• Intelligent augmented keyword search on spatial entities in real-life internet of vehicles

      Li, Yanhong; Wang, Meng; Du, Xiaokun; Feng, Yuhe; Luo, Changyin; Tian, Shasha; Anjum, Ashiq; Zhu, Rongbo; University of Derby (Elsevier, 2018-12-28)
      Internet of Vehicles (IoV) has attracted wide attention from both academia and industry. Due to the popularity of the geographical devices deployed on the vehicles, a tremendous amount of spatial entities which include spatial information, unstructured information and structured information, are generated every second. This development calls for intelligent augmented spatial keyword queries (ASKQ), which intelligently takes into account the locations, unstructured information (in the form of keyword sets), structured information (in the form of boolean expressions) of 182MinzuAvespatial entities. In this paper, we take the first step to address the issue of processing ASKQ in real traffic networks of IoV environments (ASKQIV) and focus on Top-k ASKQIV queries. To support network distance pruning, keyword pruning, and boolean expression pruning intelligently and simultaneously, a novel hybrid index structure called ASKTI is proposed. Note in the real-life traffic networks of IoV environments, travel cost is not only decided by the network distance, but also decided by some additional travel factors. By considering these additional factors, a combined factor Cftc of each road (edge) in the traffic network of IoV environments is calculated, and weighted network distance is calculated and adopted. Based on ASKTI, an efficient algorithm for Top-k ASKQIV query processing is proposed. Our method can also be extended to handle boolean range ASKQIV Queries and ranking ASKQIV Queries. Finally, simulation experiments on one real traffic network of IoV environments and two synthetic spatial entity sets are conducted. The results show that our ASKTI based method is superior to its competitors.