Approximate Query Processing for Big Data in Heterogeneous Databases

被引：5
|
作者：
Muniswamaiah, Manoj [1 ]
Agerwala, Tilak [1 ]
Tappert, Charles C. [1 ]
机构：
[1] Pace Univ, Seidenberg Sch CSIS, New York, NY 10038 USA
来源：
2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2020年
关键词：
Big Data; approximate query processing (AQP); database; query optimizer;
D O I：
10.1109/BigData50022.2020.9378310
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Big Data analytics is used in decision making. It involves heavy computation to obtain exact answers. To alleviate this problem, approximate query processing (AQP) was adopted, which provides approximate results with error bounds. The AQP models which have been proposed are supported only by a single database. In an organization, big data is stored in multiple databases that have different data models. This research aims to provide AQP as a middleware solution using query optimization for heterogeneous databases.
引用
收藏
页码：5765 / 5767
页数：3
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