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SShare: a simulator for studying and evaluating decentralized SPARQL query processing

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Abstract

Previously, we proposed efficient, scalable decentralized processing of SPARQL queries for an ad hoc Semantic Web data sharing system and explored optimization techniques. However, it has proven to be difficult to measure the performance of the proposed query processing in a decentralized setting with existing tools. This is because assessments on SPARQL query performance were typically targeted at a centralized or single-machine settings, and node-to-node communication costs occurring when (sub-)queries were forwarded among multiple nodes have rarely been taken into consideration. We hereby developed a simulator, SShare, that bridges Jena, a Java framework that supports querying RDF data with SPARQL, and ns-3 (network simulator 3), a discrete-event network simulator using C++ and Python. With SShare, one can submit any proper SPARQL query that involves RDF data of interest scattered around distributed hosts (the details of which are unknown to the query initiator), evaluate important performance metrics (e.g., the inter-site data transmission volume and communication delay) obtained at the network level, and finally get visualized results. We anticipated that SShare would be beneficial to others who are keen on better capturing and analyzing the inherent feature of various distributed and decentralized SPARQL processing mechanisms over a large-scale network.

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Notes

  1. We borrowed the term from ad hoc networking in that the ad hoc environment for Semantic Web data sharing has many features with ad hoc networking in common: no centralized authority, self-organization, multiple nodes connected by links, and dynamics.

  2. The source code and documentation for SShare are under constant maintenance and development, and can be accessed via http://sshare.sinaapp.com.

  3. Chord provides a unique mapping between an identifier space and a set of nodes; each node is therefore associated with an identifier. Chord maps an identifier, say id, to a node with the smallest identifier greater than id and the node is called the successor (node) of id.

  4. This triple-indexing approach was also presented by Atlas [7] in a similar way.

  5. Put it simply, Chord uses a hash function SHA-1 to get the key identifier Hash(key) of a given key and then stores it at its successor node.

  6. Chord was unable to provide the functionality required for this purpose since it merely associates identifiers with successor nodes. We, therefore, adopted DHASH [5] as shown in Fig. 5.

  7. We proposed to apply the move-small strategy, when evaluating a SPARQL query that contains more than two conjunction graph patterns, to resolve the query in an optimized fashion by using the frequency information (see Sect. 2) available in the location table of related index nodes [15].

  8. Information[1].query and Information[3].query are the same but they are associated with different keys. We distinguish between them to point out that Information[1].query obtains its answer directly from storage nodes. Subsequently, the answer to Information[3].query is acquired by running the query against a merged RDF graph consisting of individual RDF graphs collected by running Information[1].query as mentioned earlier.

  9. A solution mapping can be broken down into a set of tuples that contain variables and their corresponding values in RDF terms [10].

  10. http://www.pudn.com/downloads448/sourcecode/java/detail1890872.html.

  11. The ChordIpv4 module was developed by Harjot Gill to support the Chord/DHASH, see http://code.nsnam.org/gillh/ns-3-chord/.

  12. The function is frequently used during the construction of location tables. For example, when a node that has a triple (a:person b:name ‘jason’) as in Fig. 1 joins the Semantic Web data sharing system, an index on its subject needs to be built and the function insert(Index(s), s:http://a/person) will be invoked.

  13. We tested with different ratios of index nodes to storage nodes and found that the more index nodes the shorter the response time of queries. This is because less index nodes indicate that the probability of any two (or more) queries being forwarded to the same index node will be higher; due to the limitation of bandwidth, it is very likely to take longer time to respond to these queries.

  14. We set the default value for the transmission rate, propagation delay, and MTU as in ns-3.

  15. The maximum number of the RDF triple copies is a tunable parameter.

  16. http://www.nsnam.org/

  17. http://www.riverbed.com/.

  18. http://tetcos.com/.

  19. http://librdf.org/.

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Acknowledgments

This work was funded by the Engineering Disciplines Planning Project of the Communication University of China (No. 3132014XNG1453) and the National Key Technology R&D Program (No. 2013BAH66F02). The authors also acknowledge the input of PAPD and CICAEET.

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Authors and Affiliations

  1. School of Computer Science, Communication University of China, Beijing, 100024, China

    Jing Zhou & Qi Huang

  2. PLA Logistics Academy, Beijing, 100858, China

    Weifeng Xie

  3. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Zhiguo Qu

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  1. Jing Zhou
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  2. Qi Huang
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  3. Weifeng Xie
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  4. Zhiguo Qu
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Correspondence to Zhiguo Qu.

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Zhou, J., Huang, Q., Xie, W. et al. SShare: a simulator for studying and evaluating decentralized SPARQL query processing. Pers Ubiquit Comput 19, 1087–1097 (2015). https://doi.org/10.1007/s00779-015-0878-4

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  • DOI: https://doi.org/10.1007/s00779-015-0878-4

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