A Hypothesis Refinement Method for Summary Discovery in Databases

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As database systems are playing major roles in more and more applications, the amount of information in databases is rapidly growing. In order to comprehend those large volumes of information, computerized summary discovery methods are required. In this paper, we propose a hypothesis refinement method for constructing and evaluating fuzzy hypotheses. Breed on them we propose an effective and robust algorithm to discover simple linguistic summaries. In addition, we present ideas for exploiting discovered summaries to various applications such as querying database knowledge, handling query failures and semantic query optimization.
Publisher
Association for Computing Machinery (ACM)
Issue Date
1993-11
Keywords

knowledge discovery in databases; summary discovery; fuzzy set theory; query failure

Citation

Second Int'l Conf. on Information and Knowledge Management, Serial. 2, Washington D.C., USA, P. 274 - 292

ISBN
0-8979-1626-3
URI
http://hdl.handle.net/10203/2022
Link
http://doi.acm.org/10.1145/170088.170153
Appears in Collection
CS-Conference Papers(학술회의논문)

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