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Big Data Analytics

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Bringing a practitioner's view to big data analytics, this work examines the drivers behind big data, postulates a set of use cases, identifies sets of solution components, and recommends various implementation approaches. This work also addresses and thoroughly answers key questions on this emerging topic, including What is big data and how is it being used? How can strategic plans for big data analytics be generated? and How does big data change analytics architecture? The author, who has more than 20 years of experience in information management architecture and delivery, has drawn the material from a large breadth of workshops and interviews with business and information technology leaders, providing readers with the latest in evolutionary, revolutionary, and hybrid methodologies of moving forward to the brave new world of big data.
Category: Computers. ISBN: 9781583473801

Big Data in History

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Big Data in History introduces the project to create a world-historical archive, tracing the last four centuries of historical dynamics and change. Chapters address the archive's overall plan, how to interpret the past through a global archive, the missions of gathering records, linking local data into global patterns, and exploring the results.
Category: History. ISBN: 9781137378965

Big Data in Education

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This cutting-edge overview explores big data and the related topic of computer code, examining the implications for education and schooling for today and the near future.
Category: Education. ISBN: 9781473947993

Spatial Big Data Science

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Emerging Spatial Big Data (SBD) has transformative potential in solving many grand societal challenges such as water resource management, food security, disaster response, and transportation. However, significant computational challenges exist in analyzing SBD due to the unique spatial characteristics including spatial autocorrelation, anisotropy, heterogeneity, multiple scales and resolutions which is illustrated in this book. This book also discusses current techniques for, spatial big data science with a particular focus on classification techniques for earth observation imagery big data. Specifically, the authors introduce several recent spatial classification techniques, such as spatial decision trees and spatial ensemble learning. Several potential future research directions are also discussed. This book targets an interdisciplinary audience including computer scientists, practitioners and researchers working in the field of data mining, big data, as well as domain scientists working in earth science (e.g., hydrology, disaster), public safety and public health. Advanced level students in computer science will also find this book useful as a reference.
Category: Technology. ISBN: 9783319601946

Web and Big Data

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This two volume set, LNCS 10366 and 10367, constitutes  the thoroughly refereed proceedings of the First International Joint Conference, APWeb-WAIM 2017, held in Beijing, China in July 2017. The 44 full papers presented together with 32 short papers and 10 demonstrations papers were carefully reviewed and selected from 240 submissions. The papers are organized around the following topics: spatial data processing and data quality; graph data processing; data mining, privacy and semantic analysis; text and log data management; social networks; data mining and data streams; query processing; topic modeling; machine learning; recommendation systems; distributed data processing and applications; machine learning and optimization.
Category: Computers. ISBN: 9783319635781

Big Data Revolution

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Exploit the power and potential of Big Data to revolutionize business outcomes Big Data Revolution is a guide to improving performance, making better decisions, and transforming business through the effective use of Big Data. In this collaborative work by an IBM Vice President of Big Data Products and an Oxford Research Fellow, this book presents inside stories that demonstrate the power and potential of Big Data within the business realm. Readers are guided through tried-and-true methodologies for getting more out of data, and using it to the utmost advantage. This book describes the major trends emerging in the field, the pitfalls and triumphs being experienced, and the many considerations surrounding Big Data, all while guiding readers toward better decision making from the perspective of a data scientist. Companies are generating data faster than ever before, and managing that data has become a major challenge. With the right strategy, Big Data can be a powerful tool for creating effective business solutions – but deep understanding is key when applying it to individual business needs. Big Data Revolution provides the insight executives need to incorporate Big Data into a better business strategy, improving outcomes with innovation and efficient use of technology. Examine the major emerging patterns in Big Data Consider the debate surrounding the ethical use of data Recognize patterns and improve personal and organizational performance Make more informed decisions with quantifiable results In an information society, it is becoming increasingly important to make sense of data in an economically viable way. It can drive new revenue streams and give companies a competitive advantage, providing a way forward for businesses navigating an increasingly complex marketplace. Big Data Revolution provides expert insight on the tool that can revolutionize industries.
Category: Technology. ISBN: 9781118943717

Humanizing Big Data

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Unlock the value of big data and realize the impact it can have both on customer understanding and better business performance with this authoritative guide.
Category: Business. ISBN: 9780749472115

Big Data Demystified

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Big Data Demystified
Category: Business. ISBN: 9781292218106

Big Data SMACK

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Learn how to integrate full-stack open source big data architecture and to choose the correct technologyScala/Spark, Mesos, Akka, Cassandra, and Kafkain every layer.  Big data architecture is becoming a requirement for many different enterprises. So far, however, the focus has largely been on collecting, aggregating, and crunching large data sets in a timely manner. In many cases now, organizations need more than one paradigm to perform efficient analyses. Big Data SMACK explains each of the full-stack technologies and, more importantly, how to best integrate them. It provides detailed coverage of the practical benefits of these technologies and incorporates real-world examples in every situation. This book focuses on the problems and scenarios solved by the architecture, as well as the solutions provided by every technology. It covers the six main concepts of big data architecture and how integrate, replace, and reinforce every layer: The language: Scala The engine: Spark (SQL, MLib, Streaming, GraphX) The container: Mesos, Docker The view: Akka The storage: Cassandra The message broker: Kafka What You Will Learn: Make big data architecture without using complex Greek letter architectures Build a cheap but effective cluster infrastructure Make queries, reports, and graphs that business demands Manage and exploit unstructured and No-SQL data sources Use tools to monitor the performance of your architecture Integrate all technologies and decide which ones replace and which ones reinforce Who This Book Is For: Developers, data architects, and data scientists looking to integrate the most successful big data open stack architecture and to choose the correct technology in every layer
Category: Computers. ISBN: 9781484221747

Big Data Analytics

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This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome;  graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.
Category: Business. ISBN: 9788132236269
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