Finding Most Popular Indoor Semantic Locations Using Uncertain Mobility Data

Huan Li, Hua Lu, Lidan Shou, Gang Chen, Ke Chen

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Knowing popular indoor locations can benefit many applications like exhibition planning and location-based advertising, among others. In this work, we use uncertain historical indoor mobility data to find the top-k popular indoor semantic locations with the highest flow values. In the data we use, an object positioning report contains a set of samples, each consisting of an indoor location and a corresponding probability. The problem is challenging due to the difficulty in obtaining reliable flow values and the heavy computational workload on probabilistic samples for large numbers of objects. To address the first challenge, we propose an indoor flow definition that takes into account both data uncertainty and indoor topology. To efficiently compute flows for individual indoor semantic locations, we design data structures for facilitating accessing the relevant data, a data reduction method that reduces the intermediate data to process, and an overall flow computing algorithm. Furthermore, we design search algorithms for finding the top-k popular indoor semantic locations. All proposals are evaluated extensively on real and synthetic data. The evaluation results show that our data reduction method significantly reduces the data volume in computing, our search algorithms are efficient and scalable, and the top-k popular semantic locations returned are in good accord with ground truth.
Titel35th IEEE International Conference on Data Engineering, ICDE 2019
Antal sider2
ISBN (Trykt)978-1-5386-7475-8
ISBN (Elektronisk)978-1-5386-7474-1
StatusUdgivet - 2019
Udgivet eksterntJa
Begivenhed35th International Conference on Data Engineering (ICDE) - Macau, Kina
Varighed: 8 apr. 201911 apr. 2019
Konferencens nummer: 35


Konference35th International Conference on Data Engineering (ICDE)
NavnProceedings of the International Conference on Data Engineering

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