Data Stream Mining Processing

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Data Stream Management

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Author : Minos Garofalakis
language : en
Publisher: Springer
Release Date : 2016-07-11

Data Stream Management written by Minos Garofalakis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-11 with Computers categories.

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.

Data Stream Mining Processing

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Author : Sergii Babichev
language : en
Publisher: Springer Nature
Release Date : 2020-11-04

Data Stream Mining Processing written by Sergii Babichev and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-04 with Computers categories.

This book constitutes the proceedings of the third International Conference on Data Stream and Mining and Processing, DSMP 2020, held in Lviv, Ukraine*, in August 2020. The 36 full papers presented in this volume were carefully reviewed and selected from 134 submissions. The papers are organized in topical sections of ​hybrid systems of computational intelligence; machine vision and pattern recognition; dynamic data mining & data stream mining; big data & data science using intelligent approaches. *The conference was held virtually due to the COVID-19 pandemic.

Learning From Data Streams

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Author : João Gama
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-10-11

Learning From Data Streams written by João Gama and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-10-11 with Computers categories.

Processing data streams has raised new research challenges over the last few years. This book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. Applications in security, the natural sciences, and education are presented. The huge bibliography offers an excellent starting point for further reading and future research.

Data Streams

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Author : Charu C. Aggarwal
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-04-03

Data Streams written by Charu C. Aggarwal and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-04-03 with Computers categories.

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.

Adaptivity In Data Stream Mining

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Author : Conny Franke
language : en
Publisher:
Release Date : 2009

Adaptivity In Data Stream Mining written by Conny Franke and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.

In recent years data streams became a ubiquitous source of information, and thus stream mining emerged as a new field in database research. Due to the inherently dynamic nature of data streams, stream mining algorithms benefit from being adaptive to changes in the properties of a data stream. In addition, when stream mining is done in a dynamic environment like a data stream management system or a sensor network, stream mining algorithms also profit from being adaptive to the changing conditions in this environment. This work investigates two kinds of adaptivity in data stream mining. First, a model for quality-driven resource adaptive stream mining is developed. The model is applied to stream mining algorithms so they efficiently utilize available resources to achieve mining results of the highest quality possible. Every stream mining algorithm is unique in its parameters, quality measures, and resource consumption patterns. We generalize these characteristics and develop a model that captures the interactions and correlations between variables involved in the stream mining process. We then express resource adaptive stream mining as a multiobjective optimization problem and use its solution to tune the input parameters of stream mining algorithms, which results in high quality mining and optimal resource utilization. The second topic investigated in this work is feature adaptive stream mining, which is concerned with adjusting the focus of the mining process to interesting features detected in the data stream. This research is motivated by the need to efficiently detect environmental phenomena from sensor data streams. We propose methods to detect and predict heterogeneous outlier regions, which represent areas of environmental phenomena of different intensities. With the help of predictions about the location and size of outlier regions, the sampling rate of individual sensors is adapted such that sensors in the vicinity of environmental phenomena obtain new measurements more frequently than other sensors in the network to allow for a precise and timely region tracking. The research in this work enhances the state-of-the-art in data stream mining as it makes stream mining algorithms more flexible to adapt to changes in the data stream and the mining environment.

Data Stream Mining A Complete Guide

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Author : Gerardus Blokdyk
language : en
Publisher: 5starcooks
Release Date : 2018-04-16

Data Stream Mining A Complete Guide written by Gerardus Blokdyk and has been published by 5starcooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-16 with categories.

Have the types of risks that may impact Data stream mining been identified and analyzed? Are there any easy-to-implement alternatives to Data stream mining? Sometimes other solutions are available that do not require the cost implications of a full-blown project? Is there a critical path to deliver Data stream mining results? What prevents me from making the changes I know will make me a more effective Data stream mining leader? Who is the Data stream mining process owner? This valuable Data stream mining self-assessment will make you the credible Data stream mining domain master by revealing just what you need to know to be fluent and ready for any Data stream mining challenge. How do I reduce the effort in the Data stream mining work to be done to get problems solved? How can I ensure that plans of action include every Data stream mining task and that every Data stream mining outcome is in place? How will I save time investigating strategic and tactical options and ensuring Data stream mining costs are low? How can I deliver tailored Data stream mining 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 mining essentials are covered, from every angle: the Data stream mining self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that Data stream mining outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Data stream mining 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 mining are maximized with professional results. Your purchase includes access details to the Data stream mining 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.

Data Mining And Machine Learning Applications

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Author : Rohit Raja
language : en
Publisher: John Wiley & Sons
Release Date : 2022-01-26

Data Mining And Machine Learning Applications written by Rohit Raja and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-26 with Computers categories.

DATA MINING AND MACHINE LEARNING APPLICATIONS The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. The book features: A review of the state-of-the-art in data mining and machine learning, A review and description of the learning methods in human-computer interaction, Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time, The scope and implementation of a majority of data mining and machine learning strategies. A discussion of real-time problems. Audience Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.

Fundamentals Of Stream Processing

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Author : Henrique C. M. Andrade
language : en
Publisher: Cambridge University Press
Release Date : 2014-02-13

Fundamentals Of Stream Processing written by Henrique C. M. Andrade and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-02-13 with Technology & Engineering categories.

Stream processing is a novel distributed computing paradigm that supports the gathering, processing and analysis of high-volume, heterogeneous, continuous data streams, to extract insights and actionable results in real time. This comprehensive, hands-on guide combining the fundamental building blocks and emerging research in stream processing is ideal for application designers, system builders, analytic developers, as well as students and researchers in the field. This book introduces the key components of the stream computing paradigm, including the distributed system infrastructure, the programming model, design patterns and streaming analytics. The explanation of the underlying theoretical principles, illustrative examples and implementations using the IBM InfoSphere Streams SPL language and real-world case studies provide students and practitioners with a comprehensive understanding of such applications and the middleware that supports them.

Stream Data Mining Algorithms And Their Probabilistic Properties

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Author : Leszek Rutkowski
language : en
Publisher: Springer
Release Date : 2019-03-16

Stream Data Mining Algorithms And Their Probabilistic Properties written by Leszek Rutkowski and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-16 with Technology & Engineering categories.

This book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes how to adapt static decision trees to accommodate data streams; in this regard, new splitting criteria are developed to guarantee that they are asymptotically equivalent to the classical batch tree. Moreover, new decision trees are designed, leading to the original concept of hybrid trees. In turn, nonparametric techniques based on Parzen kernels and orthogonal series are employed to address concept drift in the problem of non-stationary regressions and classification in a time-varying environment. Lastly, an extremely challenging problem that involves designing ensembles and automatically choosing their sizes is described and solved. Given its scope, the book is intended for a professional audience of researchers and practitioners who deal with stream data, e.g. in telecommunication, banking, and sensor networks.

Techniques In Data Stream Mining

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Author : Suk-Man Ivy Tong
language : en
Publisher:
Release Date : 2017-01-26

Techniques In Data Stream Mining written by Suk-Man Ivy Tong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-26 with categories.

This dissertation, "Techniques in Data Stream Mining" by Suk-man, Ivy, Tong, 湯淑敏, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled Techniques in Data Stream Mining submitted by Tong Suk-man Ivy for the degree of Master of Philosophy at The University of Hong Kong in November 2005 Many organizations have been confronted by a data explosion in the last decade, and face the problem of managing very large databases that grow at a rate of sev- eral million records per day. To address this problem, database and data mining communities have recently focused on stream processing, where data arrives in the form of continuous data streams. Efficient stream mining is challenging yet critical. However, it is not feasible to perform traditional data mining algorithms on streaming data is infeasible. They have a number of limitations: 1) Most of the classical mining algorithms take multiple passes over the entire database, but the speed of arrival and the volume of the data streams makes it impossible to store them. 2) Timely response is important in stream applications. Disk-based algorithms are inappropriate. 3) Since only a small representation of the whole dataset is kept, approximate algorithms with high accuracy are needed. This study explores some techniques in data stream mining. In particular, it focuses on data from multiple sensor streams, where each stream represents a sequence of states of a monitored attribute reported by a sensor against time. (In finance, a stream may be a stock, for example.) The first technique proposed in this study is a modification of Vitter's reser-voir sampling algorithm, which can generate a fixed-size uniform sample set from an input stream without a priori knowledge of the size of the stream. Applying reservoir sampling on each stream individually would give a sample of time- uncorrelated points from different sensor streams. That is, the sensor states sampled for different streams do not co-exist within any time span. The sample obtained is therefore useless for answering queries related to associations of the streams. Instead of sampling streams individually, a sample of snapshots taken of the streams at different time instants is generated. This ensures that if the state of a stream in a certain time-span is sampled, the states of other streams in the time-span must be in the sample. The second technique is used in mining frequent patterns from a large sensor network. Data representation of sensor streams affects the efficiency of online mining. Based on the estimation mechanism of the Lossy Counting (LC) algo- rithm, a window based algorithm (ILB) which makes use of interval-list represen- tation is proposed. Experiments on synthetic datasets were conducted to show the efficiency of our ILB algorithms. Experimental results showed that if the sensor network is massive, the ILB algorithms outperform LC by a significant margin. DOI: 10.5353/th_b3473737 Subjects: Database management Data mining