Continuous Search on Dynamic Spatial Keyword Objects

As the popularity of SNS and the number of GPS-equipped mobile devices increases, a large number of web users frequently change their location (spatial attribute) and interesting keywords (keyword attribute) in real-time. An example of such would be when a user watches the news, videos, and blogs while moving. Many location-based web applications can benefit from continuously searching for these dynamic spatial keyword objects. In this paper, we define a novel query problem to continuously search for dynamic spatial keyword objects. To the best of our knowledge, this is the first work to consider dynamic spatial keyword objects. We employ a novel grid-based index to manage both queries and dynamic spatial keyword objects. With the proposed index, we develop a buffer named partial cell list to reduce the computation cost in the top-k reevaluation. The experiments confirm the superiorities of our proposed methods.

I. Introduction

Smartphones and other mobile devices enable to receive information anywhere and enrich people’s lives. People are used to watching the news, short video clips (e.g. Youtube, TikTok), posting on SNS (e.g. Twitter, Weibo) with smartphones while moving outside. Although different types of continuous spatial keyword queries have been studied [1]–[4], the existing research considers only static objects. In this research, we define a novel searching problem that continuously searches for top-k dynamic spatial keyword objects.