A solution of a keyword query over graphs is a Group Steiner tree, which is rooted at a node and whose nodes collectively satisfy the query (e.g. node keywords cover all the query keywords), and in which the sum of edge weights satisfies given conditions (e.g., need to be minimum or be the first K minimal among all possible sub-graphs satisfying the query). Most existing techniques for evaluating keyword queries over graphs run on a centralized computer. We propose a new approach, SOverlapping, to evaluate keyword queries over graphs on MapReduce framework by utilizing probabilistic theory to partition graphs. The new approach has shown to be effective and efficient when tested on real graph data sets.
['Yifan Hao', 'Huiping Cao', 'Yan Qi', 'Chuan Hu', 'Sukumar M. Brahma', 'Jingyu Han']
Efficient keyword search on graphs using MapReduce