Extended Adaptive Join Operator with Bind-Bloom Join for Federated SPARQL Queries

Damla Oguz, Shaoyi Yin, Belgin Ergenç, Abdelkader Hameurlain and Oguz Dikenelli
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Damla Oguz: Institute of Research in Computer Science of Toulouse (IRIT), Paul Sabatier University, Toulouse, France & Department of Computer Engineering, Izmir Institute of Technology, Izmir, Turkey & Department of Computer Engineering, Ege University, Izmir, Turkey
Shaoyi Yin: Institute of Research in Computer Science of Toulouse (IRIT), Paul Sabatier University, Toulouse, France
Belgin Ergenç: Department of Computer Engineering, Izmir Institute of Technology, Izmir, Turkey
Abdelkader Hameurlain: Institute of Research in Computer Science of Toulouse (IRIT), Paul Sabatier University, Toulouse, France
Oguz Dikenelli: Department of Computer Engineering, Ege University, Izmir, Turkey

International Journal of Data Warehousing and Mining (IJDWM), 2017, vol. 13, issue 3, 47-72

Abstract: The goal of query optimization in query federation over linked data is to minimize the response time and the completion time. Communication time has the highest impact on them both. Static query optimization can end up with inefficient execution plans due to unpredictable data arrival rates and missing statistics. This study is an extension of adaptive join operator which always begins with symmetric hash join to minimize the response time, and can change the join method to bind join to minimize the completion time. The authors extend adaptive join operator with bind-bloom join to further reduce the communication time and, consequently, to minimize the completion time. They compare the new operator with symmetric hash join, bind join, bind-bloom join, and adaptive join operator with respect to the response time and the completion time. Performance evaluation shows that the extended operator provides optimal response time and further reduces the completion time. Moreover, it has the adaptation ability to different data arrival rates.

Date: 2017
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