{"type":"ScholarlyArticle","title":"Retrieving Cases from Relational Data-Bases: Another Stride Towards Corporate-Wide Case-Base Systems","authors":[{"name":"Hideo Shimazu","slug":"hideo-shimazu","given":"Hideo","family":"Shimazu","affiliation":""},{"name":"Hiroaki Kitano","slug":"hiroaki-kitano","given":"Hiroaki","family":"Kitano","affiliation":""},{"name":"Akihiro Shibata","slug":"akihiro-shibata","given":"Akihiro","family":"Shibata","affiliation":""}],"abstract":"Vital information for corporate activities is generally stored in large data-bases. While conventional dat~base management systems offer limited query flexibility, systems capable of generating similarity-based queries , such as those seen in case-based reasoning research, would certainly enhance the utility of data resources. This paper describes a method for building case-based systems using a conventional relational dat~base (RDB) . The core of the algorithm is a novel approach to similarity computing in which database query form similarities , rather than similarities of individual cases, are computed. The method uses Standard Query Language (SQL) to achieve nearest neighbor matching, thus allowing similarity-based database retrieval. It has been implemented as a part of the CARET case retrieval tool and evaluated through the use of a newly developed corporate-wide case-based system for a software quality control domain . Experiments have shown the proposed method to provide retrieval results equivalent to those of non-RDB implementation at a sufficiently fast response time.","doi":null,"url":"https://ijcai.org/proceedings/1993/125","pdf":"https://ijcai.org/proceedings/1993/0125.pdf","markdown":"https://ijcai.org/proceedings/1993/125.md","bibtex":"https://ijcai.org/proceedings/1993/bibtex/125","volume":{"slug":"1993","title":"Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1993","url":"https://ijcai.org/proceedings/1993/","year":1993,"location":"Chambéry, France","dates":"","editors":"Ruzena Bajcsy","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Machine Learning","track":"Machine Learning","number":125,"pages":{"first":909,"last":914},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}