Parley 发表于 2025-3-28 17:39:02
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 NoCocker 发表于 2025-3-28 21:02:03
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.defuse 发表于 2025-3-29 02:29:42
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.voluble 发表于 2025-3-29 06:52:48
http://reply.papertrans.cn/19/1857/185607/185607_44.png不能强迫我 发表于 2025-3-29 09:59:36
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. .PLIC 发表于 2025-3-29 12:14:43
http://reply.papertrans.cn/19/1857/185607/185607_46.pngVertebra 发表于 2025-3-29 16:15:15
http://reply.papertrans.cn/19/1857/185607/185607_47.png字形刻痕 发表于 2025-3-29 21:41:26
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.舰旗 发表于 2025-3-30 00:04:52
http://reply.papertrans.cn/19/1857/185607/185607_49.pngTEM 发表于 2025-3-30 04:03:54
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.