Download or read book Data Stream Management written by Lukasz Golab and published by Morgan & Claypool Publishers. This book was released on 2010 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Data Stream Management written by Minos Garofalakis and published by Springer. This book was released on 2016-07-11 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Streams written by S. Muthukrishnan and published by Now Publishers Inc. This book was released on 2005 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Stream Management written by Lukasz Golab and published by Springer Nature. This book was released on 2022-06-01 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Data Stream Management System a Complete Guide written by Gerardus Blokdyk and published by 5starcooks. This book was released on 2018-07-21 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Streams written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2007-04-03 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Stream Management System written by Gerard Blokdyk and published by Createspace Independent Publishing Platform. This book was released on 2018-05-25 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Machine Learning for Data Streams written by Albert Bifet and published by MIT Press. This book was released on 2018-03-16 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Stream Management System A Complete Guide 2020 Edition written by Gerardus Blokdyk and published by 5starcooks. This book was released on 2020-05-15 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data stream management system A Complete Guide written by Gerardus Blokdyk and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data stream management system A Complete Guide.

Download or read book Data Management in Pervasive Systems written by Francesco Colace and published by Springer. This book was released on 2015-10-17 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contributes to illustrating the methodological and technological issues of data management in Pervasive Systems by using the DataBenc project as the running case study for a variety of research contributions: sensor data management, user-originated data operation and reasoning, multimedia data management, data analytics and reasoning for event detection and decision making, context modelling and control, automatic data and service tailoring for personalization and recommendation. The book is organized into the following main parts: i) multimedia information management; ii) sensor data streams and storage; iii) social networks as information sources; iv) context awareness and personalization. The case study is used throughout the book as a reference example.

Download or read book Bio inspired Algorithms for Data Streaming and Visualization Big Data Management and Fog Computing written by Simon James Fong and published by Springer Nature. This book was released on 2020-08-25 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Stream Data Management written by Nauman Chaudhry and published by Springer Science & Business Media. This book was released on 2005-04-14 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Researchers in data management have recently recognized the importance of a new class of data-intensive applications that requires managing data streams, i.e., data composed of continuous, real-time sequence of items. Streaming applications pose new and interesting challenges for data management systems. Such application domains require queries to be evaluated continuously as opposed to the one time evaluation of a query for traditional applications. Streaming data sets grow continuously and queries must be evaluated on such unbounded data sets. These, as well as other challenges, require a major rethink of almost all aspects of traditional database management systems to support streaming applications. Stream Data Management comprises eight invited chapters by researchers active in stream data management. The collected chapters provide exposition of algorithms, languages, as well as systems proposed and implemented for managing streaming data. Stream Data Management is designed to appeal to researchers or practitioners already involved in stream data management, as well as to those starting out in this area. This book is also suitable for graduate students in computer science interested in learning about stream data management.

Download or read book Relational Data Stream Management System Standard Requirements written by Gerardus Blokdyk and published by 5starcooks. This book was released on 2018-08-24 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: What are your results for key measures or indicators of the accomplishment of your Relational data stream management system strategy and action plans, including building and strengthening core competencies? How will variation in the actual durations of each activity be dealt with to ensure that the expected Relational data stream management system results are met? What is Effective Relational data stream management system? How can we incorporate support to ensure safe and effective use of Relational data stream management system into the services that we provide? Who will be responsible for making the decisions to include or exclude requested changes once Relational data stream management system is underway? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role... In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Relational data stream management system investments work better. This Relational data stream management system All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth Relational data stream management system Self-Assessment. Featuring 702 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Relational data stream management system improvements can be made. In using the questions you will be better able to: - diagnose Relational data stream management system projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Relational data stream management system and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Relational data stream management system Scorecard, you will develop a clear picture of which Relational data stream management system areas need attention. Your purchase includes access details to the Relational data stream management system self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. 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.

Download or read book Stream Processing with Apache Flink written by Fabian Hueske and published by O'Reilly Media. This book was released on 2019-04-11 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get started with Apache Flink, the open source framework that powers some of the world’s largest stream processing applications. With this practical book, you’ll explore the fundamental concepts of parallel stream processing and discover how this technology differs from traditional batch data processing. Longtime Apache Flink committers Fabian Hueske and Vasia Kalavri show you how to implement scalable streaming applications with Flink’s DataStream API and continuously run and maintain these applications in operational environments. Stream processing is ideal for many use cases, including low-latency ETL, streaming analytics, and real-time dashboards as well as fraud detection, anomaly detection, and alerting. You can process continuous data of any kind, including user interactions, financial transactions, and IoT data, as soon as you generate them. Learn concepts and challenges of distributed stateful stream processing Explore Flink’s system architecture, including its event-time processing mode and fault-tolerance model Understand the fundamentals and building blocks of the DataStream API, including its time-based and statefuloperators Read data from and write data to external systems with exactly-once consistency Deploy and configure Flink clusters Operate continuously running streaming applications

Download or read book Real Time Stream Data Management written by Wolfram Wingerath and published by Springer. This book was released on 2019-01-02 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: While traditional databases excel at complex queries over historical data, they are inherently pull-based and therefore ill-equipped to push new information to clients. Systems for data stream management and processing, on the other hand, are natively pushoriented and thus facilitate reactive behavior. However, they do not retain data indefinitely and are therefore not able to answer historical queries. The book provides an overview over the different (push-based) mechanisms for data retrieval in each system class and the semantic differences between them. It also provides a comprehensive overview over the current state of the art in real-time databases. It sfirst includes an in-depth system survey of today's real-time databases: Firebase, Meteor, RethinkDB, Parse, Baqend, and others. Second, the high-level classification scheme illustrated above provides a gentle introduction into the system space of data management: Abstracting from the extreme system diversity in this field, it helps readers build a mental model of the available options.

Download or read book Mining of Massive Datasets written by Jure Leskovec and published by Cambridge University Press. This book was released on 2014-11-13 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.