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

EVOKE

書目名稱Big Data Analytics and Knowledge Discovery影響因子(影響力)




書目名稱Big Data Analytics and Knowledge Discovery影響因子(影響力)學(xué)科排名




書目名稱Big Data Analytics and Knowledge Discovery網(wǎng)絡(luò)公開度




書目名稱Big Data Analytics and Knowledge Discovery網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Big Data Analytics and Knowledge Discovery被引頻次




書目名稱Big Data Analytics and Knowledge Discovery被引頻次學(xué)科排名




書目名稱Big Data Analytics and Knowledge Discovery年度引用




書目名稱Big Data Analytics and Knowledge Discovery年度引用學(xué)科排名




書目名稱Big Data Analytics and Knowledge Discovery讀者反饋




書目名稱Big Data Analytics and Knowledge Discovery讀者反饋學(xué)科排名

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ARIA

https://doi.org/10.1007/978-1-4842-2382-6the issues related to the 5 Vs (Volume, Velocity, Variety, Veracity, and Value). So it is mandatory to support the designer through automatic techniques able to quickly produce a multidimensional schema using and integrating several data sources, which can be also unstructured and, therefore, need a

FRET

https://doi.org/10.1007/978-1-4842-2382-6tics. The more complex they become, the more pressing the need for automated optimization solutions. Optimizing data flows comes in several forms, among which, optimal task ordering is one of the most challenging ones. We take a practical approach; motivated by real-world examples, such as those cap

向外

https://doi.org/10.1007/978-1-4842-6203-0 as open and machine-readable Linked Data. Although this approach allows easier data access and consumption, appropriate mechanisms are still needed to perform proper comparisons of statistical data. Indeed, the lack of an explicit representation of how statistical measures are calculated still hind

Coordinate

https://doi.org/10.1007/978-1-4842-6203-0concurrent data access. The large number of users pose multiple query optimization problems. In a distributed data warehousing system such as Hadoop/Hive, queries are evaluated one at a time and processed with the MapReduce paradigm. The massive query execution usually overloads and slows down the e

不利

Further Case Studies on the People Theme and pay-as-you-go features of cloud computing. However, most data are sensitive to some extent, and data privacy remains one of the top concerns to DBaaS users, for obvious legal and competitive reasons. In this paper, we survey the mechanisms that aim at making databases secure in a cloud environm