Abstract
This demo designs and implements a system called FMQO that can support multiple query optimization in federated RDF systems. Given a set of queries posed simultaneously, we propose a heuristic query rewriting-based approach to share the common computation during evaluation of multiple queries. Furthermore, we propose an efficient method to use the interconnection topology between SPARQL endpoints to filter out irrelevant sources and join intermediate results during multiple query evaluation. The experimental studies over both real federated RDF datasets show that the demo is effective, efficient and scalable.
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Acknowledgment
This work was supported by The National Key Research and Development Program of China under grant 2018YFB1003504, NSFC under grant 61702171, 61772191, 61622201, 61472131 and 61532010, Hunan Provincial Natural Science Foundation of China under grant 2018JJ3065, the Fundamental Research Funds for the Central Universities, Science and Technology Key Projects of Hunan Province (Grant No. 2015TP1004, 2016JC2012), and Changsha science and technology project kq1804008.
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Ge, Q., Peng, P., Xu, Z., Zou, L., Qin, Z. (2019). FMQO: A Federated RDF System Supporting Multi-query Optimization. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11642. Springer, Cham. https://doi.org/10.1007/978-3-030-26075-0_30
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DOI: https://doi.org/10.1007/978-3-030-26075-0_30
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