A Density-Based Approach to the Retrieval of Top-K Spatial Textual Clusters

TL;DR

This paper introduces a novel density-based top-k spatial textual clustering query that efficiently retrieves relevant, dense, and nearby clusters of web objects based on user keywords and location.

Contribution

It proposes a new top-k spatial textual clusters (k-STC) query type and develops algorithms with techniques like object skipping and gridded indexing for fast retrieval.

Findings

Algorithms demonstrate high scalability on real data.

The advanced approach significantly improves response times.

Methods effectively identify dense, relevant clusters near the query location.

Abstract

Keyword-based web queries with local intent retrieve web content that is relevant to supplied keywords and that represent points of interest that are near the query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, namely the top-k spatial textual clusters (k-STC) query that returns the top-k clusters that (i) are located the closest to a given query location, (ii) contain the most relevant objects with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a basic algorithm that relies on on-line density-based clustering and exploits an early stop condition. To improve the response time, we design an advanced approach that includes three techniques: (i) an object skipping rule, (ii) spatially gridded posting lists, and (iii) a fast range query algorithm. An empirical study on real data demonstrates that the paper's proposals offer scalability and are capable of excellent performance.