Abstract
Context-free path queries extend regular path queries for increased expressiveness. A context-free grammar is used to recognize accepted paths by their label strings, or traces. Such queries arise naturally in graph analytics, e.g., in bioinformatics applications. Currently, the practical performance of methods for context-free path query evaluation is not well understood. In this work, we study three state of the art context-free path query evaluation methods. We measure the performance of these methods on diverse query workloads on various data sets and compare their results. We showcase how these evaluation methods scale as graphs get bigger and queries become larger or more ambiguous. We conclude that state of the art solutions are not able to cope with large graphs as found in practice.
Original language | English |
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Title of host publication | Proceedings of the 31st International Conference on Scientific and Statistical Database Management, SSDBM 2019 |
Editors | Tanu Malik, Carlos Maltzahn, Ivo Jimenez |
Place of Publication | New York |
Publisher | Association for Computing Machinery, Inc |
Pages | 121-132 |
Number of pages | 12 |
ISBN (Electronic) | 9781450362160 |
DOIs | |
Publication status | Published - 23 Jul 2019 |
Event | 31st International Conference on Scientific and Statistical Database Management, SSDBM 2019 - Santa Cruz, United States Duration: 23 Jul 2019 → 25 Jul 2019 |
Conference
Conference | 31st International Conference on Scientific and Statistical Database Management, SSDBM 2019 |
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Country | United States |
City | Santa Cruz |
Period | 23/07/19 → 25/07/19 |