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Titlebook: Data Streams; Models and Algorithm Charu C. Aggarwal Book 2007 Springer-Verlag US 2007 algorithm.algorithms.data.data streams.database.freq

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发表于 2025-3-21 18:35:30 | 显示全部楼层 |阅读模式
书目名称Data Streams
副标题Models and Algorithm
编辑Charu C. Aggarwal
视频video
概述Unique in its primary focus on data streams.Includes data streams that perform real-time fraud detection.Includes supplementary material:
丛书名称Advances in Database Systems
图书封面Titlebook: Data Streams; Models and Algorithm Charu C. Aggarwal Book 2007 Springer-Verlag US 2007 algorithm.algorithms.data.data streams.database.freq
描述.Data Streams: Models and Algorithms primarily discusses issues related to the mining aspects of data streams.  Recent progress in hardware technology makes it possible for organizations to store and record large streams of transactional data. For example, even simple daily transactions such as using the credit card or phone result in automated data storage, which brings us to a fairly new topic called data streams...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...Data Streams: Models and Algorithms 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... ..
出版日期Book 2007
关键词algorithm; algorithms; data; data streams; database; frequent pattern mining in streams; models; organizati
版次1
doihttps://doi.org/10.1007/978-0-387-47534-9
isbn_softcover978-1-4614-9768-4
isbn_ebook978-0-387-47534-9Series ISSN 1386-2944
issn_series 1386-2944
copyrightSpringer-Verlag US 2007
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A Survey of Classification Methods in Data Streams,to as data streams. Streaming data is ubiquitous today and it is often a challenging task to store, analyze and visualize such rapid large volumes of data. Most conventional data mining techniques have to be adapted to run in a streaming environment, because of the underlying resource constraints in
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Frequent Pattern Mining in Data Streams, data streams have attracted a lot of research interests. Compared with other streaming queries, frequent pattern mining poses great challenges due to high memory and computational costs, and accuracy requirement of the mining results..In this chapter, we overview the state-of-art techniques to mine
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Multi-Dimensional Analysis of Data Streams Using Stream Cubes,ant characteristic: .. To discover high-level dynamic and evolving characteristics, one may need to perform multi-level, multi-dimensional on-line analytical processing (OLAP) of stream data. Such necessity calls for the investigation of new architectures that may facilitate on-line analytical proce
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The Sliding-Window Computation Model and Results,l and pertinent than older data. In such cases, we would like to answer questions about the data only over the last . most recent data elements (. is a parameter). We formalize this model of computation and answer questions about how much space and computation time is required to solve certain probl
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A Survey of Synopsis Construction in Data Streams,ining algorithms require efficient execution which can be difficult to achieve with a fast data stream. In many cases, it may be acceptable to generate . for such problems. In recent years a number of . have been developed, which can be used in conjunction with a variety of mining and query processi
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