Geo-Social Top-k and Skyline Keyword Queries on Road Networks.

Muhammad Attique,Hyung-Ju Cho,Farman Ali,Muhammad Fazal Ijaz,Muhammad Afzal,Irfan Mehmood

doi:10.3390/s20030798

Abstract

The rapid growth of GPS-enabled mobile devices has popularized many location-based applications. Spatial keyword search which finds objects of interest by considering both spatial locations and textual descriptions has become very useful in these applications. The recent integration of social data with spatial keyword search opens a new service horizon for users. Few previous studies have proposed methods to combine spatial keyword queries with social data in Euclidean space. However, most real-world applications constrain the distance between query location and data objects by a road network, where distance between two points is defined by the shortest connecting path. This paper proposes geo-social top-k keyword queries and geo-social skyline keyword queries on road networks. Both queries enrich traditional spatial keyword query semantics by incorporating social relevance component. We formalize the proposed query types and appropriate indexing frameworks and algorithms to efficiently process them. The effectiveness and efficiency of the proposed approaches are evaluated using real datasets.

Highlights

Smartphones and social networks are significant innovations from the past decade, and the combination of these technologies has engendered geo-social network (GSN) applications, such as Facebook, Instagram, and Foursquare
To the best of our knowledge, this is the first study to introduce geo-social top-k keyword (GSTK) and geo-social skyline keyword (GSSK) queries in road networks
This paper introduced geo-social top-k keyword (GSTK) queries for road networks for the first time, integrating social relevance into traditional spatial keyword search and returning the k best data objects based on spatial, textual, and social relevance to the query

Summary

Introduction

Smartphones and social networks are significant innovations from the past decade, and the combination of these technologies has engendered geo-social network (GSN) applications, such as Facebook, Instagram, and Foursquare. Geo-tagged data (e.g., photos, videos, check-ins, and likes) allow these applications to provide many useful services to users based on social and location relevance. The easy availability of textual descriptions for desired facilities (e.g., restaurants, departmental stores, and travel destinations) has promoted many decision support systems and recommendation services. Traditional top-k spatial keyword queries [1,2,3] rank facilities based on spatial proximity to the query location and textual relevance to query keywords. Many existing studies have proposed spatial keyword query systems in Euclidean space [3,4,5] and road networks [6,7]. This paper investigates geo-social keyword queries that exploit spatial and Sensors 2020, 20, 798; doi:10.3390/s20030798 www.mdpi.com/journal/sensors

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