Data Stream Management: Processing High-Speed Data Streams (Data-Centric Systems and Applications)

This volume focuses on the theory and practice of __data stream management__, and the novel challenges this emerging domain poses for data-management algorithms, systems, and applications. The collection of chapters, contributed by authorities in the field, offers a comprehensive introduction to both the algorithmic/theoretical foundations of data streams, as well as the streaming systems and applications built in different domains. A short introductory chapter provides a brief summary of some basic data streaming concepts and models, and discusses the key elements of a generic stream query processing architecture. Subsequently, Part I focuses on basic streaming algorithms for some key analytics functions (e.g., quantiles, norms, join aggregates, heavy hitters) over streaming data. Part II then examines important techniques for basic stream mining tasks (e.g., clustering, classification, frequent itemsets). Part III discusses a number of advanced topics on stream processing algorithms, and Part IV focuses on system and language aspects of data stream processing with surveys of influential system prototypes and language designs. Part V then presents some representative applications of streaming techniques in different domains (e.g., network management, financial analytics). Finally, the volume concludes with an overview of current data streaming products and new application domains (e.g. cloud computing, big data analytics, and complex event processing), and a discussion of future directions in this exciting field. The book provides a comprehensive overview of core concepts and technological foundations, as well as various systems and applications, and is of particular interest to students, lecturers and researchers in the area of data stream management. We Live In The Era Of “big Data”: Petabytes Of Digital Information Are Generated Daily, And Need To Be Processed And Analyzed For Interesting Patterns And Trends. Besides Volume, A Defining Characteristic Of Big Data Is Its Velocity; That Is, Data Is Instantiated In The Form Of Continuous, High-speed Data Streams That Arrive At Rapid Rates, And Need To Be Processed And Analyzed On A Continuous (24x7) Basis. Such Data Streams Pose Very Difficult Challenges For Conventional Data-management Architectures, Which Are Built Primarily On The Concept Of Persistent, Static Data Collections. This Volume Focuses On The Theory And Practice Of Data Stream Management, And The Novel Challenges This Emerging Domain Poses For Data-management Algorithms, Systems, And Applications.^ The Collection Of Chapters, Contributed By Authorities In The Field, Offers A Comprehensive Introduction To Both The Algorithmic/theoretical Foundations Of Data Streams, As Well As The Streaming Systems And Applications Built In Different Domains. A Short Introductory Chapter Provides A Brief Summary Of Some Basic Data Streaming Concepts And Models, And Discusses The Key Elements Of A Generic Stream Query Processing Architecture. Subsequently, Part I Focuses On Basic Streaming Algorithms For Some Key Analytics Functions (e.g., Quantiles, Norms, Join Aggregates, Heavy Hitters) Over Streaming Data. Part Ii Then Examines Important Techniques For Basic Stream Mining Tasks (e.g., Clustering, Classification, Frequent Itemsets). Part Iii Discusses A Number Of Advanced Topics On Stream Processing Algorithms, And Part Iv Focuses On System And Language Aspects Of Data Stream Processing With Surveys Of Influential System Prototypes And Language Designs.^ Part V Then Presents Some Representative Applications Of Streaming Techniques In Different Domains (e.g., Network Management, Financial Analytics). Finally, The Volume Concludes With An Overview Of Current Data Streaming Products And New Application Domains (e.g. Cloud Computing, Big Data Analytics, And Complex Event Processing), And A Discussion Of Future Directions In This Exciting Field. The Book Provides A Comprehensive Overview Of Core Concepts And Technological Foundations, As Well As Various Systems And Applications, And Is Of Particular Interest To Students, Lecturers And Researchers In The Area Of Data Stream Management. . Part I: Introduction -- Part Ii: Computation Of Basic Stream Synopses -- Part Iii: Mining Data Streams -- Part Iv: Advanced Topics -- Part V: Systems And Architectures -- Part Vi: Applications. . Edited By Minos Garofalakis, Johannes Gehrke, Rajeev Rastogi. We live in the era of ĺlBig Dataĺl: Petabytes of digital information are generated daily, and need to be processed and analyzed for interesting patterns and trends. Besides volume, a defining characteristic of Big Data is its velocity; that is, data is instantiated in the form of continuous, high-speed data streams that arrive at rapid rates, and need to be processed and analyzed on a continuous (24x7) basis. Such data streams pose very difficult challenges for conventional data-management architectures, which are built primarily on the concept of persistent, static data collections. This volume focuses on the theory and practice of data stream management, and the novel challenges this emerging domain poses for data-management algorithms, systems, and applications. The collection of chapters, contributed by authorities in the field, offers a comprehensive introduction to both the algorithmic/theoretical foundations of data streams, as well as the streaming systems and applications built in different domains. A short introductory chapter provides a brief summary of some basic data streaming concepts and models, and discusses the key elements of a generic stream query processing architecture. Subsequently, Part I focuses on basic streaming algorithms for some key analytics functions (e.g., quantiles, norms, join aggregates, heavy hitters) over streaming data. Part II then examines important techniques for basic stream mining tasks (e.g., clustering, classification, frequent itemsets). Part III discusses a number of advanced topics on stream processing algorithms, and Part IV focuses on system and language aspects of data stream processing with surveys of influential system prototypes and language designs. Part V then presents some representative applications of streaming techniques in different domains (e.g., network management, financial analytics). Finally, the volume concludes with an overview of current data streaming products and new application domains (e.g. cloud computing, big data analytics, and complex event processing), and a discussion of future directions in this exciting field. The book provides a comprehensive overview of core concepts and technological foundations, as well as various systems and applications, and is of particular interest to students, lecturers and researchers in the area of data stream management We live in the era of zBig Datay: Petabytes of digital information are generated daily, and need to be processed and analyzed for interesting patterns and trends. Besides volume, a defining characteristic of Big Data is its velocity; that is, data is instantiated in the form of continuous, high-speed data streams that arrive at rapid rates, and need to be processed and analyzed on a continuous (24x7) basis. Such data streams pose very difficult challenges for conventional data-management architectures, which are built primarily on the concept of persistent, static data collections. This volume focuses on the theory and practice of data stream management, and the novel challenges this emerging domain poses for data-management algorithms, systems, and applications. The collection of chapters, contributed by authorities in the field, offers a comprehensive introduction to both the algorithmic/theoretical foundations of data streams, as well as the streaming systems and applications built in different domains. A short introductory chapter provides a brief summary of some basic data streaming concepts and models, and discusses the key elements of a generic stream query processing architecture. Subsequently, Part I focuses on basic streaming algorithms for some key analytics functions (e.g., quantiles, norms, join aggregates, heavy hitters) over streaming data. Part II then examines important techniques for basic stream mining tasks (e.g., clustering, classification, frequent itemsets). Part III discusses a number of advanced topics on stream processing algorithms, and Part IV focuses on system and language aspects of data stream processing with surveys of influential system prototypes and language designs. Part V then presents some representative applications of streaming techniques in different domains (e.g., network management, financial analytics). Finally, the volume concludes with an overview of current data streaming products and new application domains (e.g. cloud computing, big data analytics, and complex event processing), and a discussion of future directions in this exciting field. The book provides a comprehensive overview of core concepts and technological foundations, as well as various systems and applications, and is of particular interest to students, lecturers and researchers in the area of data stream management "This volume focuses on the theory and practice of data stream management, and the novel challenges this emerging domain poses for data-management algorithms, systems, and applications. The collection of chapters, contributed by authorities in the field, offers a comprehensive introduction to both the algorithmic/theoretical foundations of data streams, as well as the streaming systems and applications built in different domains. A short introductory chapter provides a brief summary of some basic data streaming concepts and models, and discusses the key elements of a generic stream query processing architecture. Subsequently, Part I focuses on basic streaming algorithms for some key analytics functions (e.g., quantiles, norms, join aggregates, heavy hitters) over streaming data. Part II then examines important techniques for basic stream mining tasks (e.g., clustering, classification, frequent itemsets). Part III discusses a number of advanced topics on stream processing algorithms, and Part IV focuses on system and language aspects of data stream processing with surveys of influential system prototypes and language designs. Part V then presents some representative applications of streaming techniques in different domains (e.g., network management, financial analytics). Finally, the volume concludes with an overview of current data streaming products and new application domains (e.g. cloud computing, big data analytics, and complex event processing), and a discussion of future directions in this exciting field. The book provides a comprehensive overview of core concepts and technological foundations, as well as various systems and applications, and is of particular interest to students, lecturers and researchers in the area of data stream management"--Provided by publisher Front Matter....Pages I-VII Data Stream Management: A Brave New World....Pages 1-9 Front Matter....Pages 11-11 Data-Stream Sampling: Basic Techniques and Results....Pages 13-44 Quantiles and Equi-depth Histograms over Streams....Pages 45-86 Join Sizes, Frequency Moments, and Applications....Pages 87-102 Distinct-Values Estimation over Data Streams....Pages 103-119 The Sliding-Window Computation Model and Results....Pages 121-147 Front Matter....Pages 149-165 Clustering Data Streams....Pages 167-167 Mining Decision Trees from Streams....Pages 169-187 Frequent Itemset Mining over Data Streams....Pages 189-208 Temporal Dynamics of On-Line Information Streams....Pages 209-219 Front Matter....Pages 221-238 Sketch-Based Multi-Query Processing over Data Streams....Pages 239-239 Approximate Histogram and Wavelet Summaries of Streaming Data....Pages 241-261 Stable Distributions in Streaming Computations....Pages 263-281 Tracking Queries over Distributed Streams....Pages 283-300 Front Matter....Pages 301-314 STREAM: The Stanford Data Stream Management System....Pages 315-315 The Aurora and Borealis Stream Processing Engines....Pages 317-336 Extending Relational Query Languages for Data Streams....Pages 337-359 Hancock: A Language for Analyzing Transactional Data Streams....Pages 361-386 Sensor Network Integration with Streaming Database Systems....Pages 387-408 Front Matter....Pages 409-428 Stream Processing Techniques for Network Management....Pages 429-429 High-Performance XML Message Brokering....Pages 431-449 Fast Methods for Statistical Arbitrage....Pages 451-471 Adaptive, Automatic Stream Mining....Pages 473-497 Conclusions and Looking Forward....Pages 499-528 ....Pages 529-537 This title presents an in-depth treatment of data stream management, covering basic data stream techniques, data stream synopses, mining data streams, advanced data stream computations, and systems and architectures for data stream management systems