Supporting queries with imprecise constraints

Authors:
Ullas Nambiar;Subbarao Kambhampati
Affiliations:
Dept. of Computer Science, University of California, Davis;Dept. of Computer Science, Arizona State University
Venue:
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Year:
2006

Citing 9
Cited 3

Modern Information Retrieval

Modern Information Retrieval
Cooperative Answering through Controlled Query Relaxation

IEEE Expert: Intelligent Systems and Their Applications
Tree Pattern Relaxation

EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
ROCK: A Robust Clustering Algorithm for Categorical Attributes

ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Providing ranked relevant results for web database queries

Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Machine learning for online query relaxation

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Answering Imprecise Queries over Autonomous Web Databases

ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Discover: keyword search in relational databases

VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
BANKS: browsing and keyword searching in relational databases

VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases

A Model for Contextual Cooperative Query Answering in E-Commerce Applications

FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Exploiting correlation to rank database query results

DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Pragmatic correlation analysis for probabilistic ranking over relational data

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we motivate the need for and challenges involved in supporting imprecise queries over Web databases. Then we briefly explain our solution, AIMQ - a domain independent approach for answering imprecise queries that automatically learns query relaxation order by using approximate functional dependencies. We also describe our approach for learning similarity between values of categorical attributes. Finally. we present experimental results demonstrating the robustness, efficiency and effectiveness of AIMQ.