Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/7134
Title: Extended Adaptive Join Operator With Bind-Bloom Join for Federated Sparql Queries
Authors: Oğuz, Damla
Yin, Shaoyi
Ergenç, Belgin
Hameurlain, Abdelkader
Dikenelli, Oğuz
Keywords: Adaptive query optimization
Bloom filter
Distributed query processing
Join methods
Linked data
Query federation
Publisher: IGI Global Publishing
Source: Oğuz, D., Yin, S., Ergenç, B., Hameurlain, A., and Dikenelli, O. (2017). Extended adaptive join operator with bind-bloom join for federated SPARQL queries. International Journal of Data Warehousing and Mining, 13(3), 47-72. doi:10.4018/IJDWM.2017070103
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.
URI: http://doi.org/10.4018/IJDWM.2017070103
http://hdl.handle.net/11147/7134
ISSN: 1548-3924
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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