TY - ABST
T1 - Finding Most Popular Indoor Semantic Locations Using Uncertain Mobility Data [Extended Abstract]
AU - Li, Huan
AU - Lu, Hua
AU - Shou, Lidan
AU - Chen, Gang
AU - Chen, Ke
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Indoor flows
KW - Indoor mobility data
KW - Indoor space
UR - http://www.scopus.com/inward/record.url?scp=85067936658&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2019.00264
DO - 10.1109/ICDE.2019.00264
M3 - Conference abstract in proceeding
SN - 978-1-5386-7475-8
T3 - Proceedings of the International Conference on Data Engineering
SP - 2139
EP - 2140
BT - The 35th IEEE International Conference on Data Engineering (ICDE)
PB - IEEE
T2 - 35th IEEE International Conference on Data Engineering, ICDE 2019
Y2 - 8 April 2019 through 11 April 2019
ER -