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Titlebook: Business Intelligence and Big Data; 7th European Summer Esteban Zimányi Conference proceedings 2018 Springer Nature Switzerland AG 2018 bu

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樓主: choleric
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發(fā)表于 2025-3-25 06:59:02 | 只看該作者
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發(fā)表于 2025-3-25 11:02:50 | 只看該作者
978-3-319-96654-0Springer Nature Switzerland AG 2018
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發(fā)表于 2025-3-25 11:51:34 | 只看該作者
,Temporal Data Management – An Overview,erspective, we provide an overview of basic temporal database concepts. Then we survey the state-of-the-art in temporal database research, followed by a coverage of the support for temporal data in the current SQL standard and the extent to which the temporal aspects of the standard are supported by
24#
發(fā)表于 2025-3-25 18:52:05 | 只看該作者
,Three Big Data Tools for a Data Scientist’s Toolbox, in every big data scientists’ toolbox, including approximate frequency counting of frequent items, cardinality estimation of very large sets, and fast nearest neighbor search in huge data collections.
25#
發(fā)表于 2025-3-25 20:19:41 | 只看該作者
Sebastian Müller,Christoph Gusyerspective, we provide an overview of basic temporal database concepts. Then we survey the state-of-the-art in temporal database research, followed by a coverage of the support for temporal data in the current SQL standard and the extent to which the temporal aspects of the standard are supported by
26#
發(fā)表于 2025-3-26 03:09:23 | 只看該作者
Yolande Stolte,Rachael Craufurd Smith in every big data scientists’ toolbox, including approximate frequency counting of frequent items, cardinality estimation of very large sets, and fast nearest neighbor search in huge data collections.
27#
發(fā)表于 2025-3-26 04:35:56 | 只看該作者
An Introduction to Data Profiling,tadata discovery is known as data profiling. Profiling activities range from ad-hoc approaches, such as eye-balling random subsets of the data or formulating aggregation queries, to systematic inference of metadata via profiling algorithms. In this course, we will discuss the importance of data prof
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發(fā)表于 2025-3-26 08:27:15 | 只看該作者
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發(fā)表于 2025-3-26 14:35:22 | 只看該作者
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發(fā)表于 2025-3-26 17:55:04 | 只看該作者
Historical Graphs: Models, Storage, Processing,t corresponds to the state of the graph at the corresponding time instant. There is rich information in the history of the graph not present in just the current snapshot of the graph. In this chapter, we present logical and physical models, query types, systems and algorithms for managing historical
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