanced analytics and low-cost sensors are transforming equipment maintenance from a cost center to a profit center CIOs have gradually evolved from order takers to business innovators New analytics tools let businesses go beyond data analysis and straight to decision-making Mike Barlow is an award-winning journalist, author, and communications strategy consultant. Since launching his own firm, Cumulus Partners, he has represented major organizations in a number of industries.