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Author: Riccardo Tommasini
Publisher: Springer Nature
ISBN: 3031153715
Category : Computers
Languages : en
Pages : 170
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Book Description
This book provides a comprehensive overview of core concepts and technological foundations for continuous engineering of Web streams. It presents various systems and applications and includes real-world examples. Last not least, it introduces the readers to RSP4J, a novel open-source project that aims to gather community efforts in software engineering and empirical research. The book starts with an introductory chapter that positions the work by explaining what motivates the design of specific techniques for processing data streams using Web technologies. Chapter 2 briefly summarizes the necessary background concepts and models needed to understand the remaining content of the book. Subsequently, chapter 3 focuses on processing RDF streams, taming data velocity in an open environment characterized by high data variety. It introduces query answering algorithms with RSP-QL and analytics functions over streaming data. Chapter 4 presents the life cycle of streaming linked data, it focuses on publishing streams on the Web as a prerequisite aspect to make data findable and accessible for applications. Chapter 5 touches on the problems of benchmarks and systems that analyze Web streams to foster technological progress. It surveys existing benchmarks and introduces guidelines that may support new practitioners in approaching the issue of continuous analytics. Finally, chapter 6 presents a list of examples and exercises that will help the reader to approach the area, get used to its practices and become confident in its technological possibilities. Overall, this book is mainly written for graduate students and researchers in Web and stream data management. It collects research results and will guide the next generation of researchers and practitioners.
Author: Riccardo Tommasini
Publisher: Springer Nature
ISBN: 3031153715
Category : Computers
Languages : en
Pages : 170
Get Book Here
Book Description
This book provides a comprehensive overview of core concepts and technological foundations for continuous engineering of Web streams. It presents various systems and applications and includes real-world examples. Last not least, it introduces the readers to RSP4J, a novel open-source project that aims to gather community efforts in software engineering and empirical research. The book starts with an introductory chapter that positions the work by explaining what motivates the design of specific techniques for processing data streams using Web technologies. Chapter 2 briefly summarizes the necessary background concepts and models needed to understand the remaining content of the book. Subsequently, chapter 3 focuses on processing RDF streams, taming data velocity in an open environment characterized by high data variety. It introduces query answering algorithms with RSP-QL and analytics functions over streaming data. Chapter 4 presents the life cycle of streaming linked data, it focuses on publishing streams on the Web as a prerequisite aspect to make data findable and accessible for applications. Chapter 5 touches on the problems of benchmarks and systems that analyze Web streams to foster technological progress. It surveys existing benchmarks and introduces guidelines that may support new practitioners in approaching the issue of continuous analytics. Finally, chapter 6 presents a list of examples and exercises that will help the reader to approach the area, get used to its practices and become confident in its technological possibilities. Overall, this book is mainly written for graduate students and researchers in Web and stream data management. It collects research results and will guide the next generation of researchers and practitioners.
Author: Albert Bifet
Publisher: MIT Press
ISBN: 0262346052
Category : Computers
Languages : en
Pages : 255
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Book Description
A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.
Author: Andrew Psaltis
Publisher: Simon and Schuster
ISBN: 1638357242
Category : Computers
Languages : en
Pages : 314
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Book Description
Summary Streaming Data introduces the concepts and requirements of streaming and real-time data systems. The book is an idea-rich tutorial that teaches you to think about how to efficiently interact with fast-flowing data. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology As humans, we're constantly filtering and deciphering the information streaming toward us. In the same way, streaming data applications can accomplish amazing tasks like reading live location data to recommend nearby services, tracking faults with machinery in real time, and sending digital receipts before your customers leave the shop. Recent advances in streaming data technology and techniques make it possible for any developer to build these applications if they have the right mindset. This book will let you join them. About the Book Streaming Data is an idea-rich tutorial that teaches you to think about efficiently interacting with fast-flowing data. Through relevant examples and illustrated use cases, you'll explore designs for applications that read, analyze, share, and store streaming data. Along the way, you'll discover the roles of key technologies like Spark, Storm, Kafka, Flink, RabbitMQ, and more. This book offers the perfect balance between big-picture thinking and implementation details. What's Inside The right way to collect real-time data Architecting a streaming pipeline Analyzing the data Which technologies to use and when About the Reader Written for developers familiar with relational database concepts. No experience with streaming or real-time applications required. About the Author Andrew Psaltis is a software engineer focused on massively scalable real-time analytics. Table of Contents PART 1 - A NEW HOLISTIC APPROACH Introducing streaming data Getting data from clients: data ingestion Transporting the data from collection tier: decoupling the data pipeline Analyzing streaming data Algorithms for data analysis Storing the analyzed or collected data Making the data available Consumer device capabilities and limitations accessing the data PART 2 - TAKING IT REAL WORLD Analyzing Meetup RSVPs in real time
Author: Luke Ruth
Publisher: Simon and Schuster
ISBN: 163835216X
Category : Computers
Languages : en
Pages : 402
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Book Description
Summary Linked Data presents the Linked Data model in plain, jargon-free language to Web developers. Avoiding the overly academic terminology of the Semantic Web, this new book presents practical techniques, using everyday tools like JavaScript and Python. About this Book The current Web is mostly a collection of linked documents useful for human consumption. The evolving Web includes data collections that may be identified and linked so that they can be consumed by automated processes. The W3C approach to this is Linked Data and it is already used by Google, Facebook, IBM, Oracle, and government agencies worldwide. Linked Data presents practical techniques for using Linked Data on the Web via familiar tools like JavaScript and Python. You'll work step-by-step through examples of increasing complexity as you explore foundational concepts such as HTTP URIs, the Resource Description Framework (RDF), and the SPARQL query language. Then you'll use various Linked Data document formats to create powerful Web applications and mashups. Written to be immediately useful to Web developers, this book requires no previous exposure to Linked Data or Semantic Web technologies. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. What's Inside Finding and consuming Linked Data Using Linked Data in your applications Building Linked Data applications using standard Web techniques About the Authors David Wood is co-chair of the W3C's RDF Working Group. Marsha Zaidman served as CS chair at University of Mary Washington. Luke Ruth is a Linked Data developer on the Callimachus Project. Michael Hausenblas led the Linked Data Research Centre. Table of Contents PART 1 THE LINKED DATA WEB Introducing Linked Data RDF: the data model for Linked Consuming Linked Data PART 2 TAMING LINKED DATA Creating Linked Data with SPARQL—querying the Linked PART 3 LINKED DATA IN THE WILD Enhancing results from search RDF database fundamentals Datasets PART 4 PULLING IT ALL TOGETHER Callimachus: a Linked Data Publishing Linked Data—a recap The evolving Web
Author: Tyler Akidau
Publisher: "O'Reilly Media, Inc."
ISBN: 1491983825
Category : Computers
Languages : en
Pages : 362
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Book Description
Streaming data is a big deal in big data these days. As more and more businesses seek to tame the massive unbounded data sets that pervade our world, streaming systems have finally reached a level of maturity sufficient for mainstream adoption. With this practical guide, data engineers, data scientists, and developers will learn how to work with streaming data in a conceptual and platform-agnostic way. Expanded from Tyler Akidau’s popular blog posts "Streaming 101" and "Streaming 102", this book takes you from an introductory level to a nuanced understanding of the what, where, when, and how of processing real-time data streams. You’ll also dive deep into watermarks and exactly-once processing with co-authors Slava Chernyak and Reuven Lax. You’ll explore: How streaming and batch data processing patterns compare The core principles and concepts behind robust out-of-order data processing How watermarks track progress and completeness in infinite datasets How exactly-once data processing techniques ensure correctness How the concepts of streams and tables form the foundations of both batch and streaming data processing The practical motivations behind a powerful persistent state mechanism, driven by a real-world example How time-varying relations provide a link between stream processing and the world of SQL and relational algebra
Author: Michael D. Smith
Publisher: MIT Press
ISBN: 0262534525
Category : Business & Economics
Languages : en
Pages : 229
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Book Description
How big data is transforming the creative industries, and how those industries can use lessons from Netflix, Amazon, and Apple to fight back. “[The authors explain] gently yet firmly exactly how the internet threatens established ways and what can and cannot be done about it. Their book should be required for anyone who wishes to believe that nothing much has changed.” —The Wall Street Journal “Packed with examples, from the nimble-footed who reacted quickly to adapt their businesses, to laggards who lost empires.” —Financial Times Traditional network television programming has always followed the same script: executives approve a pilot, order a trial number of episodes, and broadcast them, expecting viewers to watch a given show on their television sets at the same time every week. But then came Netflix's House of Cards. Netflix gauged the show's potential from data it had gathered about subscribers' preferences, ordered two seasons without seeing a pilot, and uploaded the first thirteen episodes all at once for viewers to watch whenever they wanted on the devices of their choice. In this book, Michael Smith and Rahul Telang, experts on entertainment analytics, show how the success of House of Cards upended the film and TV industries—and how companies like Amazon and Apple are changing the rules in other entertainment industries, notably publishing and music. We're living through a period of unprecedented technological disruption in the entertainment industries. Just about everything is affected: pricing, production, distribution, piracy. Smith and Telang discuss niche products and the long tail, product differentiation, price discrimination, and incentives for users not to steal content. To survive and succeed, businesses have to adapt rapidly and creatively. Smith and Telang explain how. How can companies discover who their customers are, what they want, and how much they are willing to pay for it? Data. The entertainment industries, must learn to play a little “moneyball.” The bottom line: follow the data.
Author: Simon James Fong
Publisher: Springer Nature
ISBN: 981156695X
Category : Technology & Engineering
Languages : en
Pages : 228
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Book Description
This book aims to provide some insights into recently developed bio-inspired algorithms within recent emerging trends of fog computing, sentiment analysis, and data streaming as well as to provide a more comprehensive approach to the big data management from pre-processing to analytics to visualization phases. The subject area of this book is within the realm of computer science, notably algorithms (meta-heuristic and, more particularly, bio-inspired algorithms). Although application domains of these new algorithms may be mentioned, the scope of this book is not on the application of algorithms to specific or general domains but to provide an update on recent research trends for bio-inspired algorithms within a specific application domain or emerging area. These areas include data streaming, fog computing, and phases of big data management. One of the reasons for writing this book is that the bio-inspired approach does not receive much attention but shows considerable promise and diversity in terms of approach of many issues in big data and streaming. Some novel approaches of this book are the use of these algorithms to all phases of data management (not just a particular phase such as data mining or business intelligence as many books focus on); effective demonstration of the effectiveness of a selected algorithm within a chapter against comparative algorithms using the experimental method. Another novel approach is a brief overview and evaluation of traditional algorithms, both sequential and parallel, for use in data mining, in order to provide an overview of existing algorithms in use. This overview complements a further chapter on bio-inspired algorithms for data mining to enable readers to make a more suitable choice of algorithm for data mining within a particular context. In all chapters, references for further reading are provided, and in selected chapters, the author also include ideas for future research.
Author: Charu C. Aggarwal
Publisher: Springer Science & Business Media
ISBN: 0387475346
Category : Computers
Languages : en
Pages : 365
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Book Description
This book primarily discusses issues related to the mining aspects of data streams and it is unique in its primary focus on the subject. This volume covers mining aspects of data streams comprehensively: each contributed chapter contains a survey on the topic, the key ideas in the field for that particular topic, and future research directions. The book is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for advanced-level students in computer science.
Author: S. Muthukrishnan
Publisher: Now Publishers Inc
ISBN: 193301914X
Category : Computers
Languages : en
Pages : 136
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Book Description
In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerged for reasoning about algorithms that work within these constraints on space, time, and number of passes. Some of the methods rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity. The applications for this scenario include IP network traffic analysis, mining text message streams and processing massive data sets in general. Researchers in Theoretical Computer Science, Databases, IP Networking and Computer Systems are working on the data stream challenges.
Author: Byron Ellis
Publisher: John Wiley & Sons
ISBN: 1118838025
Category : Computers
Languages : en
Pages : 432
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Book Description
Construct a robust end-to-end solution for analyzing and visualizing streaming data Real-time analytics is the hottest topic in data analytics today. In Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data, expert Byron Ellis teaches data analysts technologies to build an effective real-time analytics platform. This platform can then be used to make sense of the constantly changing data that is beginning to outpace traditional batch-based analysis platforms. The author is among a very few leading experts in the field. He has a prestigious background in research, development, analytics, real-time visualization, and Big Data streaming and is uniquely qualified to help you explore this revolutionary field. Moving from a description of the overall analytic architecture of real-time analytics to using specific tools to obtain targeted results, Real-Time Analytics leverages open source and modern commercial tools to construct robust, efficient systems that can provide real-time analysis in a cost-effective manner. The book includes: A deep discussion of streaming data systems and architectures Instructions for analyzing, storing, and delivering streaming data Tips on aggregating data and working with sets Information on data warehousing options and techniques Real-Time Analytics includes in-depth case studies for website analytics, Big Data, visualizing streaming and mobile data, and mining and visualizing operational data flows. The book's "recipe" layout lets readers quickly learn and implement different techniques. All of the code examples presented in the book, along with their related data sets, are available on the companion website.
