Hybrid.Poly: Performance Evaluation of Linear Algebra Analytical Extensions

被引:0
|
作者
Podkorytov, Maksim [1 ]
Gubanov, Michael [1 ]
机构
[1] Florida State Univ, Dept Comp Sci, Tallahassee, FL 32306 USA
来源
2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2018年
基金
美国国家科学基金会;
关键词
Linear Algebra; Large-scale Data Management;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Anecdotal evidence suggests that Variety is one of the most challenging problems in Big data research [1]. Different data providers use different data models and formats to represent their data, which causes significant impediment to data scientists, whose goal is to make sense of all relevant data regardless of the source. Hybrid. Poly [2], [3] is the analytical polystore data management system designed to make all data accessible to the analyst, oblivious of the source differences. In this paper, we focus on the in-depth analysis and performance evaluation of the Linear Algebra extensions added to the Hybrid. Poly language.
引用
收藏
页码:5430 / 5432
页数:3
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