BLINKS

Hao He, Haixun Wang, Jun Yang, Philip S. Yu
2007 Proceedings of the 2007 ACM SIGMOD international conference on Management of data - SIGMOD '07  
Query processing over graph-structured data is enjoying a growing number of applications. A top-k keyword search query on a graph nds the top k answers according to some ranking criteria, where each answer is a substructure of the graph containing all query keywords. Current techniques for supporting such queries on general graphs suffer from several drawbacks, e.g., poor worst-case performance, not taking full advantage of indexes, and high memory requirements. To address these problems, we
more » ... pose BLINKS, a bi-level indexing and query processing scheme for top-k keyword search on graphs. BLINKS follows a search strategy with provable performance bounds, while additionally exploiting a bi-level index for pruning and accelerating the search. To reduce the index space, BLINKS partitions a data graph into blocks: The bilevel index stores summary information at the block level to initiate and guide search among blocks, and more detailed information for each block to accelerate search within blocks. Our experiments show that BLINKS offers orders-of-magnitude performance improvement over existing approaches.
doi:10.1145/1247480.1247516 dblp:conf/sigmod/HeWYY07 fatcat:cbrod677w5ex5mhiicfukakrxi