Web-Scale Data Management for the Cloud
Web-Scale Data Management for the Cloud

Author: Wolfgang Lehner

Publisher: Springer Science & Business Media

Published: 2013-04-06

Total Pages: 209

ISBN-13: 1461468566

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The efficient management of a consistent and integrated database is a central task in modern IT and highly relevant for science and industry. Hardly any critical enterprise solution comes without any functionality for managing data in its different forms. Web-Scale Data Management for the Cloud addresses fundamental challenges posed by the need and desire to provide database functionality in the context of the Database as a Service (DBaaS) paradigm for database outsourcing. This book also discusses the motivation of the new paradigm of cloud computing, and its impact to data outsourcing and service-oriented computing in data-intensive applications. Techniques with respect to the support in the current cloud environments, major challenges, and future trends are covered in the last section of this book. A survey addressing the techniques and special requirements for building database services are provided in this book as well.

Frontiers in Massive Data Analysis
Frontiers in Massive Data Analysis

Author: National Research Council

Publisher: National Academies Press

Published: 2013-09-03

Total Pages: 191

ISBN-13: 0309287812

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Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.

Large Scale and Big Data
Large Scale and Big Data

Author: Sherif Sakr

Publisher: CRC Press

Published: 2014-06-25

Total Pages: 640

ISBN-13: 1466581506

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Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments. The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-based deployment models. The book’s second section examines the usage of advanced Big Data processing techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big Data processing such as consistency management, privacy, and security. Supplying a comprehensive summary from both the research and applied perspectives, the book covers recent research discoveries and applications, making it an ideal reference for a wide range of audiences, including researchers and academics working on databases, data mining, and web scale data processing. After reading this book, you will gain a fundamental understanding of how to use Big Data-processing tools and techniques effectively across application domains. Coverage includes cloud data management architectures, big data analytics visualization, data management, analytics for vast amounts of unstructured data, clustering, classification, link analysis of big data, scalable data mining, and machine learning techniques.

Data Management at Scale
Data Management at Scale

Author: Piethein Strengholt

Publisher: O'Reilly Media

Published: 2020-07-29

Total Pages: 348

ISBN-13: 1492054755

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As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including technological developments, regulatory requirements, and privacy concerns Go deep into the Scaled Architecture and learn how the pieces fit together Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata

Fast and Scalable Cloud Data Management
Fast and Scalable Cloud Data Management

Author: Felix Gessert

Publisher: Springer Nature

Published: 2020-05-15

Total Pages: 199

ISBN-13: 3030435067

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The unprecedented scale at which data is both produced and consumed today has generated a large demand for scalable data management solutions facilitating fast access from all over the world. As one consequence, a plethora of non-relational, distributed NoSQL database systems have risen in recent years and today’s data management system landscape has thus become somewhat hard to overlook. As another consequence, complex polyglot designs and elaborate schemes for data distribution and delivery have become the norm for building applications that connect users and organizations across the globe – but choosing the right combination of systems for a given use case has become increasingly difficult as well. To help practitioners stay on top of that challenge, this book presents a comprehensive overview and classification of the current system landscape in cloud data management as well as a survey of the state-of-the-art approaches for efficient data distribution and delivery to end-user devices. The topics covered thus range from NoSQL storage systems and polyglot architectures (backend) over distributed transactions and Web caching (network) to data access and rendering performance in the client (end-user). By distinguishing popular data management systems by data model, consistency guarantees, and other dimensions of interest, this book provides an abstract framework for reasoning about the overall design space and the individual positions claimed by each of the systems therein. Building on this classification, this book further presents an application-driven decision guidance tool that breaks the process of choosing a set of viable system candidates for a given application scenario down into a straightforward decision tree.

Data Management in the Cloud
Data Management in the Cloud

Author: Divyakant Agrawal

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 120

ISBN-13: 3031018958

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Cloud 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

Transactions on Large-Scale Data- and Knowledge-Centered Systems XXX
Transactions on Large-Scale Data- and Knowledge-Centered Systems XXX

Author: Abdelkader Hameurlain

Publisher: Springer

Published: 2016-12-08

Total Pages: 140

ISBN-13: 3662540541

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This, the 30th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains six in-depth papers focusing on the subject of cloud computing. Topics covered within this context include cloud storage, model-driven development, informative modeling, and security-critical systems.

Large Scale Data Analytics
Large Scale Data Analytics

Author: Chung Yik Cho

Publisher: Springer

Published: 2019-01-09

Total Pages: 89

ISBN-13: 3030038920

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This book presents a language integrated query framework for big data. The continuous, rapid growth of data information to volumes of up to terabytes (1,024 gigabytes) or petabytes (1,048,576 gigabytes) means that the need for a system to manage and query information from large scale data sources is becoming more urgent. Currently available frameworks and methodologies are limited in terms of efficiency and querying compatibility between data sources due to the differences in information storage structures. For this research, the authors designed and programmed a framework based on the fundamentals of language integrated query to query existing data sources without the process of data restructuring. A web portal for the framework was also built to enable users to query protein data from the Protein Data Bank (PDB) and implement it on Microsoft Azure, a cloud computing environment known for its reliability, vast computing resources and cost-effectiveness.

Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIV
Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIV

Author: Abdelkader Hameurlain

Publisher: Springer Nature

Published: 2020-09-09

Total Pages: 195

ISBN-13: 3662622718

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The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 44th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains six fully revised and extended papers selected from the 35th conference on Data Management – Principles, Technologies and Applications, BDA 2019. The topics covered include big data, graph data streams, workflow execution in the cloud, privacy in crowdsourcing, secure distributed computing, machine learning, and data mining for recommendation systems.

Data management and visualisation in response to large-scale nuclear emergencies affecting food and agriculture
Data management and visualisation in response to large-scale nuclear emergencies affecting food and agriculture

Author: Food and Agriculture Organization of the United Nations

Publisher: Food & Agriculture Org.

Published: 2019-11-07

Total Pages: 49

ISBN-13: 9251318794

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In a large-scale nuclear emergency affecting food and agriculture, the release of radionuclides to the environment can severely impact the food chain and human health. Up-to-date information of soil, water and crops are pertinent to informing decisions that prevent potentially contaminated products from reaching consumers. However, traditional management and visualisation of data are constrained in response times and decision-making accuracy as they are often not centralized and performed manually. Developments in information technology (IT) allow for Decision Support System (DSS) tools and algorithms to enhance real-time management of large volumes of data and decision-making in a spatio-temporal context. These IT support functions increase the capacity of stakeholders to focus on the most important matters at hand – ensuring food and consumer safety. This publication presents the challenges and solutions of real-time data management, geo-visualisation and decision making, as well as two case-studies of how innovative IT systems can assist in nuclear emergency response affecting food and agriculture. One of the case studies presented is by the Soil and Water Management and Crop Nutrition Laboratory of the Joint FAO/IAEA Division; the other case study by Japanese Competent Authorities in the aftermath of the Fukushima Daiichi Nuclear Power Plant accident.