{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T14:08:03Z","timestamp":1788703683585,"version":"build-2803163510"},"reference-count":48,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T00:00:00Z","timestamp":1606262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"crossref","award":["61672455 and 61528207"],"award-info":[{"award-number":["61672455 and 61528207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Independent Research Fund Denmark","award":["8022-00366B"],"award-info":[{"award-number":["8022-00366B"]}]},{"name":"Natural Science Foundation of Zhejiang Province of China","award":["LY18F020005"],"award-info":[{"award-number":["LY18F020005"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM\/IMS Trans. Data Sci."],"published-print":{"date-parts":[[2020,11,30]]},"abstract":"<jats:p>\n                    Indoor mobility analyses are increasingly interesting due to the rapid growth of raw indoor positioning data obtained from IoT infrastructure. However, high-level analyses are still in urgent need of a concise but semantics-oriented representation of the mobility implied by the raw data. This work studies the problem of translating raw indoor positioning data into\n                    <jats:italic toggle=\"yes\">mobility semantics<\/jats:italic>\n                    that describe a moving object\u2019s mobility event (What) someplace (Where) at some time (When). The problem is non-trivial mainly because of the inherent errors in the uncertain, discrete raw data. We propose a three-layer framework to tackle the problem. In the\n                    <jats:italic toggle=\"yes\">cleaning<\/jats:italic>\n                    layer, we design a cleaning method that eliminates positioning data errors by considering indoor mobility constraints. In the\n                    <jats:italic toggle=\"yes\">annotation<\/jats:italic>\n                    layer, we propose a split-and-match approach to annotate mobility semantics on the cleaned data. The approach first employs a density-based splitting method to divide positioning sequences into split snippets according to underlying mobility events, followed by a semantic matching method that makes proper annotations for split snippets. In the\n                    <jats:italic toggle=\"yes\">complementing<\/jats:italic>\n                    layer, we devise an inference method that makes use of the indoor topology and the mobility semantics already obtained to recover the missing mobility semantics. The extensive experiments demonstrate that our solution is efficient and effective on both real and synthetic data. For typical queries, our solution\u2019s resultant mobility semantics lead to more precise answers but incur less execution time than alternatives.\n                  <\/jats:p>","DOI":"10.1145\/3385190","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T08:37:20Z","timestamp":1594111040000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Toward Translating Raw Indoor Positioning Data into Mobility Semantics"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0084-1662","authenticated-orcid":false,"given":"Huan","family":"Li","sequence":"first","affiliation":[{"name":"Aalborg University, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Lu","sequence":"additional","affiliation":[{"name":"Aalborg University, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinkuang","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, 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":[[2020,11,25]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Retrieved","author":"World T","year":"2018","unstructured":"IIoT World. 2018. 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