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Titlebook: Spatio-Temporal Data Streams; Zdravko Gali? Book 2016 The Author(s) 2016 Spatio-temporal.Data streams.Geostreaming.Big spatial data.Distri

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發(fā)表于 2025-3-21 19:37:18 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Spatio-Temporal Data Streams
編輯Zdravko Gali?
視頻videohttp://file.papertrans.cn/874/873602/873602.mp4
概述Includes supplementary material:
叢書名稱SpringerBriefs in Computer Science
圖書封面Titlebook: Spatio-Temporal Data Streams;  Zdravko Gali? Book 2016 The Author(s) 2016 Spatio-temporal.Data streams.Geostreaming.Big spatial data.Distri
描述This SpringerBrief presents the fundamental concepts of a specialized class of data stream, spatio-temporal data streams, and demonstrates their distributed processing using Big Data frameworks and platforms. It explores a consistent framework which facilitates a thorough understanding of all different facets of the technology, from basic definitions to state-of-the-art techniques. Key topics include spatio-temporal continuous queries, distributed stream processing, SQL-like language embedding, and trajectory stream clustering.?.?.Over the course of the book, the reader will become familiar with spatio-temporal data streams management and data flow processing, which enables the analysis of huge volumes of location-aware continuous data streams. Applications range from mobile object tracking and real-time intelligent transportation systems to traffic monitoring and complex event processing..?.Spatio-Temporal Data Streams is a valuable resource for researchers studying spatio-temporal data streams and Big Data analytics, as well as data engineers and data scientists solving data management and analytics problems associated with this class of data..
出版日期Book 2016
關(guān)鍵詞Spatio-temporal; Data streams; Geostreaming; Big spatial data; Distributed processing; Complex event proc
版次1
doihttps://doi.org/10.1007/978-1-4939-6575-5
isbn_softcover978-1-4939-6573-1
isbn_ebook978-1-4939-6575-5Series ISSN 2191-5768 Series E-ISSN 2191-5776
issn_series 2191-5768
copyrightThe Author(s) 2016
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 22:37:46 | 只看該作者
板凳
發(fā)表于 2025-3-22 00:42:51 | 只看該作者
https://doi.org/10.1007/978-1-4939-6575-5Spatio-temporal; Data streams; Geostreaming; Big spatial data; Distributed processing; Complex event proc
地板
發(fā)表于 2025-3-22 06:47:47 | 只看該作者
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發(fā)表于 2025-3-22 10:14:34 | 只看該作者
Introduction,ta mining technologies beyond their limits due to their massively increasing data volumes and demands for real-time processing. This chapter gives an overview of query processing in data stream management systems (DSMS) and the most influential academic prototypes, as well as available commercial pr
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發(fā)表于 2025-3-22 16:36:20 | 只看該作者
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發(fā)表于 2025-3-22 19:51:04 | 只看該作者
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發(fā)表于 2025-3-23 00:31:51 | 只看該作者
Spatio-Temporal Data Stream Clustering,roblem, since the possibility to extract useful information from this peculiar kind of data is crucial in many RFIP application scenarios. Moreover, spatio-temporal data streams pose interesting challenges for their proper representation, thus making the mining process harder than for classical data
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發(fā)表于 2025-3-23 05:17:08 | 只看該作者
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發(fā)表于 2025-3-23 06:54:21 | 只看該作者
Spatio-Temporal Continuous Queries,sses of management systems, DSMSs resemble DBMSs—they process data streams using SQL and operators defined by the relational algebra. This chapter gives an insight into spatio-temporal stream processing at conceptual level, i.e. from the DSMS user perspective.
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