Join processing in database systems with large main memories
ACM Transactions on Database Systems (TODS)
An efficient algorithm for sequential random sampling
ACM Transactions on Mathematical Software (TOMS)
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Top-k selection queries over relational databases: Mapping strategies and performance evaluation
ACM Transactions on Database Systems (TODS)
Supporting Incremental Join Queries on Ranked Inputs
Proceedings of the 27th International Conference on Very Large Data Bases
RankSQL: query algebra and optimization for relational top-k queries
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Supporting top-K join queries in relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
Efficient processing of exact top-k queries over disk-resident sorted lists
The VLDB Journal — The International Journal on Very Large Data Bases
Horizontal partitioning by predicate abstraction and its application to data warehouse design
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
Xplus: a SQL-tuning-aware query optimizer
Proceedings of the VLDB Endowment
An optimal strategy for monitoring top-k queries in streaming windows
Proceedings of the 14th International Conference on Extending Database Technology
TopRecs: Top-k algorithms for item-based collaborative filtering
Proceedings of the 14th International Conference on Extending Database Technology
Monitoring reverse top-k queries over mobile devices
Proceedings of the 10th ACM International Workshop on Data Engineering for Wireless and Mobile Access
Answering top-k queries over a mixture of attractive and repulsive dimensions
Proceedings of the VLDB Endowment
Hi-index | 0.00 |
In this paper, we propose a new method for efficient processing of a top-k join query by its translation into a sequence of range queries, which are generated by performing iterative domain refinement of attributes included in the scoring function. In this process, we exploit the statistics for data distributions of the individual attributes, which in the form of histograms are available to an RDBMS. To improve the performance of our method, we use heuristic techniques to minimize the execution cost of range queries and the number of iterations. We use the PostgreSQL query engine optimizer to prove our theoretical results. We have done exhaustive set of experiments by exploiting different input parameters and by using cross checks to prove the results. We have applied our experiments to the TPC-H benchmark data sets, and the results we obtained confirm the efficiency of our approach.