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Author: Lukasz Golab
Publisher: Morgan & Claypool Publishers
ISBN: 1608452727
Category : Computers
Languages : en
Pages : 65
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Book Description
In this lecture many applications process high volumes of streaming data, among them Internet traffic analysis, financial tickers, and transaction log mining. In general, a data stream is an unbounded data set that is produced incrementally over time, rather than being available in full before its processing begins. In this lecture, we give an overview of recent research in stream processing, ranging from answering simple queries on high-speed streams to loading real-time data feeds into a streaming warehouse for off-line analysis. We will discuss two types of systems for end-to-end stream processing: Data Stream Management Systems (DSMSs) and Streaming Data Warehouses (SDWs). A traditional database management system typically processes a stream of ad-hoc queries over relatively static data. In contrast, a DSMS evaluates static (long-running) queries on streaming data, making a single pass over the data and using limited working memory. In the first part of this lecture, we will discuss research problems in DSMSs, such as continuous query languages, non-blocking query operators that continually react to new data, and continuous query optimization. The second part covers SDWs, which combine the real-time response of a DSMS by loading new data as soon as they arrive with a data warehouse's ability to manage Terabytes of historical data on secondary storage. Table of Contents: Introduction / Data Stream Management Systems / Streaming Data Warehouses / Conclusions
Author: Lukasz Golab
Publisher: Morgan & Claypool Publishers
ISBN: 1608452727
Category : Computers
Languages : en
Pages : 65
Get Book Here
Book Description
In this lecture many applications process high volumes of streaming data, among them Internet traffic analysis, financial tickers, and transaction log mining. In general, a data stream is an unbounded data set that is produced incrementally over time, rather than being available in full before its processing begins. In this lecture, we give an overview of recent research in stream processing, ranging from answering simple queries on high-speed streams to loading real-time data feeds into a streaming warehouse for off-line analysis. We will discuss two types of systems for end-to-end stream processing: Data Stream Management Systems (DSMSs) and Streaming Data Warehouses (SDWs). A traditional database management system typically processes a stream of ad-hoc queries over relatively static data. In contrast, a DSMS evaluates static (long-running) queries on streaming data, making a single pass over the data and using limited working memory. In the first part of this lecture, we will discuss research problems in DSMSs, such as continuous query languages, non-blocking query operators that continually react to new data, and continuous query optimization. The second part covers SDWs, which combine the real-time response of a DSMS by loading new data as soon as they arrive with a data warehouse's ability to manage Terabytes of historical data on secondary storage. Table of Contents: Introduction / Data Stream Management Systems / Streaming Data Warehouses / Conclusions
Author: Minos Garofalakis
Publisher: Springer
ISBN: 354028608X
Category : Computers
Languages : en
Pages : 528
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Book Description
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.
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: 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: 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: Gerardus Blokdyk
Publisher: 5starcooks
ISBN: 9780655317593
Category :
Languages : en
Pages : 278
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Book Description
Does Data stream management system appropriately measure and monitor risk? What are the expected benefits of Data stream management system to the business? Can we do Data stream management system without complex (expensive) analysis? Is the Data stream management system scope manageable? What are the rough order estimates on cost savings/opportunities that Data stream management system brings? This one-of-a-kind Data stream management system self-assessment will make you the principal Data stream management system domain veteran by revealing just what you need to know to be fluent and ready for any Data stream management system challenge. How do I reduce the effort in the Data stream management system work to be done to get problems solved? How can I ensure that plans of action include every Data stream management system task and that every Data stream management system outcome is in place? How will I save time investigating strategic and tactical options and ensuring Data stream management system costs are low? How can I deliver tailored Data stream management system advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Data stream management system essentials are covered, from every angle: the Data stream management system self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that Data stream management system outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Data stream management system practitioners. Their mastery, combined with the easy elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Data stream management system are maximized with professional results. Your purchase includes access details to the Data stream management system self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows you exactly what to do next. Your exclusive instant access details can be found in your book. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard, and... - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation ...plus an extra, special, resource that helps you with project managing. INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.
Author: Lukasz Golab
Publisher: Springer Nature
ISBN: 3031018370
Category : Computers
Languages : en
Pages : 65
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Book Description
Many applications process high volumes of streaming data, among them Internet traffic analysis, financial tickers, and transaction log mining. In general, a data stream is an unbounded data set that is produced incrementally over time, rather than being available in full before its processing begins. In this lecture, we give an overview of recent research in stream processing, ranging from answering simple queries on high-speed streams to loading real-time data feeds into a streaming warehouse for off-line analysis. We will discuss two types of systems for end-to-end stream processing: Data Stream Management Systems (DSMSs) and Streaming Data Warehouses (SDWs). A traditional database management system typically processes a stream of ad-hoc queries over relatively static data. In contrast, a DSMS evaluates static (long-running) queries on streaming data, making a single pass over the data and using limited working memory. In the first part of this lecture, we will discuss research problems in DSMSs, such as continuous query languages, non-blocking query operators that continually react to new data, and continuous query optimization. The second part covers SDWs, which combine the real-time response of a DSMS by loading new data as soon as they arrive with a data warehouse's ability to manage Terabytes of historical data on secondary storage. Table of Contents: Introduction / Data Stream Management Systems / Streaming Data Warehouses / Conclusions
Author: Gerard Blokdyk
Publisher: Createspace Independent Publishing Platform
ISBN: 9781719556743
Category :
Languages : en
Pages : 140
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Book Description
How did the Data stream management system manager receive input to the development of a Data stream management system improvement plan and the estimated completion dates/times of each activity? Who will be responsible for making the decisions to include or exclude requested changes once Data stream management system is underway? Are there any constraints known that bear on the ability to perform Data stream management system work? How is the team addressing them? When a Data stream management system manager recognizes a problem, what options are available? Do we monitor the Data stream management system decisions made and fine tune them as they evolve? This exclusive Data stream management system self-assessment will make you the assured Data stream management system domain auditor by revealing just what you need to know to be fluent and ready for any Data stream management system challenge. How do I reduce the effort in the Data stream management system work to be done to get problems solved? How can I ensure that plans of action include every Data stream management system task and that every Data stream management system outcome is in place? How will I save time investigating strategic and tactical options and ensuring Data stream management system costs are low? How can I deliver tailored Data stream management system advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Data stream management system essentials are covered, from every angle: the Data stream management system self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that Data stream management system outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Data stream management system practitioners. Their mastery, combined with the easy elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Data stream management system are maximized with professional results. Your purchase includes access details to the Data stream management system self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows you exactly what to do next. Your exclusive instant access details can be found in your book.
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: Gerardus Blokdyk
Publisher: 5starcooks
ISBN: 9781867405108
Category :
Languages : en
Pages : 308
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Book Description
Why improve in the first place? What does your signature ensure? Are you aware of what could cause a problem? How do you recognize an Data Stream Management System objection? What are the long-term Data Stream Management System goals? This premium Data Stream Management System self-assessment will make you the established Data Stream Management System domain expert by revealing just what you need to know to be fluent and ready for any Data Stream Management System challenge. How do I reduce the effort in the Data Stream Management System work to be done to get problems solved? How can I ensure that plans of action include every Data Stream Management System task and that every Data Stream Management System outcome is in place? How will I save time investigating strategic and tactical options and ensuring Data Stream Management System costs are low? How can I deliver tailored Data Stream Management System advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Data Stream Management System essentials are covered, from every angle: the Data Stream Management System self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that Data Stream Management System outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Data Stream Management System practitioners. Their mastery, combined with the easy elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Data Stream Management System are maximized with professional results. Your purchase includes access details to the Data Stream Management System self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows you exactly what to do next. Your exclusive instant access details can be found in your book. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Data Stream Management System Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.
