Download or read book Web Scale Data Management for the Cloud written by Wolfgang Lehner and published by Springer Science & Business Media. This book was released on 2013-04-06 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Cloud Data Management written by Liang Zhao and published by Springer. This book was released on 2014-07-08 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: In practice, the design and architecture of a cloud varies among cloud providers. We present a generic evaluation framework for the performance, availability and reliability characteristics of various cloud platforms. We describe a generic benchmark architecture for cloud databases, specifically NoSQL database as a service. It measures the performance of replication delay and monetary cost. Service Level Agreements (SLA) represent the contract which captures the agreed upon guarantees between a service provider and its customers. The specifications of existing service level agreements (SLA) for cloud services are not designed to flexibly handle even relatively straightforward performance and technical requirements of consumer applications. We present a novel approach for SLA-based management of cloud-hosted databases from the consumer perspective and an end-to-end framework for consumer-centric SLA management of cloud-hosted databases. The framework facilitates adaptive and dynamic provisioning of the database tier of the software applications based on application-defined policies for satisfying their own SLA performance requirements, avoiding the cost of any SLA violation and controlling the monetary cost of the allocated computing resources. In this framework, the SLA of the consumer applications are declaratively defined in terms of goals which are subjected to a number of constraints that are specific to the application requirements. The framework continuously monitors the application-defined SLA and automatically triggers the execution of necessary corrective actions (scaling out/in the database tier) when required. The framework is database platform-agnostic, uses virtualization-based database replication mechanisms and requires zero source code changes of the cloud-hosted software applications.
Download or read book Frontiers in Massive Data Analysis written by National Research Council and published by National Academies Press. This book was released on 2013-09-03 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Large Scale and Big Data written by Sherif Sakr and published by CRC Press. This book was released on 2014-06-25 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Data Management at Scale written by Piethein Strengholt and published by "O'Reilly Media, Inc.". This book was released on 2020-07-29 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Transactions on Large Scale Data and Knowledge Centered Systems XXX written by Abdelkader Hameurlain and published by Springer. This book was released on 2016-12-08 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Transactions on Large Scale Data and Knowledge Centered Systems III written by Abdelkader Hameurlain and published by Springer Science & Business Media. This book was released on 2011-08-02 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 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. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between Grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments. This, the third issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains two kinds of papers: Firstly, a selection of the best papers from the third International Conference on Data Management in Grid and Peer-to-Peer Systems, Globe 2010, and secondly, a selection of 6 papers from the 18 papers submitted in response to the call for papers for this issue. The topics covered by this special issue include replication, the semantic web, information retrieval, data storage, source selection, and large-scale distributed applications.
Download or read book Fast and Scalable Cloud Data Management written by Felix Gessert and published by Springer Nature. This book was released on 2020-05-15 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Large Scale Data Analytics written by Chung Yik Cho and published by Springer. This book was released on 2019-01-09 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Managing Big Data in Cloud Computing Environments written by Zongmin Ma and published by Information Science Reference. This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cloud computing has proven to be a successful paradigm of service-oriented computing, and has revolutionized the way computing infrastructures are abstracted and used. By means of cloud computing technology, massive data can be managed effectively and efficiently to support various aspects of problem solving and decision making. Managing Big Data in Cloud Computing Environments explores the latest advancements in the area of data management and analysis in the cloud. Providing timely, research-based information relating to data storage, sharing, extraction, and indexing in cloud systems, this publication is an ideal reference source for graduate students, IT specialists, researchers, and professionals working in the areas of data and knowledge engineering.
Download or read book Transactions on Large Scale Data and Knowledge Centered Systems XX written by Abdelkader Hameurlain and published by Springer. This book was released on 2015-03-17 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 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. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments. This, the 20th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, presents a representative and useful selection of articles covering a wide range of important topics in the domain of advanced techniques for big data management. Big data has become a popular term, used to describe the exponential growth and availability of data. The recent radical expansion and integration of computation, networking, digital devices, and data storage has provided a robust platform for the explosion in big data, as well as being the means by which big data are generated, processed, shared, and analyzed. In general, data are only useful if meaning and value can be extracted from them. Big data discovery enables data scientists and other analysts to uncover patterns and correlations through analysis of large volumes of data of diverse types. Insights gleaned from big data discovery can provide businesses with significant competitive advantages, leading to more successful marketing campaigns, decreased customer churn, and reduced loss from fraud. In practice, the growing demand for large-scale data processing and data analysis applications has spurred the development of novel solutions from both industry and academia.
Download or read book Data Management in the Cloud written by Divyakant Agrawal and published by Morgan & Claypool Publishers. This book was released on 2012 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Designing Cloud Data Platforms written by Danil Zburivsky and published by Simon and Schuster. This book was released on 2021-04-20 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms. Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is an hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud. As you read, you''ll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. You''ll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyse it. about the technology Access to affordable, dependable, serverless cloud services has revolutionized the way organizations can approach data management, and companies both big and small are raring to migrate to the cloud. But without a properly designed data platform, data in the cloud can remain just as siloed and inaccessible as it is today for most organizations. Designing Cloud Data Platforms lays out the principles of a well-designed platform that uses the scalable resources of the public cloud to manage all of an organization''s data, and present it as useful business insights. about the book In Designing Cloud Data Platforms, you''ll learn how to integrate data from multiple sources into a single, cloud-based, modern data platform. Drawing on their real-world experiences designing cloud data platforms for dozens of organizations, cloud data experts Danil Zburivsky and Lynda Partner take you through a six-layer approach to creating cloud data platforms that maximizes flexibility and manageability and reduces costs. Starting with foundational principles, you''ll learn how to get data into your platform from different databases, files, and APIs, the essential practices for organizing and processing that raw data, and how to best take advantage of the services offered by major cloud vendors. As you progress past the basics you''ll take a deep dive into advanced topics to get the most out of your data platform, including real-time data management, machine learning analytics, schema management, and more. what''s inside The tools of different public cloud for implementing data platforms Best practices for managing structured and unstructured data sets Machine learning tools that can be used on top of the cloud Cost optimization techniques about the reader For data professionals familiar with the basics of cloud computing and distributed data processing systems like Hadoop and Spark. about the authors Danil Zburivsky has over 10 years experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years.
Download or read book Cloud Computing written by Lizhe Wang and published by CRC Press. This book was released on 2017-12-19 with total page 854 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cloud computing has created a shift from the use of physical hardware and locally managed software-enabled platforms to that of virtualized cloud-hosted services. Cloud assembles large networks of virtual services, including hardware (CPU, storage, and network) and software resources (databases, message queuing systems, monitoring systems, and load-balancers). As Cloud continues to revolutionize applications in academia, industry, government, and many other fields, the transition to this efficient and flexible platform presents serious challenges at both theoretical and practical levels—ones that will often require new approaches and practices in all areas. Comprehensive and timely, Cloud Computing: Methodology, Systems, and Applications summarizes progress in state-of-the-art research and offers step-by-step instruction on how to implement it. Summarizes Cloud Developments, Identifies Research Challenges, and Outlines Future Directions Ideal for a broad audience that includes researchers, engineers, IT professionals, and graduate students, this book is designed in three sections: Fundamentals of Cloud Computing: Concept, Methodology, and Overview Cloud Computing Functionalities and Provisioning Case Studies, Applications, and Future Directions It addresses the obvious technical aspects of using Cloud but goes beyond, exploring the cultural/social and regulatory/legal challenges that are quickly coming to the forefront of discussion. Properly applied as part of an overall IT strategy, Cloud can help small and medium business enterprises (SMEs) and governments in optimizing expenditure on application-hosting infrastructure. This material outlines a strategy for using Cloud to exploit opportunities in areas including, but not limited to, government, research, business, high-performance computing, web hosting, social networking, and multimedia. With contributions from a host of internationally recognized researchers, this reference delves into everything from necessary changes in users’ initial mindset to actual physical requirements for the successful integration of Cloud into existing in-house infrastructure. Using case studies throughout to reinforce concepts, this book also addresses recent advances and future directions in methodologies, taxonomies, IaaS/SaaS, data management and processing, programming models, and applications.
Download or read book Transactions on Large Scale Data and Knowledge Centered Systems XLIV written by Abdelkader Hameurlain and published by Springer Nature. This book was released on 2020-09-09 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Applications of Big Data in Large and Small Scale Systems written by Goundar, Sam and published by IGI Global. This book was released on 2021-01-15 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: With new technologies, such as computer vision, internet of things, mobile computing, e-governance and e-commerce, and wide applications of social media, organizations generate a huge volume of data and at a much faster rate than several years ago. Big data in large-/small-scale systems, characterized by high volume, diversity, and velocity, increasingly drives decision making and is changing the landscape of business intelligence. From governments to private organizations, from communities to individuals, all areas are being affected by this shift. There is a high demand for big data analytics that offer insights for computing efficiency, knowledge discovery, problem solving, and event prediction. To handle this demand and this increase in big data, there needs to be research on innovative and optimized machine learning algorithms in both large- and small-scale systems. Applications of Big Data in Large- and Small-Scale Systems includes state-of-the-art research findings on the latest development, up-to-date issues, and challenges in the field of big data and presents the latest innovative and intelligent applications related to big data. This book encompasses big data in various multidisciplinary fields from the medical field to agriculture, business research, and smart cities. While highlighting topics including machine learning, cloud computing, data visualization, and more, this book is a valuable reference tool for computer scientists, data scientists and analysts, engineers, practitioners, stakeholders, researchers, academicians, and students interested in the versatile and innovative use of big data in both large-scale and small-scale systems.
Download or read book Management of Data written by and published by Allied Publishers. This book was released on 2010 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt:
