{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T18:14:57Z","timestamp":1648577697744},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2020,12,16]],"date-time":"2020-12-16T00:00:00Z","timestamp":1608076800000},"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":[[2020,12,16]]},"abstract":"<jats:p>This paper presents a new knowledge base creation method for personal\/collective health data with knowledge of preemptive care and potential risk inspection with a global and geographical mapping and visualization functions of 5D World Map System. The final goal of this research project is a realization of a system to analyze the personal health\/bio data and potential-risk inspection data and provide a set of appropriate coping strategies and alert with semantic computing technologies. The main feature of 5D World Map System is to provide a platform of collaborative work for users to perform a global analysis for sensing data in a physical space along with the related multimedia data in a cyber space, on a single view of time-series maps based on the spatiotemporal and semantic correlation calculations. In this application, the concrete target data for world-wide evaluation is (1) multi-parameter personal health\/bio data such as blood pressure, blood glucose, BMI, uric acid level etc. and daily habit data such as food, smoking, drinking etc., for a health monitoring and (2) time-series multi-parameter collective health\/bio data in the national\/regional level for global analysis of potential cause of disease. This application realizes a new multidimensional data analysis and knowledge sharing for both a personal and global level health monitoring and disease analysis. The results are able to be analyzed by the time-series difference of the value of each spot, the differences between the values of multiple places in a focused area, and the time-series differences between the values of multiple locations to detect and predict a potential-risk of diseases.<\/jats:p>","DOI":"10.3233\/faia200825","type":"book-chapter","created":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T13:25:11Z","timestamp":1609853111000},"source":"Crossref","is-referenced-by-count":0,"title":["Global &amp; Geographical Mapping and Visualization Method for Personal\/Collective Health Data with 5D World Map System"],"prefix":"10.3233","author":[{"given":"Shiori","family":"Sasaki","sequence":"first","affiliation":[{"name":"Graduate School of Media and Governance, Keio University, Japan"}]},{"given":"Koji","family":"Murakami","sequence":"additional","affiliation":[{"name":"Prevent Science Co., Ltd."}]},{"given":"Yasushi","family":"Kiyoki","sequence":"additional","affiliation":[{"name":"Graduate School of Media and Governance, Keio University, Japan"}]},{"given":"Asako","family":"Uraki","sequence":"additional","affiliation":[{"name":"Graduate School of Media and Governance, Keio University, Japan"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Information Modelling and Knowledge Bases XXXII"],"original-title":[],"link":[{"URL":"http:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA200825","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T13:25:16Z","timestamp":1609853116000},"score":1,"resource":{"primary":{"URL":"http:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA200825"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,16]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia200825","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,16]]}}}