{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:04:28Z","timestamp":1755219868404,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>Japanese public healthcare insurance claims are a promising population-scale dataset to offer tangible evidence for healthcare policy making. The dataset has employed a nested-tuple form, which no known techniques can efficiently transform to the standard relational data model. This paper explores the technical benefit of allowing vector attributes in the relational database for managing public healthcare insurance claims. The experiment with the commercial implementation and Japanese population-scale dataset confirms that the proposed approach performs much (up to 3.4 times) faster for analytical queries having compositive predicates, while it only needs 10% penalty time for data preprocessing and database import.<\/jats:p>","DOI":"10.3233\/shti251122","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:41:42Z","timestamp":1754566902000},"source":"Crossref","is-referenced-by-count":0,"title":["Allowing Vector Attributes in Relational Database for Japanese Insurance Claims"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6032-1584","authenticated-orcid":false,"given":"Jumpei","family":"Sato","sequence":"first","affiliation":[{"name":"The University of Tokyo, Tokyo, Japan"},{"name":"Hitachi, Ltd., Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuo","family":"Goda","sequence":"additional","affiliation":[{"name":"The University of Tokyo, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroyasu","family":"Kiba","sequence":"additional","affiliation":[{"name":"Hitachi, Ltd., Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taro","family":"Fujimoto","sequence":"additional","affiliation":[{"name":"Hitachi, Ltd., Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shinji","family":"Fujiwara","sequence":"additional","affiliation":[{"name":"Hitachi, Ltd., Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kouji","family":"Kimura","sequence":"additional","affiliation":[{"name":"Hitachi, Ltd., Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Norihiro","family":"Hara","sequence":"additional","affiliation":[{"name":"Hitachi, Ltd., Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenji","family":"Suzuki","sequence":"additional","affiliation":[{"name":"Ministry of Health, Labour and Welfare, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251122","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:41:42Z","timestamp":1754566902000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251122"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251122","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}