A Pruning-based Approach for Supporting Top-K Join Queries
An important issue arising from large scale data integration is how to efficiently select the top-K ranking answers from multiple sources while minimizing the transmission cost. This paper resolves this issue by proposing an efficient pruning-based approach to answer top-K join queries. The total amount of transmitted data can be greatly reduced by pruning tuples that can not produce the desired join results with a rank value greater than or equal to the rank value generated so far.
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