Department of Computer Science
Predicting the optimal ad-hoc index for reachability queries on graph databases
Due to the recent advances in graph databases, a large number of ad-hoc indexes for a fundamental query, in particular, reachability query, have been proposed. The performances of these indexes on different graphs have known to be very different. Worst still, deriving an accurate cost model for selecting the optimal index of a graph database appears to be a daunting task. In this paper, we propose a hierarchical prediction framework, based on neural networks and a set of graph features and a knowledge base on past predictions, to determine the optimal index for a graph database. For ease of presentation, we propose our framework with three structurally distinguishable indexes. Our experiments show that our framework is accurate. © 2011 ACM.
graph indexing, neural networks, reachability queries
Source Publication Title
Proceedings of the 20th ACM international conference on Information and knowledge management
Glasgow, United Kingdom
Link to Publisher's Edition
Deng, Jintian, Fei Liu, Yun Peng, Byron Choi, and Jianliang Xu. "Predicting the optimal ad-hoc index for reachability queries on graph databases." Proceedings of the 20th ACM international conference on Information and knowledge management (2011): 2357-2360.