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DC Field | Value | Language |
---|---|---|
dc.citation.conferencePlace | US | - |
dc.citation.conferencePlace | Seattle, WA | - |
dc.citation.endPage | 181 | - |
dc.citation.startPage | 171 | - |
dc.citation.title | 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014 | - |
dc.contributor.author | Koehler, Christian | - |
dc.contributor.author | Banovic, Nikola | - |
dc.contributor.author | Oakley, Ian | - |
dc.contributor.author | Mankoff, Jennifer | - |
dc.contributor.author | Dey, Anind K. | - |
dc.date.accessioned | 2023-12-19T23:37:14Z | - |
dc.date.available | 2023-12-19T23:37:14Z | - |
dc.date.created | 2015-12-28 | - |
dc.date.issued | 2014-09-13 | - |
dc.description.abstract | Location prediction enables us to use a person’s mobility history to realize various applications such as efficient temperature control, opportunistic meeting support, and automated receptionists. Indoor location prediction is a challenging problem, particularly due to a high density of possible locations and short transition distances between these locations. In this paper we present Indoor-ALPS, an Adaptive Indoor Location Prediction System that uses temporal-spatial features to create individual daily models for the prediction of when a user will leave their current location (transition time) and the next location she will transition to. We tested Indoor-ALPS on the Augsburg Indoor Location Tracking Benchmark and compared our approach to the best performing temporal-spatial mobility prediction algorithm, Prediction by Partial Match (PPM). Our results show that Indoor-ALPS improves the temporal-spatial prediction accuracy over PPM for look-aheads up to 90 minutes by 6.2%, and for up to 30 minute look-aheads by 10.7%. These results demonstrate that Indoor-ALPS can be used to support a wide variety of indoor mobility prediction-based applications. | - |
dc.identifier.bibliographicCitation | 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014, pp.171 - 181 | - |
dc.identifier.doi | 10.1145/2632048.2632069 | - |
dc.identifier.scopusid | 2-s2.0-84908590235 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/34389 | - |
dc.identifier.url | http://dl.acm.org/citation.cfm?id=2632069 | - |
dc.language | 영어 | - |
dc.publisher | 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014 | - |
dc.title | Indoor-ALPS: an adaptive indoor location prediction system | - |
dc.type | Conference Paper | - |
dc.date.conferenceDate | 2015-09-13 | - |
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