Finding Multiple New Optimal Locations in a Road Network

We study the problem of optimal location querying for location based services in road networks, which aims to find locations for new servers or facilities. The existing optimal solutions on this problem consider only the cases with one new server. When two or more new servers are to be set up, the problem with minmax cost criteria, MinMax, becomes NP-hard. In this work we identify some useful properties about the potential locations for the new servers, from which we derive a novel algorithm for MinMax, and show that it is efficient when the number of new servers is small. When the number of new servers is large, we propose an efficient 3-approximate algorithm. We verify with experiments on real road networks that our solutions are effective and attains significantly better result quality compared to the existing greedy algorithms

A new approach for maximizing bichromatic reverse nearest neighbor search

Maximizing bichromatic reverse nearest neighbor (MaxBRNN) is a variant of bichromatic reverse nearest neighbor (BRNN). The purpose of the MaxBRNN problem is to find an optimal region that maximizes the size of BRNNs. This problem has lots of real applications such as location planning and profile-based marketing. The best-known algorithm for the MaxBRNN problem is called MaxOverlap. In this paper, we study the MaxBRNN problem and propose a new approach called MaxSegment for a two-dimensional space when the -norm is used. Then, we extend our algorithm to other variations of the MaxBRNN problem such as the MaxBRNN problem with other metric spaces, and a three-dimensional space. Finally, we conducted experiments on real and synthetic datasets to compare our proposed algorithm with existing algorithms. The experimental results verify the efficiency of our proposed approach

Efficient query processing on spatial and textual data: beyond individual queries

With the increasing popularity of GPS enabled mobile devices, queries with locational intent are quickly becoming the most common type of search task on the web. This development has driven several research work on efficient processing of spatial and spatial-textual queries in the past few decades. While most of the existing work focus on answering queries independently, e.g., one query at a time, many real-life applications require the processing of multiple queries in a short period of time, and can benefit from sharing computations. This thesis focuses on efficient processing of the queries on spatial and spatial-textual data for the applications where multiple queries are of interest. Specifically, the following queries are studied: (i) batch processing of top-k spatial-textual queries; (ii) optimal location and keyword selection queries; and (iii) top-m rank aggregation on streaming spatial queries. The batch processing of queries is motivated from different application scenarios that require computing the result of multiple queries efficiently, including (i) multiple-query optimization, where the overall efficiency and throughput can be improved by grouping or partitioning a large set of queries; and (ii) continuous processing of a query stream, where in each time slot, the queries that have arrived can be processed together. In this thesis, given a set of top-k spatial-textual queries, the problem of computing the results for all the queries concurrently and efficiently as a batch is addressed. Some applications require an aggregation over the results of multiple queries. An exam- ple application is to identify the optimal value of attributes (e.g., location, text) for a new facility/service, so that the facility will appear in the query result of the maximum number of potential customers. This problem is essentially an aggregation (maximization) over the results of queries issued by multiple potential customers, where each user can be treated as a top-k query. In this thesis, we address this problem for spatial and textual data where the computations for multiple users are shared to find the final result. Rank aggregation is the problem of combining multiple rank orderings to produce a single ordering of the objects. Thus, aggregating the ranks of spatial objects can provide key insights into the importance of the objects in many different scenarios. This translates into a natural extension of the problem that finds the top-m objects with the highest aggregate rank over multiple queries. As the users issue new queries, clearly the rank aggregations continuously change over time, and recency also play an important role when interpreting the final results. The top-m rank aggregation of spatial objects for streaming queries is studied in this thesis, where the problem is to report the updated top-m objects with the highest aggregate rank over a subset of the most recent queries from a stream