Finding Most Popular Indoor Semantic Locations Using Uncertain Mobility Data [Extended Abstract]

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

Research output: Contribution to book/anthology/report/conference proceedingConference abstract in proceedingResearchpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationThe 35th IEEE International Conference on Data Engineering (ICDE)
PublisherIEEE
Publication date2019
Pages2139-2140
Publication statusPublished - 2019
Event35th IEEE International Conference on Data Engineering, ICDE 2019 - Macau, China
Duration: 8 Apr 201911 Apr 2019

Conference

Conference35th IEEE International Conference on Data Engineering, ICDE 2019
CountryChina
CityMacau
Period08/04/201911/04/2019

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Semantics
Data reduction
Data structures
Marketing
Topology
Planning

Cite this

Li, H., Lu, H., Shou, L., Chen, G., & Chen, K. (2019). Finding Most Popular Indoor Semantic Locations Using Uncertain Mobility Data [Extended Abstract]. In The 35th IEEE International Conference on Data Engineering (ICDE) (pp. 2139-2140). IEEE.
Li, Huan ; Lu, Hua ; Shou, Lidan ; Chen, Gang ; Chen, Ke. / Finding Most Popular Indoor Semantic Locations Using Uncertain Mobility Data [Extended Abstract]. The 35th IEEE International Conference on Data Engineering (ICDE). IEEE, 2019. pp. 2139-2140
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Li, H, Lu, H, Shou, L, Chen, G & Chen, K 2019, Finding Most Popular Indoor Semantic Locations Using Uncertain Mobility Data [Extended Abstract]. in The 35th IEEE International Conference on Data Engineering (ICDE). IEEE, pp. 2139-2140, 35th IEEE International Conference on Data Engineering, ICDE 2019, Macau, China, 08/04/2019.

Finding Most Popular Indoor Semantic Locations Using Uncertain Mobility Data [Extended Abstract]. / Li, Huan; Lu, Hua; Shou, Lidan; Chen, Gang; Chen, Ke.

The 35th IEEE International Conference on Data Engineering (ICDE). IEEE, 2019. p. 2139-2140.

Research output: Contribution to book/anthology/report/conference proceedingConference abstract in proceedingResearchpeer-review

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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.

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Li H, Lu H, Shou L, Chen G, Chen K. Finding Most Popular Indoor Semantic Locations Using Uncertain Mobility Data [Extended Abstract]. In The 35th IEEE International Conference on Data Engineering (ICDE). IEEE. 2019. p. 2139-2140