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

Reverse Nearest Neighbor Heat Maps: A Tool for Influence Exploration

We study the problem of constructing a reverse nearest neighbor (RNN) heat map by finding the RNN set of every point in a two-dimensional space. Based on the RNN set of a point, we obtain a quantitative influence (i.e., heat) for the point. The heat map provides a global view on the influence distribution in the space, and hence supports exploratory analyses in many applications such as marketing and resource management. To construct such a heat map, we first reduce it to a problem called Region Coloring (RC), which divides the space into disjoint regions within which all the points have the same RNN set. We then propose a novel algorithm named CREST that efficiently solves the RC problem by labeling each region with the heat value of its containing points. In CREST, we propose innovative techniques to avoid processing expensive RNN queries and greatly reduce the number of region labeling operations. We perform detailed analyses on the complexity of CREST and lower bounds of the RC problem, and prove that CREST is asymptotically optimal in the worst case. Extensive experiments with both real and synthetic data sets demonstrate that CREST outperforms alternative algorithms by several orders of magnitude.Comment: Accepted to appear in ICDE 201

Predicting optimal facility location without customer locations

Deriving meaningful insights from location data helps businesses make better decisions. One critical decision made by a business is choosing a location for its new facility. Optimal location queries ask for a location to build a new facility that optimizes an objective function. Most of the existing works on optimal location queries propose solutions to return best location when the set of existing facilities and the set of customers are given. However, most businesses do not know the locations of their customers. In this paper, we introduce a new problem setting for optimal location queries by removing the assumption that the customer locations are known. We propose an optimal location predictor which accepts partial information about customer locations and returns a location for the new facility. The predictor generates synthetic customer locations by using given partial information and it runs optimal location queries with generated location data. Experiments with real data show that the predictor can find the optimal location when sufficient information is provided. © 2017 Copyright held by the owner/author(s)

Efficient algorithms for optimal location queries in road network

This report is base on a research paper Efficient Algorithms for Optimal Location Queries in Road Networks [1]. Given a Road Network, Data of Clients and Servers, the algorithm suggest areas on the edge where is optimize to set up a new Server. Many companies today hopes to expand themselves to be able to reached by as many customers as possible. Expanding allow the company to built a strong brand and allowing more customer to reach where the business stretch. Growth is important for a business sustainability. There are many considerations in deciding the location to set up a new shop and using optimal location queries in road networks will help in making good decision. This report gives three algorithms that will solve three different type of problems. An example to the first problem will be when a retail shop owner wants to open a new shop to attract most clients. The solution will be finding an area where most customers will be willing to travel to visit. The second problem will be if a delivery company wants to minimize petrol cost. The solution will be having the minimum average distance to all clients. The last problem will be if a government wants to open a new facility and aiming to decrease the traveling distance from the furthest client. The solution is to find an area where will be decrease the distance to furthest client.. The results is successful and all algorithm have achieve good performance. The correctness of the program is verified with modular testing of each part. The results had satisfy the requirement.Bachelor of Engineering (Computer Engineering

Optimal location queries in road network databases

The Optimal Location Query problem is the exploration for an ideal location that satisfies a specified cost metric in a spatial database. The usage of Optimal Location Query extends to many real life practical scenarios such as scouting a site to open a hypermarket in an area where it would be able attract the most number of customers as possible, yet keeping competition with other retailers at bay. Optimal Location Queries can also be used to facilitate in determining an area where its minimum distance to its benefiters are maximised. The aim of this project would encompass the implementation of Optimal Location Query algorithms presented in the scholarly paper entitled “Optimal Location Queries in Road Networks”. This involves implementing both basic and fine grain partitioning approaches, experimenting and analysing the efficacy on large datasets and producing the result on a graphical user interface. Performance and memory consumption impact was analysed on FGP parameter Θ, number of User-Specified Edges |Ec| / |E|, number of clients |C|, and on Number of Facility |F|. It is noted that FGP performance in computational speed and memory consumption faired better than basic traversal in most test. Also when Θ set at 0.01 (1%) would maximise FGP performance, however, setting Θ at 0.1 can also be considered as it gives a balanced reduction in both computational time and memory.Bachelor of Engineering (Computer Science