On Generalizing Collective Spatial Keyword Queries

Chan, Harry Kai-Ho; Long, Cheng; Wong, Raymond Chi-Wing

doi:10.1109/tkde.2018.2800746

Cited by 31 publications

(41 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[11] considered the problem of scalable collective spatial keyword queries and proposed a distributed method to solve this problem effectively. In [4], a unified cost function and a unified method are proposed for the collective spatial keyword query problem to systematically solve the query problem.…”

Section: Related Workmentioning

confidence: 99%

Time-aware collective spatial keyword query

Chen

Zhao

Liu

2021

ComSIS

View full text Add to dashboard Cite

The collective spatial keyword query is a hot research topic in the database community in recent years, which considers both the positional relevance to the query location and textual relevance to the query keywords. However, in real life, the temporal information of object is not always valid. Based on this, we define a new query, namely time-aware collective spatial keyword query (TCoSKQ), which considers the positional relevance, textual relevance, and temporal relevance between objects and query at the same time. Two evaluation functions are defined to meet different needs of users, for each of which we propose an algorithm. Effective pruning strategies are proposed to improve query efficiency based on the two algorithms. Finally, the experimental results show that the proposed algorithms are efficient and scalable.

show abstract

Section: Related Workmentioning

confidence: 99%

Time-aware collective spatial keyword query

Chen

Zhao

Liu

2021

ComSIS

View full text Add to dashboard Cite

show abstract

“…The top-k nearest neighbor query considers both the location proximity and text relevance between spatial objects and query. Collective Spatial Keyword Query (CSKQ) [13], [14] returns a set of objects that collectively cover user's query keywords, those objects are close to the query location and have small interobject distances. Following the CSKQ, the Reverse Collective Spatial Keyword Query (RCSKQ) [15], [16] returns a region, in which the query objects are qualified objects with the highest spatial and textual similarity.…”

Section: Related Workmentioning

confidence: 99%

A Personalized and Approximated Spatial Keyword Query Approach

Meng

Zhang

2020

IEEE Access

View full text Add to dashboard Cite

With the universal use of GPS and rapid increase of spatial Web objects, spatial keyword query has been widely used in Location-Based Services (LBS). Most of the existing spatial keyword query processing models only support location proximity and strict text matching which makes the semantically related objects cannot be provided to users and even may lead to the empty answer problem. In addition, the current index structures (such as IR-tree, Quadtree) cannot process numerical attributes which are usually contained in the descriptive information associated to the spatial objects. To deal with these problems, this paper proposes a spatial keyword query method that can support semantic approximate query processing. Firstly, the user original query is expanded by Conditional Generative Adversarial Nets (CGAN) method to generate a series of query keywords that are semantically related to the original query keywords. And then, a hybrid index structure called AIR-tree is built to facilitate the query matching, which can support the text semantic matching and process numerical attributes with Skyline method. Experimental analysis and results demonstrate that the proposed method achieves higher execution efficiency and better user satisfaction compared with the state-of-the-art methods.

show abstract

“…Jin et al [43] formalized a CoSKQ on a knowledge base which proved to be NP-hard. In [44], the CoSKQ problem was systematically studied by putting forward a unified cost function which can be illustrated with existing different cost functions and a unified method which includes two algorithms, one is an exact algorithm and the other is an approximate algorithm. None of these work can be directly used in solving our problem because they usually return one group of objects as the answer and they are all index-based.…”

Section: B Collective Spatial Keyword Querymentioning

confidence: 99%

“…TkCoSKQ returns {S 1 , S 2 , S 3 } as the final result when k is set to 3. For all we know, although different variants of CoSKQ have been extensively studied in [39], [42], and [44], no one has systematically studied TkCoSKQ. Even if there are relevant content, e.g., K-MAXM-E [34], but its approach has been challenged.…”

Section: Introductionmentioning

confidence: 99%

Top-K Collective Spatial Keyword Queries

Zhou

Yang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

With the advent of a large number of spatial-textual data, collective spatial keyword queries have been widely studied in recent years. However, the collective spatial keyword query studied so far usually looks for only a set of objects. In addition, the existing collective spatial keyword query algorithms are all based on index structure, which requires excessive additional memory overhead. In this paper, we study the Top-k collective spatial keyword queries(TkCoSKQ), which aims at retrieving a set G including k sets of objects. Each group of object set can cover all the query keywords, and the objects in the set are close to the query position and have the minimum inter-object distance. We prove that the TkCoSKQ problem is NP-hard, and then propose two index-independent algorithms based on the spatial-textual similarity constraint, containing an exact algorithm and a heuristic algorithm. In addition, a variety of effective pruning strategies are presented to minimize the search scope. A large number of experiments on real datasets demonstrate the effectiveness and scalability of the proposed algorithms. INDEX TERMS Algorithm, collective, spatial keyword query, top-k.

show abstract

On Generalizing Collective Spatial Keyword Queries

Cited by 31 publications

References 27 publications

Time-aware collective spatial keyword query

Time-aware collective spatial keyword query

A Personalized and Approximated Spatial Keyword Query Approach

Top-K Collective Spatial Keyword Queries

Contact Info

Product

Resources

About