Abstract
Indoor mobility semantics analytics can greatly benefit many pertinent applications. Existing semantic annotation methods mainly focus on outdoor space and require extra knowledge such as POI category or human activity regularity. However, these conditions are difficult to meet in indoor venues with relatively small extents but complex topology. This work studies the annotation of indoor mobility semantics that describe an object's mobility event (what ) at a semantic indoor region (where ) during a time period (when ). A coupled conditional Markov network (C2MN) is proposed with a set of feature functions carefully designed by incorporating indoor topology and mobility behaviors. C2MN is able to capture probabilistic dependencies among positioning records, semantic regions, and mobility events jointly. Nevertheless, the correlation of regions and events hinders the parameters learning. Therefore, we devise an alternate learning algorithm to enable the parameter learning over correlated variables. The extensive experiments demonstrate that our C2MN-based semantic annotation is efficient and effective on both real and synthetic indoor mobility data.
Original language | English |
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Title of host publication | Proceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020 |
Editors | Murat Kantarcioglu, Dimitrios Gunopulos, S. Sudarshan |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 1441-1452 |
Number of pages | 12 |
ISBN (Electronic) | 9781728129037 |
ISBN (Print) | 9781728129044 |
DOIs | |
Publication status | Published - 2020 |
Event | IEEE International Conference on Data Engineering 2020 - Online - Virtual, Dallas, United States of America Duration: 20 Apr 2020 → 24 Apr 2020 Conference number: 36th https://ieeexplore.ieee.org/xpl/conhome/9093725/proceeding (Proceedings) https://www.utdallas.edu/icde/ (Website) |
Publication series
Name | Proceedings - International Conference on Data Engineering |
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Publisher | The Institute of Electrical and Electronics Engineers, Inc. |
Volume | 2020-April |
ISSN (Print) | 1084-4627 |
ISSN (Electronic) | 2375-026X |
Conference
Conference | IEEE International Conference on Data Engineering 2020 |
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Abbreviated title | ICDE 2020 |
Country | United States of America |
City | Dallas |
Period | 20/04/20 → 24/04/20 |
Internet address |
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