{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T12:13:59Z","timestamp":1771330439250,"version":"3.50.1"},"reference-count":11,"publisher":"Association for Computing Machinery (ACM)","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2016,9]]},"abstract":"<jats:p>\n            We demonstrate a generic, user-configurable toolkit for generating different types of indoor mobility data for real-world buildings. Our prototype generates the desired data in a three-layer pipeline. The\n            <jats:italic>Infrastructure Layer<\/jats:italic>\n            accepts industry-standard digital building information (DBI) files to generate the host indoor environment, allowing users to configure the generation of a variety of positioning devices, such as Wi-Fi, Bluetooth, RFID, etc. The\n            <jats:italic>Moving Object Layer offers<\/jats:italic>\n            the functionality of defining objects or trajectories, with configurable indoor moving patterns, distribution models, and sampling frequencies. The\n            <jats:italic>Positioning Layer<\/jats:italic>\n            generates synthetic signal strength measurements known as raw RSSI\n            <jats:sup>1<\/jats:sup>\n            measurements according to the positioning device data and trajectory data generated at relevant layers. It also generates different types of indoor positioning data through the customization of all typical indoor positioning methods on the raw RSSI data.\n          <\/jats:p>","DOI":"10.14778\/3007263.3007282","type":"journal-article","created":{"date-parts":[[2016,11,1]],"date-time":"2016-11-01T13:47:47Z","timestamp":1478008067000},"page":"1453-1456","source":"Crossref","is-referenced-by-count":33,"title":["Vita"],"prefix":"10.14778","volume":"9","author":[{"given":"Huan","family":"Li","sequence":"first","affiliation":[{"name":"Zhejiang University, China"}]},{"given":"Hua","family":"Lu","sequence":"additional","affiliation":[{"name":"Aalborg University, Denmark"}]},{"given":"Xin","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}]},{"given":"Ke","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}]},{"given":"Lidan","family":"Shou","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}]}],"member":"320","published-online":{"date-parts":[[2016,9]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Vita Project. http:\/\/db.zju.edu.cn\/vita\/.  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IEEE Data Eng. Bull. , 33 ( 2 ): 12 -- 17 , 2010 . C. S. Jensen, H. Lu, and B. Yang. Indoor-a new data management frontier. IEEE Data Eng. Bull., 33(2):12--17, 2010.","journal-title":"IEEE Data Eng. Bull."},{"key":"e_1_2_1_8_1","first-page":"461","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 , pages 461 -- 472 , 2016 . H. Lu, C. Guo, B. Yang, and C. S. Jensen. Finding frequently visited indoor pois using symbolic indoor tracking data. In EDBT, pages 461--472, 2016."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/MDM.2012.39"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1739041.1739083"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCIE.2010.5668290"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3007263.3007282","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T09:38:39Z","timestamp":1672220319000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3007263.3007282"}},"subtitle":["a versatile toolkit for generating indoor mobility data for real-world buildings"],"short-title":[],"issued":{"date-parts":[[2016,9]]},"references-count":11,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2016,9]]}},"alternative-id":["10.14778\/3007263.3007282"],"URL":"https:\/\/doi.org\/10.14778\/3007263.3007282","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2016,9]]}}}