Skyline Path Queries for Location-based Services

Nishu Chowdhury,Mohammad Shamsul

doi:10.14569/ijacsa.2019.0100555

Abstract

A skyline query finds objects that are not dominated by another object from a given set of objects. Skyline queries help us to filter unnecessary information efficiently and provide us clues for various decision making tasks. In this paper, we consider skyline queries for location-based services and proposed a framework that can efficiently compute all non-dominated paths in road networks. A path p is said to dominate another path q if p is not worse than q in any of the k dimensions and p is better than q in at least one of the k dimensions. Our proposed skyline framework considers several features related to road networks and return all non-dominated paths from the road networks. In our work, we compute skylines considering two different perspectives: business perspective and individual user’s perspective. We have conducted several experiments to show the effectiveness of our method. From the experimental results, we can say that our system can perform efficient computation of skyline paths from road networks.

Highlights

Given a k-dimensional database DB, a skyline query retrieves a set of skyline objects, each of which is not dominated by another object
Thereafter, the process migrates into the query execution module, where a resulted dataset is generated imposing skyline queries
Our experimental results demonstrate that the proposed algorithm is scalable enough to compute the skyline path for a specific time

Summary

Introduction

Given a k-dimensional database DB, a skyline query retrieves a set of skyline objects, each of which is not dominated by another object. An object p is said to dominate another object pif p is not worse than pin any of the k dimensions and p is better than pin at least one of the k dimensions. The skyline of the list is {R1, R3, and R4}. A number of efficient algorithms for computing skylines from the database have been demonstrated in the literature [1, 2, 3, 4, 5, 6]

Objectives

Methods

Conclusion