Toward Mining Stop-by Behaviors in Indoor Space
Toward Mining Stop-by Behaviors in Indoor Space
复制标题
DOI：
10.1145/3106736
复制
发表时间：
2017-08
期刊：
影响因子：
--
通讯作者：
Shan-Yun Teng;Wei-Shinn Ku;Kun-Ta Chuang
中科院分区：
文献类型：
--
作者：
Shan-Yun Teng;Wei-Shinn Ku;Kun-Ta Chuang
在这篇文章中，我们探索了一种新的挖掘范式，称为室内停车模式（ISP），以发现用户在类似商场的室内环境中的停车行为。ISP的发现使得能够在室内空间的商店之间进行新的营销合作，例如联合优惠券促销（例如，购物中心）。此外，它还有助于消除过度拥挤的情况。为了追求更好的实用性，我们考虑了成本效益的无线传感器为基础的环境，并进行室内停的行为分析的真实的数据。然而，这是一个极具挑战性的问题，在室内环境中，检索频繁的ISP，特别是当用户隐私的问题是突出的今天。互联网服务提供商的挖掘将面临来自空间不确定性的严峻挑战。以往的室内运动模式挖掘工作通常依赖于精确的时空信息，通过特定的定位设备的部署，这不能直接应用。在这篇文章中，提出的概率Top-k室内停车模式发现（PTkISP）框架采用概率模型来识别从传感日志收集的不确定数据上的Top-k ISP。此外，我们开发了一个不确定的模型，并设计了一个索引1-项集（IIS）算法，以提高精度和效率。我们的实验研究表明，建议的PTkISP框架可以有效地发现高质量的ISP，并可以提供有见地的观察营销合作。
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.