Logic-based approach to semantic query optimization
ACM Transactions on Database Systems (TODS)
A method for automatic rule derivation to support semantic query optimization
ACM Transactions on Database Systems (TODS)
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A database perspective on knowledge discovery
Communications of the ACM
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Inductive databases and condensed representations for data mining (extended abstract)
ILPS '97 Proceedings of the 1997 international symposium on Logic programming
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
Intelligent Query Answering by Knowledge Discovery Techniques
IEEE Transactions on Knowledge and Data Engineering
A Tightly-Coupled Architecture for Data Mining
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Querying Inductive Databases: A Case Study on the MINE RULE Operator
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Modeling KDD Processes within the Inductive Database Framework
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
A perspective on inductive databases
ACM SIGKDD Explorations Newsletter
A Theory of Inductive Query Answering
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Intelligent Query Answering Based on Neighborhood Systems and Data Mining Techniques
IDEAS '04 Proceedings of the International Database Engineering and Applications Symposium
Semantic Query Transformation Using Ontologies
IDEAS '05 Proceedings of the 9th International Database Engineering & Application Symposium
QUIST: a system for semantic query optimization in relational databases
VLDB '81 Proceedings of the seventh international conference on Very Large Data Bases - Volume 7
Knowledge-based query processing
VLDB '80 Proceedings of the sixth international conference on Very Large Data Bases - Volume 6
Brighthouse: an analytic data warehouse for ad-hoc queries
Proceedings of the VLDB Endowment
Rough Sets in Data Warehousing
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Data warehouse technology by infobright
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Intelligent Data Granulation on Load: Improving Infobright's Knowledge Grid
FGIT '09 Proceedings of the 1st International Conference on Future Generation Information Technology
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We study query evaluation within a framework of inductive databases. An inductive database is a concept of the next generation database in that the repository should contain not only persistent and derived data, but also the patterns of stored data in a unified format. Hence, the database management system should support both data processing and data mining tasks. Having provided with a tightly-coupling environment, users can then interact with the system to create, access, and modify data as well as to induce and query mining patterns. In this paper, we present a framework and techniques of query evaluation in such an environment so that the induced patterns can play a key role as semantic knowledge in the query rewriting and optimization process. Our knowledge induction approach is based on rough set theory. We present the knowledge induction algorithm driven by a user's query and explain the method through running examples. The advantages of the proposed techniques are confirmed with experimental results.