Reverse spatial visual top-k query

With the wide application of mobile Internet techniques an location-based services (LBS), massive multimedia data with geo-tags has been generated and collected. In this paper, we investigate a novel type of spatial query problem, named reverse spatial visual top- $k$ query (RSVQ k ) that aims to retrieve a set of geo-images that have the query as one of the most relevant geo-images in both geographical proximity and visual similarity. Existing approaches for reverse top- $k$ queries are not suitable to address this problem because they cannot effectively process unstructured data, such as image. To this end, firstly we propose the definition of RSVQ k problem and introduce the similarity measurement. A novel hybrid index, named VR 2 -Tree is designed, which is a combination of visual representation of geo-image and R-Tree. Besides, an extension of VR 2 -Tree, called CVR 2 -Tree is introduced and then we discuss the calculation of lower/upper bound, and then propose the optimization technique via CVR 2 -Tree for further pruning. In addition, a search algorithm named RSVQ k algorithm is developed to support the efficient RSVQ k query. Comprehensive experiments are conducted on four geo-image datasets, and the results illustrate that our approach can address the RSVQ k problem effectively and efficiently

Accurate and Efficient Query Processing at Location-Based Services by using Route APIs

Efficient query processing system provides best search results to user by gathering user point of interest. Mobile users required a Location based server (LBS) to search the spatial related data. Existing system provided route results but it takes more time to execute the query and does not gives the accurate results means traffic related travel timings. The proposed system is a fastest processer for location search users. Here, LBS obtain route travel times from online route API. So it gives the accurate results to user by preventing number route request and query execution time. We use range query algorithm to reduce the number of route request and Parallel Scheduling Techniques to reduce the query execution time. Our experimental result shows that the proposed system is more efficient than existing processer