Logic-Based Integration of Query Answering and Knowledge Discovery

Aragão, Marcelo A. T.; Fernandes, Alvaro A. A.

doi:10.1007/978-3-540-25957-2_7

Cited by 4 publications

(2 citation statements)

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“…Then the learned information is used to relax the constraints in the query that originally returns an empty answer. Aragao and Fernandes (4) proposed a unified foundation for query answering and knowledge discovery. The combined system is called CIDS (Combined Inference Database Systems).…”

Section: Preliminary and Related Workmentioning

confidence: 99%

“…Knowledge, either intentional or extensional, is the key ingredient of intelligence. Many researchers (2)(3)(4) propose to integrate data mining techniques as a knowledge discovery engine to serve an intelligent query answering purpose. We extend this idea by incorporating both virtual mining and materialized views in the query answering system.…”

Section: Introductionmentioning

confidence: 99%

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Query Answering Engine for the Next Generation Database Systems

Kerdprasop

2015

The Proceedings of the 2nd International Conference on Industrial Application Engineering 2015

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The first generation of databases has emerged since 1970's as hierarchical and network systems that unified data collection and manipulation as a single set of data records. The second generation has started since the early 1980 with the concept of relational databases and some variations such as object databases and deductive databases. The idea of moving databases toward the next generation has proposed since the 1990's as a better system to support new kinds of objects structures and business rules. With the advancement of data mining and learning techniques, we propose that the next generation database systems should include the learning capability. We therefore introduce the design of a query answering system that can support the database system to be more intelligent than the current systems. We also conduct some experiments to show the performance of the proposed query rewriting system.

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Section: Preliminary and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%