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Data Management in Cloud, Grid and P2P Systems: 6th International Conference, Globe 2013, Prague, Czech Republic, August 28-29, 2013, Proceedings (Lecture Notes in Computer Science #8059)
by Abdelkader Hameurlain Wenny Rahayu David TaniarThis book constitutes the refereed proceedings of the 6th International Conference on Data Management in Grid and Peer-to-Peer Systems, Globe 2013, held in Prague, Czech Republic, in August 2013 in conjunction with DEXA 2013. The 10 revised full papers presented were carefully reviewed and selected from 19 submissions. The papers are organized in the following topical sections: data partitioning and consistency; RDF data publishing, querying linked data, and applications; and distributed storage systems and virtualization.
Data Management in Grid and Peer-to-Peer Systems: First International Conference, Globe 2008, Turin, Italy, September 3, 2008, Proceedings (Lecture Notes in Computer Science #5187)
by Abdelkader HameurlainFirst International Conference on Data Management in Grid and Peer-to-Peer (P2P) Systems, Globe 2008 Data management can be achieved by different types of systems: from centralized file management systems to grid and P2P systems passing through distributed systems, par- lel systems, and data integration systems. An increase in the demand of data sharing from different sources accessible through networks has led to proposals for virtual data in- gration approach. The aim of data integration systems, based on the mediator-wrapper architecture, is to provide uniform access to multiple distributed, autonomous and h- erogeneous data sources. Heterogeneity may occur at various levels (e. g. , different ha- ware platforms, operating systems, DBMS). For more than ten years, research topics such as grid and P2P systems have been very active and their synergy has been pointed out. They are important for scale d- tributed systems and applications that require effective management of voluminous, distributed, and heterogeneous data. This importance comes out of characteristics offered by these systems (e. g. , autonomy and the dynamicity of nodes, decentralized control for scaling). Today, the grid and P2P systems intended initially for intensive computing and file sharing are open to the management of voluminous, heteroge- ous, and distributed data in a large-scale environment.
Data Management in Grid and Peer-to-Peer Systems: Third International Conference, Globe 2010, Bilbao, Spain, September 1-2, 2010, Proceedings (Lecture Notes in Computer Science #6265)
by Abdelkader Hameurlain Franck Morvan A. Min TjoaSince 2008, Globe has been an annual international conference on data management in grid and peer-to-peer systems. Initially, grid and peer-to-peer systems experienced significant success in scientific and file sharing applications. Today, these systems cover the management of large, distributed and heterogeneous data. These systems are characterized by high heterogeneity, high autonomy and dynamics of nodes, dec- tralization of control and large-scale distribution of resources. Research on data m- agement in grid and peer-to-peer, relatively recent, aims to scale distributed systems and applications that require effective management of voluminous, large-scale distr- uted and heterogeneous data. The third edition of the international conference Globe was held in Bilbao, Spain during September 1-2, 2010. Globe provided opportunities for academia or industry researchers to present and discuss the latest research and applications on data m- agement in grid and peer-to-peer systems. Globe 2010 received 26 papers from 15 countries. The reviewing process led to the acceptance of 13 papers for presentation at the conference and inclusion in this LNCS volume. Each paper was reviewed by at least two Program Committee members. The conference would not have been possible without the support of the Program Committee members, external reviewers, Organizing Committee, members of the DEXA conference and the authors. In particular, we would like to thank Gabriela Wagner and Roland Wagner (FAW, University of Linz) for their help in the reali- tion of this conference.
Data Management in Grid and Peer-to-Peer Systems: 4th International Conference, Globe 2011, Toulouse, France, September 1-2, 2011, Proceedings (Lecture Notes in Computer Science #6864)
by Abdelkader Hameurlain A. Min TjoaThis book constitutes the refereed proceedings of the 4th International Conference on Data Management in Grid and Peer-to-Peer Systems, Globe 2011, held in Toulouse, France, in September 2011 in conjunction with DEXA 2011. The 12 revised full papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in topical sections on data storage and replication, semantics for P2P systems and performance evaluation, resource discovery and routing in mobile P2P networks, and data stream systems and large-scale distributed applications.
Data Management in Grid and Peer-to-Peer Systems: Second International Conference, Globe 2009 Linz, Austria, September 1-2, 2009 Proceedings (Lecture Notes in Computer Science #5697)
by Abdelkader Hameurlain A. Min TjoaThe synergy and convergence of research on grid computing and peer-to-peer (P2P) computing have materialized in the meeting of the two research communities: parallel systems and distributed systems. The main common objective is to harness Internet-connected resources (e.g., CPU, memory, network bandwidth, data sources) at very large scale. In this framework, the Globe Conference tries to consolidate the bidirectional bridge between grid and P2P systems and large-scale heterogeneous distributed database systems. Today, the grid and P2P systems hold a more and more important position in the landscape of the research in large-scale distributed systems, and the applications which require an effective management of voluminous, distributed and heterogeneous data. This importance comes out of characteristics offered by these systems: autonomy and dynamicity of peers, decentralized control for scaling, and transparent sharing large-scale distributed resources. The second edition of the International Conference on Data Management in Grid and P2P Systems was held during September 1-2, 2009 in Linz, Austria. The main objective of this conference was to present the latest results in research and applications, to identify new issues, and to shape future directions.
Data Management in Grids: First VLDB Workshop, DMG 2005, Trondheim, Norway, September 2-3, 2005, Revised Selected Papers (Lecture Notes in Computer Science #3836)
by Jean-Marc PiersonRefereed post-proceedings of the First International Workshop on Data Management in Grids, in Trondheim, Norway. The 11 papers in this book address current research activities in relation to data management in dynamic, heterogeneous and cross-organizational environments, or grids. They show expertise in the management of very large, widely distributed databases. Conversely, Grids offer a novel and exciting field of research for database scientists in application domains and for fundamental research.
Data Management in Machine Learning Systems (Synthesis Lectures on Data Management)
by Matthias Boehm Arun Kumar Jun YangLarge-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scalable manner. Data management is at the heart of many ML systems due to data-driven application characteristics, data-centric workload characteristics, and system architectures inspired by classical data management techniques. In this book, we follow this data-centric view of ML systems and aim to provide a comprehensive overview of data management in ML systems for the end-to-end data science or ML lifecycle. We review multiple interconnected lines of work: (1) ML support in database (DB) systems, (2) DB-inspired ML systems, and (3) ML lifecycle systems. Covered topics include: in-database analytics via query generation and user-defined functions, factorized and statistical-relational learning; optimizing compilers for ML workloads; execution strategies and hardware accelerators; data access methods such as compression, partitioning and indexing; resource elasticity and cloud markets; as well as systems for data preparation for ML, model selection, model management, model debugging, and model serving. Given the rapidly evolving field, we strive for a balance between an up-to-date survey of ML systems, an overview of the underlying concepts and techniques, as well as pointers to open research questions. Hence, this book might serve as a starting point for both systems researchers and developers.
Data Management in Pervasive Systems (Data-Centric Systems and Applications)
by Francesco Colace Massimo De Santo Vincenzo Moscato Antonio Picariello Fabio A. Schreiber Letizia TancaThis book contributes to illustrating the methodological and technological issues of data management in Pervasive Systems by using the DataBenc project as the running case study for a variety of research contributions: sensor data management, user-originated data operation and reasoning, multimedia data management, data analytics and reasoning for event detection and decision making, context modelling and control, automatic data and service tailoring for personalization and recommendation. The book is organized into the following main parts: i) multimedia information management; ii) sensor data streams and storage; iii) social networks as information sources; iv) context awareness and personalization. The case study is used throughout the book as a reference example.
Data Management in the Cloud (Synthesis Lectures on Data Management)
by Divyakant Agrawal Sudipto Das Amr El AbbadiCloud computing has emerged as a successful paradigm of service-oriented computing and has revolutionized the way computing infrastructure is used. This success has seen a proliferation in the number of applications that are being deployed in various cloud platforms. There has also been an increase in the scale of the data generated as well as consumed by such applications. Scalable database management systems form a critical part of the cloud infrastructure. The attempt to address the challenges posed by the management of big data has led to a plethora of systems. This book aims to clarify some of the important concepts in the design space of scalable data management in cloud computing infrastructures. Some of the questions that this book aims to answer are: the appropriate systems for a specific set of application requirements, the research challenges in data management for the cloud, and what is novel in the cloud for database researchers? We also aim to address one basic question: whether cloud computing poses new challenges in scalable data management or it is just a reincarnation of old problems? We provide a comprehensive background study of state-of-the-art systems for scalable data management and analysis. We also identify important aspects in the design of different systems and the applicability and scope of these systems. A thorough understanding of current solutions and a precise characterization of the design space are essential for clearing the "cloudy skies of data management" and ensuring the success of DBMSs in the cloud, thus emulating the success enjoyed by relational databases in traditional enterprise settings. Table of Contents: Introduction / Distributed Data Management / Cloud Data Management: Early Trends / Transactions on Co-located Data / Transactions on Distributed Data / Multi-tenant Database Systems / Concluding Remarks
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 Spyros Blanas Rajesh Bordawekar Tirthankar Lahiri Justin Levandoski Andrew PavloThis 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 Technologies and Applications: 10th International Conference, DATA 2021, Virtual Event, July 6–8, 2021, and 11th International Conference, DATA 2022, Lisbon, Portugal, July 11-13, 2022, Revised Selected Papers (Communications in Computer and Information Science #1860)
by Alfredo Cuzzocrea Oleg Gusikhin Slimane Hammoudi Christoph QuixThis book constitutes the refereed post-proceedings of the 10th International Conference and 11th International Conference on Data Management Technologies and Applications, DATA 2021 and DATA 2022, was held virtually due to the COVID-19 crisis on July 6–8, 2021 and in Lisbon, Portugal on July 11-13, 2022.The 11 full papers included in this book were carefully reviewed and selected from 148 submissions. They were organized in topical sections as follows: engineers and practitioners interested on databases, big data, data mining, data management, data security and other aspects of information systems and technology involving advanced applications of data.
Data Management Technologies and Applications: 6th International Conference, DATA 2017, Madrid, Spain, July 24–26, 2017, Revised Selected Papers (Communications in Computer and Information Science #814)
by Joaquim Filipe Jorge Bernardino Christoph QuixThis book constitutes the thoroughly refereed proceedings of the 6th International Conference on Data Management Technologies and Applications, DATA 2017, held in Madrid, Spain, in July 2017. The 13 revised full papers were carefully reviewed and selected from 66 submissions. The papers deal with the following topics: databases, big data, data mining, data management, data security, and other aspects of information systems and technology involving advanced applications of data.
Data Management Technologies and Applications: 5th International Conference, DATA 2016, Colmar, France, July 24-26, 2016, Revised Selected Papers (Communications in Computer and Information Science #737)
by Chiara Francalanci Markus HelfertThis book constitutes the thoroughly refereed proceedings of the Fourth International Conference on Data Technologies and Applications, DATA 2016, held in Colmar, France, in July 2016. The 9 revised full papers were carefully reviewed and selected from 50 submissions. The papers deal with the following topics: databases, data warehousing, data mining, data management, data security, knowledge and information systems and technologies; advanced application of data.
Data Management Technologies and Applications: 8th International Conference, DATA 2019, Prague, Czech Republic, July 26–28, 2019, Revised Selected Papers (Communications in Computer and Information Science #1255)
by Slimane Hammoudi Christoph Quix Jorge BernardinoThis book constitutes the thoroughly refereed proceedings of the 8th International Conference on Data Management Technologies and Applications, DATA 2019, held in Prague, Czech Republic, in July 2019. The 8 revised full papers were carefully reviewed and selected from 90 submissions. The papers deal with the following topics: decision support systems, data analytics, data and information quality, digital rights management, big data, knowledge management, ontology engineering, digital libraries, mobile databases, object-oriented database systems, and data integrity.
Data Management Technologies and Applications: 9th International Conference, DATA 2020, Virtual Event, July 7–9, 2020, Revised Selected Papers (Communications in Computer and Information Science #1446)
by Slimane Hammoudi Christoph Quix Jorge BernardinoThis book constitutes the thoroughly refereed proceedings of the 9th International Conference on Data Management Technologies and Applications, DATA 2020, which was supposed to take place in Paris, France, in July 2020. Due to the Covid-19 pandemic the event was held virtually. The 14 revised full papers were carefully reviewed and selected from 70 submissions. The papers deal with the following topics: datamining; decision support systems; data analytics; data and information quality; digital rights management; big data; knowledge management; ontology engineering; digital libraries; mobile databases; object-oriented database systems; data integrity.
Data Management Technologies and Applications: Third International Conference, DATA 2014, Vienna, Austria, August 29-31, 2014, Revised Selected papers (Communications in Computer and Information Science #178)
by Markus Helfert Andreas Holzinger Orlando Belo Chiara FrancalanciThis book constitutes the thoroughly refereed proceedings of the Third International Conference on Data Technologies and Applications, DATA 2014, held in Vienna, Austria, in August 2014. The 12 revised full papers were carefully reviewed and selected from 87 submissions. The papers deal with the following topics: databases, data warehousing, data mining, data management, data security, knowledge and information systems and technologies; advanced application of data.
Data Management Technologies and Applications: 4th International Conference, DATA 2015, Colmar, France, July 20-22, 2015, Revised Selected Papers (Communications in Computer and Information Science #584)
by Markus Helfert Andreas Holzinger Orlando Belo Chiara FrancalanciThis book constitutes the thoroughly refereed proceedings of the Fourth International Conference on Data Technologies and Applications, DATA 2015, held in Colmar, France, in July 2015.The 9 revised full papers were carefully reviewed and selected from 70 submissions. The papers deal with the following topics: databases, data warehousing, data mining, data management, data security, knowledge and information systems and technologies; advanced application of data.
Data Management Technologies and Applications: 7th International Conference, DATA 2018, Porto, Portugal, July 26–28, 2018, Revised Selected Papers (Communications in Computer and Information Science #862)
by Christoph Quix Jorge BernardinoThis book constitutes the thoroughly refereed proceedings of the 7th International Conference on Data Management Technologies and Applications, DATA 2018, held in Porto, Portugal, in July 2018. The 9 revised full papers were carefully reviewed and selected from 69 submissions. The papers deal with the following topics: databases, big data, data mining, data management, data security, and other aspects of information systems and technology involving advanced applications of data.
Data Mangement in Cloud, Grid and P2P Systems: 5th International Conference, Globe 2012, Vienna, Austria, September 5-6, 2012, Proceedings (Lecture Notes in Computer Science #7450)
by Abdelkader Hameurlain Farookh Khadeer Hussain Franck Morvan A. Min TjoaThis book constitutes the refereed proceedings of the 5th International Conference on Data Management in Grid and Peer-to-Peer Systems, Globe 2012, held in Vienna, Austria, in September 2012 in conjunction with DEXA 2012. The 9 revised full papers presented were carefully reviewed and selected from 15 submissions. The papers are organized in topical sections on data management in the cloud, cloud MapReduce and performance evaluation, and data stream systems and distributed data mining.
Data Manipulation with R (Use R!)
by Phil SpectorThe R language provides a rich environment for working with data, especially data to be used for statistical modeling or graphics. Coupled with the large variety of easily available packages, it allows access to both well-established and experimental statistical techniques. However techniques that might make sense in other languages are often very ine?cient in R, but, due to R’s ?- ibility, it is often possible to implement these techniques in R. Generally, the problem with such techniques is that they do not scale properly; that is, as the problem size grows, the methods slow down at a rate that might be unexpected. The goal of this book is to present a wide variety of data - nipulation techniques implemented in R to take advantage of the way that R works,ratherthandirectlyresemblingmethodsusedinotherlanguages. Since this requires a basic notion of how R stores data, the ?rst chapter of the book is devoted to the fundamentals of data in R. The material in this chapter is a prerequisite for understanding the ideas introduced in later chapters. Since one of the ?rst tasks in any project involving data and R is getting the data into R in a way that it will be usable, Chapter 2 covers reading data from a variety of sources (text ?les, spreadsheets, ?les from other programs, etc. ), as well as saving R objects both in native form and in formats that other programs will be able to work with.
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 Mapping for Data Warehouse Design
by Qamar ShahbazData mapping in a data warehouse is the process of creating a link between two distinct data models’ (source and target) tables/attributes. Data mapping is required at many stages of DW life-cycle to help save processor overhead; every stage has its own unique requirements and challenges. Therefore, many data warehouse professionals want to learn data mapping in order to move from an ETL (extract, transform, and load data between databases) developer to a data modeler role. Data Mapping for Data Warehouse Design provides basic and advanced knowledge about business intelligence and data warehouse concepts including real life scenarios that apply the standard techniques to projects across various domains. After reading this book, readers will understand the importance of data mapping across the data warehouse life cycle.Covers all stages of data warehousing and the role of data mapping in eachIncludes a data mapping strategy and techniques that can be applied to many situationsBased on the author’s years of real-world experience designing solutions
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 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 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 LearnCreate custom print media with text styles using Adobe InDesign CC 2017Work with GREPs in conjunction with Character and Paragraph Styles to customize dataBuild a numbering sequence for ticketsCreate single and multiple data mergesWho This Book Is ForStudents, graphic designers, and corporate administrators who need to create documents for events.