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Titlebook: Big Data Analytics and Knowledge Discovery; 19th International C Ladjel Bellatreche,Sharma Chakravarthy Conference proceedings 2017 Springe

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樓主: EVOKE
41#
發(fā)表于 2025-3-28 17:39:02 | 只看該作者
0302-9743 nd Knowledge Discovery, DaWaK 2017, held?in Lyon, France, in August 2017..The 24 revised full papers and 11 short papers presented were carefully reviewed and?selected from 97 submissions. The papers are organized in the following topical?sections: new generation data warehouses design; cloud and No
42#
發(fā)表于 2025-3-28 21:02:03 | 只看該作者
Further Case Studies on the People ThemeBaaS users, for obvious legal and competitive reasons. In this paper, we survey the mechanisms that aim at making databases secure in a cloud environment, and discuss current pitfalls and related research challenges.
43#
發(fā)表于 2025-3-29 02:29:42 | 只看該作者
Enforcing Privacy in Cloud DatabasesBaaS users, for obvious legal and competitive reasons. In this paper, we survey the mechanisms that aim at making databases secure in a cloud environment, and discuss current pitfalls and related research challenges.
44#
發(fā)表于 2025-3-29 06:52:48 | 只看該作者
45#
發(fā)表于 2025-3-29 09:59:36 | 只看該作者
Conference proceedings 2017ms; non-functional requirements satisfaction; machine learning; social media and twitter analysis; sentiment analysis and user influence; knowledge discovery; ?and data flow management and optimization. ?.
46#
發(fā)表于 2025-3-29 12:14:43 | 只看該作者
47#
發(fā)表于 2025-3-29 16:15:15 | 只看該作者
48#
發(fā)表于 2025-3-29 21:41:26 | 只看該作者
https://doi.org/10.1007/978-1-4842-2382-6h data sources. In the paper, we perform a metric comparison among different methodologies, in order to demonstrate that methodologies classified as hybrid, ontology-based, automatic, and agile are tailored for the Big Data context.
49#
發(fā)表于 2025-3-30 00:04:52 | 只看該作者
50#
發(fā)表于 2025-3-30 04:03:54 | 只看該作者
Evaluation of Data Warehouse Design Methodologies in the Context of Big Datah data sources. In the paper, we perform a metric comparison among different methodologies, in order to demonstrate that methodologies classified as hybrid, ontology-based, automatic, and agile are tailored for the Big Data context.
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