{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T05:12:13Z","timestamp":1790831533515,"version":"4.1.0"},"reference-count":13,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2018,8]]},"abstract":"<jats:p>\n            The rapid accumulation of indoor positioning data is increasingly booming the interest in indoor mobility analyses. As a fundamental analysis, it is highly relevant to translate raw indoor positioning data into mobility semantics that describe what, where and when in a more concise and semantics-oriented way. Such a translation is challenging as multiple data sources are involved, raw indoor positioning data is of low quality, and translation results are hard to assess. We demonstrate a system\n            <jats:italic>TRIPS<\/jats:italic>\n            that streamlines the entire translation process by three functional components. The Configurator provides a standard but concise means to configure multiple input sources, including the indoor positioning data, indoor space information, and relevant contexts. The Translator cleans the indoor positioning data and exports reliable mobility semantics without manual interventions. The Viewer offers a suite of flexible operations to trace the input, output and intermediate data involved in the translation. Data analysts can interact with TRIPS to obtain the desired mobility semantics in a visual and convenient way.\n          <\/jats:p>","DOI":"10.14778\/3229863.3236224","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T12:12:28Z","timestamp":1536581548000},"page":"1918-1921","source":"Crossref","is-referenced-by-count":13,"title":["TRIPS"],"prefix":"10.14778","volume":"11","author":[{"given":"Huan","family":"Li","sequence":"first","affiliation":[{"name":"Zhejiang University, China and Key Laboratory of Big Data Intelligent Computing of Zhejiang Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Lu","sequence":"additional","affiliation":[{"name":"Aalborg University, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feichao","family":"Shi","sequence":"additional","affiliation":[{"name":"Zhejiang University, China and Key Laboratory of Big Data Intelligent Computing of Zhejiang Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, China and Key Laboratory of Big Data Intelligent Computing of Zhejiang Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, China and Key Laboratory of Big Data Intelligent Computing of Zhejiang Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lidan","family":"Shou","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,8]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Project TRIPS. https:\/\/longaspire.github.io\/trips.  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Journal of Exposure Science, 11(3): 231, 2001.","journal-title":"Journal of Exposure Science"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2632048.2632069"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.14778\/3007263.3007282"},{"key":"e_1_2_1_8_1","first-page":"449","volume-title":"EDBT","author":"Lu H.","year":"2016","unstructured":"H. Lu , C. Guo , B. Yang , and C. S. Jensen . Finding frequently visited indoor pois using symbolic indoor tracking data . In EDBT , pp. 449 -- 460 , 2016 . H. Lu, C. Guo, B. Yang, and C. S. Jensen. Finding frequently visited indoor pois using symbolic indoor tracking data. In EDBT, pp. 449--460, 2016."},{"key":"e_1_2_1_9_1","volume-title":"Approaching the limit of predictability in human mobility. Scientific reports, 3(10)","author":"Lu X.","year":"2013","unstructured":"X. Lu , E. Wetter , N. Bharti , A. J. Tatem , and L. Bengtsson . Approaching the limit of predictability in human mobility. Scientific reports, 3(10) , 2013 . X. Lu, E. Wetter, N. Bharti, A. J. Tatem, and L. Bengtsson. Approaching the limit of predictability in human mobility. Scientific reports, 3(10), 2013."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1626536.1626539"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.14778\/2733004.2733065"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2483669.2483682"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/1739041.1739083"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3229863.3236224","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:13:08Z","timestamp":1672222388000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3229863.3236224"}},"subtitle":["a system for translating raw indoor positioning data into visual mobility semantics"],"short-title":[],"issued":{"date-parts":[[2018,8]]},"references-count":13,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2018,8]]}},"alternative-id":["10.14778\/3229863.3236224"],"URL":"https:\/\/doi.org\/10.14778\/3229863.3236224","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2018,8]]}}}