A pruning-based approach for supporting Top-K join queries

Authors:
Jie Liu;Liang Feng;Yunpeng Xing
Affiliations:
Chinese Academy of Sciences, Beijing, China;Chinese Academy of Sciences, Beijing, China;Chinese Academy of Sciences, Beijing, China
Venue:
Proceedings of the 15th international conference on World Wide Web
Year:
2006

Citing 6
Cited 1

Combining fuzzy information from multiple systems (extended abstract)

PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Ripple joins for online aggregation

SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Optimal aggregation algorithms for middleware

PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Evaluating top-k queries over web-accessible databases

ACM Transactions on Database Systems (TODS)
The Knowledge Grid

The Knowledge Grid
Supporting top-K join queries in relational databases

VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29

Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine

ACM Transactions on Intelligent Systems and Technology (TIST)

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.