A Scalable Graph-Coarsening Based Index for Dynamic Graph Databases
Document Type
Conference Proceeding
Publication Date
2017
DOI
https://doi.org/10.1145/3132847.3133003
Abstract
A graph database D is a collection of graphs. To speed up subgraph query answering on graph databases, indexes are commonly used. State-of-the-art graph database indexes do not adapt or scale well to dynamic graph database use; they are static, and their ability to prune possible search responses to meet user needs worsens over time as databases change and grow. Users can re-mine indexes to gain some improvement, but it is time consuming. Users must also tune numerous parameters on an ongoing basis to optimize performance and can inadvertently worsen the query response time if they do not choose parameters wisely. Recently, a one-pass algorithm has been developed to enhance the performance of frequent subgraphs based indexes by using the algorithm to update them regularly. However, there are some drawbacks, most notably the need to make updates as the query workload changes.
In this paper, we propose a new index based on graph-coarsening to speed up subgraph query answering time in dynamic graph databases. Our index is parameter-free, query-independent, scalable,small enough to store in the main memory, and is simpler and less costly to maintain for database updates. Experimental results show that our index outperforms hybrid-indexes (i.e. indexes updated with one-pass) for query answering time in the case of social network databases, and is comparable with these indexes for frequent and infrequent queries on chemical databases. Our index can be updated up to 60 times faster in comparison to one-pass on dynamic graph databases. Moreover, our index is independent of the query workload for index update and is up to 15 times faster after hybrid-indexes are attuned to query workload.
Publication Information
Kansal, Akshay and Spezzano, Francesca. (2017). "A Scalable Graph-Coarsening Based Index for Dynamic Graph Databases". CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 207-216.