Supporting Incremental Join Queries on Ranked Inputs
Proceedings of the 27th International Conference on Very Large Data Bases
Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Evaluating top-k queries over web-accessible databases
ACM Transactions on Database Systems (TODS)
Management of probabilistic data: foundations and challenges
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Maximally joining probabilistic data
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Supporting top-K join queries in relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Top-k query evaluation with probabilistic guarantees
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
TopX: efficient and versatile top-k query processing for semistructured data
The VLDB Journal — The International Journal on Very Large Data Bases
Monte-Carlo algorithms for enumeration and reliability problems
SFCS '83 Proceedings of the 24th Annual Symposium on Foundations of Computer Science
Efficient Processing of Top-k Queries in Uncertain Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Recommending Join Queries via Query Log Analysis
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
A unified approach to ranking in probabilistic databases
Proceedings of the VLDB Endowment
Top-k linked data query processing
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Being picky: processing top-k queries with set-defined selections
Proceedings of the 21st ACM international conference on Information and knowledge management
TJJE: An efficient algorithm for top-k join on massive data
Information Sciences: an International Journal
Efficient Top-k Keyword Search Over Multidimensional Databases
International Journal of Data Warehousing and Mining
Using a real-time top-k algorithm to mine the most frequent items over multiple streams
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
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We consider the problem of efficiently finding the top-k answers for join queries over web-accessible databases. Classical algorithms for finding top-k answers use branch-and-bound techniques to avoid computing scores of all candidates in identifying the top-k answers. To be able to apply such techniques, it is critical to efficiently compute (lower and upper) bounds and expected scores of candidate answers in an incremental fashion during the evaluation. In this paper, we describe novel techniques for these problems. The first contribution of this paper is a method to efficiently compute bounds for the score of a query result when tuples in tables from the "FROM" clause are discovered incrementally, through either sorted or random access. Our second contribution is an algorithm that, given a set of partially evaluated candidate answers, determines a good order in which to access the tables to minimize wasted efforts in the computation of top-k answers. We evaluate our algorithms on a variety of queries and data sets and demonstrate the significant benefits they provide.