Sumrall, J.M. and Fletcher, G.H.L. and Poulovassilis, Alexandra and Svensson, J. and Vejlstrup, M. and Vest, C. and Webber, J. (2016) Investigations on path indexing for graph databases. In: Desprez, F. and Dutot, P.-F. and Kaklamanis, C. and Marchal, L. and Molitorisz, K. and Ricci, L. and Scarano, V. and Vega-Rodriguez, M.A. and Varbanescu, A.L. and Hunold, S. and Scott, S.L. and Lankes, S. and Weidendorfer, J. (eds.) Euro-Par 2016: Parallel Processing Workshops. Lecture Notes in Computer Science 10104. Grenoble, Switzerland: Springer, pp. 532-544. ISBN 9783319589428.
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Abstract
Graph databases have become an increasingly popular choice for the management of the massive network data sets arising in many contemporary applications. We investigate the effectiveness of path indexing for accelerating query processing in graph database systems, using as an exemplar the widely used open-source Neo4j graph database. We present a novel path index design which supports efficient ordered access to paths in a graph dataset. Our index is fully persistent and designed for external memory storage and retrieval. We also describe a compression scheme that exploits the limited differences between consecutive keys in the index, as well as a workload-driven approach to indexing. We demonstrate empirically the speed-ups achieved by our implementation, showing that the path index yields query run-times from 2x up to 8000x faster than Neo4j. Empirical evaluation also shows that our scheme leads to smaller indexes than using general-purpose LZ4 compression. The complete stand-alone implementation of our index, as well as supporting tooling such as a bulk-loader, are provided as open source for further research and development.
Metadata
| Item Type: | Book Section |
|---|---|
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Innovation Management Research, Birkbeck Centre for, Bioinformatics, Bloomsbury Centre for (Closed), Birkbeck Knowledge Lab |
| Depositing User: | Alex Poulovassilis |
| Date Deposited: | 29 Nov 2016 11:29 |
| Last Modified: | 09 Aug 2023 12:39 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/16329 |
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