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Data Management at Scale: Modern Data Architecture with Data Mesh and Data Fabric
by Piethein StrengholtAs data management continues to evolve rapidly, managing all of your data in a central place, such as a data warehouse, is no longer scalable. Today's world is about quickly turning data into value. This requires a paradigm shift in the way we federate responsibilities, manage data, and make it available to others. With this practical book, you'll learn how to design a next-gen data architecture that takes into account the scale you need for your organization.Executives, architects and engineers, analytics teams, and compliance and governance staff will learn how to build a next-gen data landscape. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed.Examine data management trends, including regulatory requirements, privacy concerns, and new developments such as data mesh and data fabricGo deep into building a modern data architecture, including cloud data landing zones, domain-driven design, data product design, and moreExplore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata
Data Management of Protein Interaction Networks
by Mario Cannataro Pietro Hiram GuzziCurrent PPI databases do not offer sophisticated querying interfaces and especially do not integrate existing information about proteins. Current algorithms for PIN analysis use only topological information, while emerging approaches attempt to exploit the biological knowledge related to proteins and kinds of interaction, e. g. protein function, localization, structure, described in Gene Ontology or PDB. The book discusses technologies, standards and databases for, respectively, generating, representing and storing PPI data. It also describes main algorithms and tools for the analysis, comparison and knowledge extraction from PINs. Moreover, some case studies and applications of PINs are also discussed.
Data Management on New Hardware: 7th International Workshop on Accelerating Data Analysis and Data Management Systems Using Modern Processor and Storage Architectures, ADMS 2016 and 4th International Workshop on In-Memory Data Management and Analytics, IMDM 2016, New Delhi, India, September 1, 2016, Revised Selected Papers (Lecture Notes in Computer Science #10195)
by Justin Levandoski Andrew Pavlo Spyros Blanas Rajesh Bordawekar Tirthankar LahiriThis book contains selected papers from the 7th International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures, ADMS 2016, and the 4th International Workshop on In-Memory Data Management and Analytics, IMDM 2016, held in New Dehli, India, in September 2016. The joint Workshops were co-located with VLDB 2016. The 9 papers presented were carefully reviewed and selected from 18 submissions. They investigate opportunities in accelerating analytics/data management systems and workloads (including traditional OLTP, data warehousing/OLAP, ETL streaming/real-time, business analytics, and XML/RDF processing) running memory-only environments, using processors (e. g. commodity and specialized multi-core, GPUs and FPGAs, storage systems (e. g. storage-class memories like SSDs and phase-change memory), and hybrid programming models like CUDA, OpenCL, and Open ACC. The papers also explore the interplay between overall system design, core algorithms, query optimization strategies, programming approaches, performance modeling and evaluation, from the perspective of data management applications.
Data Management, Analytics and Innovation: Proceedings Of Icdmai 2018, Volume 2 (Advances In Intelligent Systems and Computing #839)
by Valentina Emilia Balas Amlan Chakrabarti Neha SharmaThe volume on Data Management, Analytics and Innovations presents the latest high-quality technical contributions and research results in the areas of data management and smart computing, big data management, artificial intelligence and data analytics along with advances in network technologies. It deals with the state-of-the-art topics and provides challenges and solutions for future development. Original, unpublished research work highlighting specific research domains from all viewpoints are contributed from scientists throughout the globe. This volume is mainly designed for professional audience, composed of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2018, Volume 1 (Advances in Intelligent Systems and Computing #808)
by Valentina Emilia Balas Amlan Chakrabarti Neha SharmaThe book presents the latest, high-quality, technical contributions and research findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. It discusses state-of-the-art topics as well as the challenges and solutions for future development. It includes original and previously unpublished international research work highlighting research domains from different perspectives. This book is mainly intended for researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2019, Volume 1 (Advances in Intelligent Systems and Computing #1042)
by Valentina Emilia Balas Amlan Chakrabarti Neha SharmaThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2019, Volume 2 (Advances in Intelligent Systems and Computing #1016)
by Valentina Emilia Balas Amlan Chakrabarti Neha SharmaThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2020, Volume 1 (Advances in Intelligent Systems and Computing #1174)
by Valentina Emilia Balas Amlan Chakrabarti Neha Sharma Jan MartinovicThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. Gathering peer-reviewed research papers presented at the Fourth International Conference on Data Management, Analytics and Innovation (ICDMAI 2020), held on 17–19 January 2020 at the United Services Institute (USI), New Delhi, India, it addresses cutting-edge topics and discusses challenges and solutions for future development. Featuring original, unpublished contributions by respected experts from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2020, Volume 2 (Advances in Intelligent Systems and Computing #1175)
by Valentina Emilia Balas Amlan Chakrabarti Neha Sharma Jan MartinovicThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. Gathering peer-reviewed research papers presented at the Fourth International Conference on Data Management, Analytics and Innovation (ICDMAI 2020), held on 17–19 January 2020 at the United Services Institute (USI), New Delhi, India, it addresses cutting-edge topics and discusses challenges and solutions for future development. Featuring original, unpublished contributions by respected experts from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2021, Volume 1 (Lecture Notes on Data Engineering and Communications Technologies #70)
by Valentina Emilia Balas Amlan Chakrabarti Neha Sharma Alfred M. BrucksteinThis book presents the latest findings in the areas of data management and smart computing, machine learning, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at Fifth International Conference on Data Management, Analytics and Innovation (ICDMAI 2021), held during January 15–17, 2021, in a virtual mode. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2021, Volume 2 (Lecture Notes on Data Engineering and Communications Technologies #71)
by Valentina Emilia Balas Amlan Chakrabarti Neha Sharma Alfred M. BrucksteinThis book presents the latest findings in the areas of data management and smart computing, machine learning, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at Fifth International Conference on Data Management, Analytics and Innovation (ICDMAI 2021), held during January 15–17, 2021, in a virtual mode. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2022 (Lecture Notes on Data Engineering and Communications Technologies #137)
by Alfred M. Bruckstein C. Mohan Saptarsi Goswami Inderjit Singh Barara Amol GojeThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at Sixth International Conference on Data Management, Analytics and Innovation (ICDMAI 2022), held virtually during January 14–16, 2022. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2023 (Lecture Notes in Networks and Systems #662)
by Amlan Chakrabarti Neha Sharma Alfred M. Bruckstein Amol GojeThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. The volume is a collection of peer reviewed research papers presented at Seventh International Conference on Data Management, Analytics and Innovation (ICDMAI 2023), held during 20 – 22 January, 2023 in Pune, India. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2024, Volume 1 (Lecture Notes in Networks and Systems #997)
by Amlan Chakrabarti Neha Sharma Alfred M. Bruckstein Amol C. GojeThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at 8th International Conference on Data Management, Analytics and Innovation (ICDMAI 2024), held during 19–21 January 2024 in Vellore Institute of Technology, Vellore, India. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. The book is divided into two volumes.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2024, Volume 2 (Lecture Notes in Networks and Systems #998)
by Amlan Chakrabarti Neha Sharma Alfred M. Bruckstein Amol C. GojeThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at 8th International Conference on Data Management, Analytics and Innovation (ICDMAI 2024), held during 19–21 January 2024 in Vellore Institute of Technology, Vellore, India. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. The book is divided into two volumes.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2025, Volume 3 (Lecture Notes in Networks and Systems #1368)
by Saptarsi Goswami Sajal Saha Kanadpriya Basu Romit S BeedThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at 9th International Conference on Data Management, Analytics and Innovation (ICDMAI 2025), held during 17–19 January 2025 at St. Xavier’s College (Autonomous), Kolkata, India. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. The book is divided into three volumes.
Data Management: Der Weg zum datengetriebenen Unternehmen
by Klaus-Dieter GronwaldDieses Lehrbuch betrachtet Data Management als interdisziplinäres Konzept mit Fokus auf den Zielen datengetriebener Unternehmen. Im Zentrum steht die interaktive Entwicklung eines Unternehmensdatenmodells für ein virtuelles Unternehmen mit Unterstützung eines online Learning Games unter Einbeziehung der Aufgaben, Ziele und Grundsätze des Data Managements, typischer Data-Management-Komponenten und Frameworks wie Datenmodellierung und Design, Metadaten Management, Data Architecture, und Data Governance, und verknüpft diese mit datengetriebenen Anwendungen wie Business Warehousing, Big Data, In-Memory Data Management, und Machine Learning im Data Management Kontext.Das Buch dient als Lehrbuch für Studierende der Informatik, der Wirtschaft und der Wirtschaftsinformatik an Universitäten, Hochschulen und Fachschulen und zur industriellen Aus- und Weiterbildung.
Data Manipulation with R
by Jaynal AbedinThis book is a step-by step, example-oriented tutorial that will show both intermediate and advanced users how data manipulation is facilitated smoothly using R. This book is aimed at intermediate to advanced level users of R who want to perform data manipulation with R, and those who want to clean and aggregate data effectively. Readers are expected to have at least an introductory knowledge of R and some basic administration work in R, such as installing packages and calling them when required.
Data Manipulation with R - Second Edition
by Jaynal Abedin Kishor Kumar DasThis book is for all those who wish to learn about data manipulation from scratch and excel at aggregating data effectively. It is expected that you have basic knowledge of R and have previously done some basic administration work with R.
Data Mashup with Microsoft Excel Using Power Query and M: Finding, Transforming, and Loading Data from External Sources
by Adam AspinMaster the art of loading external data into Excel for use in reporting, charting, dashboarding, and business intelligence. This book provides a complete and thorough explanation of Microsoft Excel’s Get and Transform feature set, showing you how to connect to a range of external databases and other data sources to find data and pull that data into your local spreadsheet for further analysis. Leading databases are covered, including Microsoft Azure data sources and web sources, and you will learn how to access those sources from your Microsoft Excel spreadsheets.Getting data into Excel is a prerequisite for using Excel's analytics capabilities. This book takes you beyond copying and pasting by showing you how to connect to your corporate databases that are hosted in the Azure cloud, and how to pull data from Oracle Database and SQL Server, and other sources.Accessing data is only half the problem, and the other half involves cleansing and rearranging your data to make it useful in spreadsheet form. Author Adam Aspin shows you how to create datasets and transformations. For advanced problems, there is help on the M language that is built into Excel, specifically to support mashing up data in support of business intelligence and analysis. If you are an Excel user, you won't want to be without this book that teaches you to extract and prepare external data ready for use in what is arguably the world’s leading analytics tool.What You Will LearnConnect to a range of external data, from databases to Azure sourcesIngest data directly into your spreadsheets, or into PowerPivot data modelsCleanse and prepare external data so it can be used inside ExcelRefresh data quickly and easily to always have the latest informationTransform data into ready-to-use structures that fit the spreadsheet formatExecute M language functions for complex data transformationsWho This Book Is ForExcel users who want to access data from external sources—including the Microsoft Azure platform—in order to create business intelligence reporting, dashboards, and visualizations. For Excel users needing to cleanse and rearrange such data to meet their own, specific needs.
Data Mashups in R: A Case Study in Real-World Data Analysis
by Jeremy Leipzig Xiao-Yi LiHow do you use R to import, manage, visualize, and analyze real-world data? With this short, hands-on tutorial, you learn how to collect online data, massage it into a reasonable form, and work with it using R facilities to interact with web servers, parse HTML and XML, and more. Rather than use canned sample data, you'll plot and analyze current home foreclosure auctions in Philadelphia.This practical mashup exercise shows you how to access spatial data in several formats locally and over the Web to produce a map of home foreclosures. It's an excellent way to explore how the R environment works with R packages and performs statistical analysis.Parse messy data from public foreclosure auction postingsPlot the data using R's PBSmapping packageImport US Census data to add context to foreclosure dataUse R's lattice and latticeExtra packages for data visualizationCreate multidimensional correlation graphs with the pairs() scatterplot matrix package
Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection (Data-Centric Systems and Applications)
by Peter ChristenData matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. Based on research in various domains including applied statistics, health informatics, data mining, machine learning, artificial intelligence, database management, and digital libraries, significant advances have been achieved over the last decade in all aspects of the data matching process, especially on how to improve the accuracy of data matching, and its scalability to large databases. Peter Christen's book is divided into three parts: Part I, "Overview", introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, "Steps of the Data Matching Process", then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, "Further Topics", deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today. By providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.
Data Matters: Ethics, Data, And International Research Collaboration In A Changing World: Proceedings Of A Workshop
by Engineering Medicine National Academies of SciencesIn an increasingly interconnected world, perhaps it should come as no surprise that international collaboration in science and technology research is growing at a remarkable rate. As science and technology capabilities grow around the world, U.S.-based organizations are finding that international collaborations and partnerships provide unique opportunities to enhance research and training. International research agreements can serve many purposes, but data are always involved in these collaborations. The kinds of data in play within international research agreements varies widely and may range from financial and consumer data, to Earth and space data, to population behavior and health data, to specific project-generated data—this is just a narrow set of examples of research data but illustrates the breadth of possibilities. The uses of these data are various and require accounting for the effects of data access, use, and sharing on many different parties. Cultural, legal, policy, and technical concerns are also important determinants of what can be done in the realms of maintaining privacy, confidentiality, and security, and ethics is a lens through which the issues of data, data sharing, and research agreements can be viewed as well. A workshop held on March 14-16, 2018, in Washington, DC explored the changing opportunities and risks of data management and use across disciplinary domains. The third workshop in a series, participants gathered to examine advisory principles for consideration when developing international research agreements, in the pursuit of highlighting promising practices for sustaining and enabling international research collaborations at the highest ethical level possible. The intent of the workshop was to explore, through an ethical lens, the changing opportunities and risks associated with data management and use across disciplinary domains—all within the context of international research agreements. This publication summarizes the presentations and discussions from the workshop.
Data Merge and Styles for Adobe InDesign CC 2018: Creating Custom Documents for Mailouts and Presentation Packages
by Jennifer HarderHarness the power of Adobe InDesign's data merge and style panel. Whether you're creating custom mail-outs or other mail-merge needs, familiarize yourself with this powerful InDesign panel in this in-depth, step-by-step guide. This book shows you how to easily create, edit, and print data merged documents that match specific branding and style guidelines. You'll learn how to combine MS Excel to create a faster workflow and quickly turn your Adobe InDesign CC 2017 files into printer-ready files. In this book, we'll also take a look at how to apply paragraph and character styles to your text and how you can alter formatting using Global Regular Expressions Print (GREPs). With Data Merge and Styles for Adobe InDesign CC 2017 as your guide, you'll see how to save time and money by learning all the peculiarities and powerful features of Adobe InDesign data merge. By the end of this book, you'll be able to streamline your workflow and avoid using MS Word's mail merge and back-and-forth edits. What You'll Learn Create custom print media with text styles using Adobe InDesign CC 2017 Work with GREPs in conjunction with Character and Paragraph Styles to customize data Build a numbering sequence for tickets Create single and multiple data merges Who This Book Is For Students, graphic designers, and corporate administrators who need to create documents for events.
Data Mesh in Action
by Jacek Majchrzak Sven Balnojan Marian SiwiakRevolutionize the way your organization approaches data with a data mesh! This new decentralized architecture outpaces monolithic lakes and warehouses and can work for a company of any size.In Data Mesh in Action you will learn how to: Implement a data mesh in your organization Turn data into a data product Move from your current data architecture to a data mesh Identify data domains, and decompose an organization into smaller, manageable domains Set up the central governance and local governance levels over data Balance responsibilities between the two levels of governance Establish a platform that allows efficient connection of distributed data products and automated governance Data Mesh in Action reveals how this groundbreaking architecture looks for both small startups and large enterprises. You won&’t need any new technology—this book shows you how to start implementing a data mesh with flexible processes and organizational change. You&’ll explore both an extended case study and multiple real-world examples. As you go, you&’ll be expertly guided through discussions around Socio-Technical Architecture and Domain-Driven Design with the goal of building a sleek data-as-a-product system. Plus, dozens of workshop techniques for both in-person and remote meetings help you onboard colleagues and drive a successful transition. About the technology Business increasingly relies on efficiently storing and accessing large volumes of data. The data mesh is a new way to decentralize data management that radically improves security and discoverability. A well-designed data mesh simplifies self-service data consumption and reduces the bottlenecks created by monolithic data architectures. About the book Data Mesh in Action teaches you pragmatic ways to decentralize your data and organize it into an effective data mesh. You&’ll start by building a minimum viable data product, which you&’ll expand into a self-service data platform, chapter-by-chapter. You&’ll love the book&’s unique &“sliders&” that adjust the mesh to meet your specific needs. You&’ll also learn processes and leadership techniques that will change the way you and your colleagues think about data. What's inside Decompose an organization into manageable domains Turn data into a data product Set up central and local governance levels Build a fit-for-purpose data platform Improve management, initiation, and support techniques About the reader For data professionals. Requires no specific programming stack or data platform. About the author Jacek Majchrzak is a hands-on lead data architect. Dr. Sven Balnojan manages data products and teams. Dr. Marian Siwiak is a data scientist and a management consultant for IT, scientific, and technical projects. Table of Contents PART 1 FOUNDATIONS 1 The what and why of the data mesh 2 Is a data mesh right for you? 3 Kickstart your data mesh MVP in a month PART 2 THE FOUR PRINCIPLES IN PRACTICE 4 Domain ownership 5 Data as a product 6 Federated computational governance 7 The self-serve data platform PART 3 INFRASTRUCTURE AND TECHNICAL ARCHITECTURE 8 Comparing self-serve data platforms 9 Solution architecture design