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Web and Big Data

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This book constitutes the thoroughly refereed post-conference proceedings of the First APWeb-WAIM 2017 Workshops, held jointly with the First International Joint Conference APWeb-WAIM 2017, held in Beijing, China, in July 2017. The 25 full papers presented were carefully reviewed and selected from 40 submissions. The papers originating from six workshops present cutting-edge ideas, results, experiences, techniques, and tools from all aspects of web data management with the focus on mobile web data analytics; big spatial data and urban computing; graph data management and analytics; mobility analytics from spatial and social data.
Category: Computers. ISBN: 9783319697802

Practical Big Data Analytics

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Get command of your organizational Big Data using the power of data science and analytics About This Book A perfect companion to boost your Big Data storing, processing, analyzing skills to help you take informed business decisions Work with the best tools such as Apache Hadoop, R, Python, and Spark for NoSQL platforms to perform massive online analyses Get expert tips on statistical inference, machine learning, mathematical modeling, and data visualization for Big Data Who This Book Is For The book is intended for existing and aspiring Big Data professionals who wish to become the go-to person in their organization when it comes to Big Data architecture, analytics, and governance. While no prior knowledge of Big Data or related technologies is assumed, it will be helpful to have some programming experience. What You Will Learn Get a 360-degree view into the world of Big Data, data science and machine learning Broad range of technical and business Big Data analytics topics that caters to the interests of the technical experts as well as corporate IT executives Get hands-on experience with industry-standard Big Data and machine learning tools such as Hadoop, Spark, MongoDB, KDB+ and R Create production-grade machine learning BI Dashboards using R and R Shiny with step-by-step instructions Learn how to combine open-source Big Data, machine learning and BI Tools to create low-cost business analytics applications Understand corporate strategies for successful Big Data and data science projects Go beyond general-purpose analytics to develop cutting-edge Big Data applications using emerging technologies In Detail Big Data analytics relates to the strategies used by organizations to collect, organize and analyze large amounts of data to uncover valuable business insights that otherwise cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization's data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages and BI Tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology with the practical ground reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB and even learn how to write R code for neural networks. By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using different tools and methods articulated in this book. Style and approach This book equips you with a knowle
Category: Computers. ISBN: 9781783554393

Big Digital Forensic Data

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This book provides an in-depth understanding of big data challenges to digital forensic investigations, also known as big digital forensic data. It also develops the basis of using data mining in big forensic data analysis, including data reduction, knowledge management, intelligence, and data mining principles to achieve faster analysis in digital forensic investigations. By collecting and assembling a corpus of test data from a range of devices in the real world, it outlines a process of big data reduction, and evidence and intelligence extraction methods. Further, it includes the experimental results on vast volumes of real digital forensic data. The book is a valuable resource for digital forensic practitioners, researchers in big data, cyber threat hunting and intelligence, data mining and other related areas.
Category: Computers. ISBN: 9789811077623

Exploring Big Historical Data

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The Digital Humanities have arrived at a moment when digital Big Data is becoming more readily available, opening exciting new avenues of inquiry but also new challenges. This pioneering book describes and demonstrates the ways these data can be explored to construct cultural heritage knowledge, for research and in teaching and learning. It helps humanities scholars to grasp Big Data in order to do their work, whether that means understanding the underlying algorithms at work in search engines, or designing and using their own tools to process large amounts of information. Demonstrating what digital tools have to offer and also what 'digital' does to how we understand the past, the authors introduce the many different tools and developing approaches in Big Data for historical and humanistic scholarship, show how to use them, what to be wary of, and discuss the kinds of questions and new perspectives this new macroscopic perspective opens up. Authored 'live' online with ongoing feedback from the wider digital history community, Exploring Big Historical Data breaks new ground and sets the direction for the conversation into the future. It represents the current state-of-the-art thinking in the field and exemplifies the way that digital work can enhance public engagement in the humanities. Exploring Big Historical Data should be the go-to resource for undergraduate and graduate students confronted by a vast corpus of data, and researchers encountering these methods for the first time. It will also offer a helping hand to the interested individual seeking to make sense of genealogical data or digitized newspapers, and even the local historical society who are trying to see the value in digitizing their holdings. The companion website to Exploring Big Historical Data can be found at http://www.themacroscope.org/. On this site you will find code, a discussion forum, essays, and datafiles that accompany this book.
Category: Computers. ISBN: 9781783266081

Principles of Big Data

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Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators. Learn general methods for specifying Big Data in a way that is understandable to humans and to computers Avoid the pitfalls in Big Data design and analysis Understand how to create and use Big Data safely and responsibly with a set of laws, regulations and ethical standards that apply to the acquisition, distribution and integration of Big Data resources
Category: Business. ISBN: 9780124045767

Big Data BigData 2018

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This volume constitutes the proceedings of the 7th International Conference on BIGDATA 2018, held as Part of SCF 2018 in Seattle, WA, USA in June 2018. The 22 full papers together with 10 short papers published in this volume were carefully reviewed and selected from 97 submissions. They are organized in topical sections such as Data analysis, data as a service, services computing, data conversion, data storage, data centers, dataflow architectures, data compression, data exchange, data modeling, databases, and data management.
Category: Computers. ISBN: 9783319943008

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 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

www.ebooks.com/cj.asp?IID=95835158&cjsku=95835158...
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
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