Finding top-k relevant groups of spatial web objects

Skovsgaard, Anders; Jensen, Christian S.

doi:10.1007/s00778-015-0388-z

Cited by 20 publications

(17 citation statements)

References 27 publications

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“…The top-k groups query [2,47] aims to support users who wish to explore different objects. For example, a user may want to explore different restaurants before deciding where to have dinner.…”

Section: Beyond Single Object Results Granularitymentioning

confidence: 99%

Querying Geo-Textual Data

Cong

Jensen

2016

Proceedings of the 2016 International Conference on Management of Data

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Over the past decade, we have moved from a predominantly desktop based web to a predominantly mobile web, where users most often access the web from mobile devices such as smartphones. In addition, we are witnessing a proliferation of geo-located, textual web content. Motivated in part by these developments, the research community has been hard at work enabling the efficient computation of a variety of query functionality on geo-textual data, yielding a sizable body of literature on the querying of geo-textual data.With a focus on different types of keyword-based queries on geo-textual data, the tutorial also explores topics such as continuous queries on streaming geo-textual data, queries that retrieve attractive regions of geo-textual objects, and queries that extract properties, e.g., topics and top-k frequent words, of the objects in regions. The tutorial is designed to offer an overview of the problems addressed in this body of literature and offers an overview of pertinent concepts and techniques. In addition, the tutorial suggests open problems and new research direction.

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Section: Beyond Single Object Results Granularitymentioning

confidence: 99%

Querying Geo-Textual Data

Cong

Jensen

2016

Proceedings of the 2016 International Conference on Management of Data

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“…Skovsgaard et al [21] proposed a query to find top-k groups of objects with the ranking function considering the spatial proximity and textual relevance of the groups. Liu et al proposed the cluebased spatio-textual query [16] which takes a set of keywords and a clue as inputs, and returns k objects with highest similarities against the clue.…”

Section: Related Workmentioning

confidence: 99%

On Generalizing Collective Spatial Keyword Queries

Chan

Long

Wong

2018

IEEE Trans. Knowl. Data Eng.

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Abstract-With the proliferation of spatial-textual data such as location-based services and geo-tagged websites, spatial keyword queries are ubiquitous in real life. One example of spatial-keyword query is the so-called collective spatial keyword query (CoSKQ) which is to find for a given query consisting a query location and several query keywords a set of objects which covers the query keywords collectively and has the smallest cost wrt the query location. In the literature, many different functions were proposed for defining the cost and correspondingly, many different approaches were developed for the CoSKQ problem. In this paper, we study the CoSKQ problem systematically by proposing a unified cost function and a unified approach for the CoSKQ problem (with the unified cost function). The unified cost function includes all existing cost functions as special cases and the unified approach solves the CoSKQ problem with the unified cost function in a unified way. Experiments were conducted on both real and synthetic datasets which verified our proposed approach.

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“…There are also some other studies on spatial keyword queries, including [22] which finds top-k groups of objects with the ranking function considering the spatial proximity and textual relevance of the groups, [17] which takes a set of keywords and a clue as inputs, and returns k objects with highest similarities against the clue, [14,23] which finds an object set in the road network, [4,12] which finds a region as a solution and [1,27] which finds a route as a solution.…”

Section: Related Workmentioning

confidence: 99%

Inherent-Cost Aware Collective Spatial Keyword Queries

Chan

Long

Wong

2017

Advances in Spatial and Temporal Databases

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Abstract. With the proliferation of spatial-textual data such as location-based services and geo-tagged websites, spatial keyword queries become popular in the literature. One example of these queries is the collective spatial keyword query (CoSKQ) which is to find a set of objects in the database such that it covers a given set of query keywords collectively and has the smallest cost. Some existing cost functions were proposed in the literature, which capture different aspects of the distances among the objects in the set and the query. However, we observe that in some applications, each object has an inherent cost (e.g., workers have monetary costs) which are not captured by any of the existing cost functions. Motivated by this, in this paper, we propose a new cost function called the maximum dot size cost which captures both the distances among objects in a set and a query as existing cost functions do and the inherent costs of the objects. We prove that the CoSKQ problem with the new cost function is NP-hard and develop two algorithms for the problem. One is an exact algorithm which is based on a novel search strategy and employs a few pruning techniques and the other is an approximate algorithm which provides a ln |q.ψ| approximation factor, where |q.ψ| denotes the number of query keywords. We conducted extensive experiments based on both real datasets and synthetic datasets, which verified our theoretical results and efficiency of our algorithms.

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Finding top-k relevant groups of spatial web objects

Cited by 20 publications

References 27 publications

Querying Geo-Textual Data

Querying Geo-Textual Data

On Generalizing Collective Spatial Keyword Queries

Inherent-Cost Aware Collective Spatial Keyword Queries

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