K-Graphs: Selecting Top-k Data Sources for XML Keyword Queries

Existing approaches on XML keyword search mostly focus on querying over single data source. However, searching over hundreds or even thousands of (distributed) data sources by sequentially querying every single data source is extremely high cost, thus it can be impractical. In this paper, we propose an approach for selecting top-k data sources to a given query in order to avoid high cost of search in numerous, potentially irrelevant data sources. The proposed approach can efficiently select top-k mostly relevant data sources without querying over the data sources. We propose a ranking function for measuring the strength of correlation between keywords in a data source and summarize the data sources as keywords correlation graphs (K-Graphs). The top-k relevant data sources will be selected by estimating the relevance of corresponding K-Graphs to the query. Experimental results show that the approach achieves good performance with a variety of experimental parameters.