A Pruning-based Approach for Supporting Top-K Join Queries

• Jie Liu, Institute of Computing Technology, Chinese Academy of Sciences, China
• Liang Feng, Institute of Computing Technology, Chinese Academy of Sciences, China
• Chao He, Institute of Computing Technology, Chinese Academy of Sciences, China
• Yunpeng Xing, Institute of Computing Technology, Chinese Academy of Sciences, China

Full text:

Track: Posters

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.

Other items being presented by these speakers

• Semantic Link Based Top-K Join Queries in P2P Networks (Posters Track)