Fast Compression of Large Semantic Web Data using X10

Cheng, L.; Avinash, M.; Kotoulas, S.; Ward, T.; Theodoropoulos, G.

Abstract

The Semantic Web comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of applications that perform computations over large volumes of such information. A common approach to alleviate this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding is particularly prevalent in Semantic Web database systems for this purpose. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we propose an efficient algorithm for fast encoding large Semantic Web data. Specially, we present the detailed implementation of our approach based on the state-of-art asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art approach, we demonstrate a speed-up of 2:6 7:4 and excellent scalability. In the meantime, these results also illustrate the significant potential of the APGAS model for efficient implementation of dictionary encoding and contributes to the engineering of more efficient, larger scale Semantic Web applications.

Citation

Cheng, L., Avinash, M., Kotoulas, S., Ward, T., & Theodoropoulos, G. (2016). Fast Compression of Large Semantic Web Data using X10. IEEE Transactions on Parallel and Distributed Systems, 27(9), 2603-2617. https://doi.org/10.1109/tpds.2015.2496579

• Journal Article Type: Article
• Acceptance Date: Oct 25, 2015
• Online Publication Date: Oct 30, 2015
• Publication Date: Sep 1, 2016
• Deposit Date: Apr 21, 2016
• Publicly Available Date: Apr 21, 2016
• Journal: IEEE Transactions on Parallel and Distributed Systems
• Print ISSN: 1045-9219
• Electronic ISSN: 1558-2183
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 27
• Issue: 9
• Pages: 2603-2617
• DOI: https://doi.org/10.1109/tpds.2015.2496579
• Public URL: https://durham-repository.worktribe.com/output/1386283