Toward Mining Stop-by Behaviors in Indoor Space

Abstract

In this article, we explore a new mining paradigm, called Indoor Stop-by Patterns ( ISP ), to discover user stop-by 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 spatio-temporal 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.