Efficient continuously moving top-k spatial keyword query processing

Abstract

Web users and content are increasingly being geopositioned. This development gives prominence to spatial keyword queries, which involve both the locations and textual descriptions of content. We study the efficient processing of continuously moving topk spatial keyword (MkSK) queries over spatial keyword data. State-of-the-art solutions for moving queries employ safe zones that guarantee the validity of reported results as long as the user remains within a zone. However, existing safe zone methods focus solely on spatial locations and ignore text relevancy. We propose two algorithms for computing safe zones that guarantee correct results at any time and that aim to optimize the computation on the server as well as the communication between the server and the client. We exploit tight and conservative approximations of safe zones and aggressive computational space pruning. Empirical studies with real data suggest that our proposals are efficient.

Inherent-Cost Aware Collective Spatial Keyword Queries

“…This type of queries has been studied on Euclidean space [9,19,16], road network databases [20], trajectory databases [21,10] and moving object databases [26]. Usually, the methods for this kind of queries adopt an index structure called the IR-tree [9,24] capturing both the spatial proximity and the textual information of the objects to speed up the keyword-based nearest neighbor (NN) queries and range queries.…”

“…A top-k kNN query [9,19,16,20,21,10,26] adopts the ranking function considering both the spatial proximity and the textual relevance of the objects and returns top-k objects based on the ranking function. This type of queries has been studied on Euclidean space [9,19,16], road network databases [20], trajectory databases [21,10] and moving object databases [26].…”

On Generalizing Collective Spatial Keyword Queries

“…This type of queries has been studied on Euclidean space [8], [18], [15], road network databases [19], trajectory databases [20], [9] and moving object databases [25]. Usually, the methods for this kind of queries adopt an index structure called the IR-tree [8], [23] capturing both the spatial proximity and the textual information of the objects to speed up the keyword-based nearest neighbor (NN) queries and range queries.…”

“…A top-k kNN query [8], [18], [15], [19], [20], [9], [25] adopts the ranking function considering both the spatial proximity and the textual relevance of the objects and returns top-k objects based on the ranking function. This type of queries has been studied on Euclidean space [8], [18], [15], road network databases [19], trajectory databases [20], [9] and moving object databases [25].…”

The Collective Spatial Keyword Query Processing Based on User Preferences

“…Therefore this paper, propose a novel method to enhance the result and there is no existing method which combines both the approaches. This development gives prominence to spatial keyword queries [5,6,8,10]. A typical such query takes a location and a set of keywords as arguments and returns the single spatial web object that best matches these arguments.…”