Toward mining stop-by behaviors in indoor space

Shan Yun Teng, Wei Shinn Ku, Kun Ta Chuang

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


In this article, we explore a new mining paradigm, called Indoor Stop-by Patterns (ISP), to discover user stopby behavior in mall-like indoor environments. The discovery of ISPs enables new marketing collaborations, such as a joint coupon promotion, among stores in indoor spaces (e.g., shopping malls). Moreover, it can also help in eliminating the overcrowding situation. To pursue better practicability, we consider the cost-effective wireless sensor-based environment and conduct the analysis of indoor stop-by behaviors on real data. However, it is a highly challenging issue, in indoor environments, to retrieve frequent ISPs, especially when the issue of user privacy is highlighted nowadays. The mining of ISPs will face a critical challenge from spatial uncertainty. Previous work on mining indoor movement patterns usually relies on precise spatiooral information by a specific deployment of positioning devices, which cannot be directly applied. In this article, the proposed Probabilistic Top-k Indoor Stop-by Patterns Discovery (PTkISP) framework incorporates the probabilistic model to identify top-k ISPs over uncertain data collected from sensing logs. Moreover, we develop an uncertain model and devise an Index 1-itemset (IIS) algorithm to enhance the accuracy and efficiency. Our experimental studies show that the proposed PTkISP framework can efficiently discover high-quality ISPs and can provide insightful observations for marketing collaborations.

Original languageEnglish
Article number7
JournalACM Transactions on Spatial Algorithms and Systems
Issue number2
Publication statusPublished - 2017 Aug

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Modelling and Simulation
  • Computer Science Applications
  • Geometry and Topology
  • Discrete Mathematics and Combinatorics


Dive into the research topics of 'Toward mining stop-by behaviors in indoor space'. Together they form a unique fingerprint.

Cite this