讲座题目: Efficient Distance-Aware Query Evaluation on Indoor Moving Objects

主讲人: 谢希科  副教授

主持人: 林学民 教授

开始时间: 2014-12-19  10:00

讲座地址: 中北校区数学馆201室

主办单位: 软件学院

报告人简介：

Dr. Xike Xie is currently an Assistant Professor in the Department of Computer Science, Aalborg University, Denmark. He received the PhD degree in computer science from the University of Hong Kong in 2012. His research focuses on the management of complex data, in particular query processing topics on uncertain data, spatiotemporal data, and multidimensional data..

报告内容摘要：

Indoor spaces accommodate large parts of people’s life. The increasing availability of indoor positioning, driven by technologies like Wi-Fi, RFID, and Bluetooth, enables a variety of indoor location-based services (LBSs). Efficient indoor distance-aware queries on indoor moving objects play an important role in supporting and boosting such LBSs. However, the distance-aware query evaluation on indoor moving objects is challenging because: (1) indoor spaces are characterized by many special entities and thus render distance calculation very complex; (2) the limitations of indoor positioning technologies create inherent uncertainties in indoor moving objects data. 

 In this paper, we propose a complete set of techniques for efficient distance-aware queries on indoor moving objects. We define and categorize the indoor distances in relation to indoor uncertain objects, and derive different distance bounds that can facilitate query evaluation. Existing works often assume indoor floor plans are static, and require extensive pre-computation on indoor topologies. In contrast, we design a composite index scheme that integrates indoor geometries, indoor topologies, as well as indoor uncertain objects, and thus supports indoor distance-aware queries efficiently without time-consuming and volatile distance computation. We design algorithms for range query and k nearest neighbor query on indoor moving objects. The results of extensive experimental studies demonstrate that our proposals are efficient and scalable in evaluating distanceaware queries over indoor moving objects..