Efficient Group Top-k Spatial Keyword Query Processing

Yao, Kai; Li, Jianjun; Li, Guohui; Luo, Changyin

doi:10.1007/978-3-319-45814-4_13

Cited by 10 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The synthetic dataset Flickr-US is produced by obtaining geographical locations from the US Board on Geographic Names. 8 Like the dataset Flickr-RP, we use these geographical location information to generate new geo-tags for the images in Flickr.…”

Section: A Datasets and Workload 1) Datasetsmentioning

confidence: 99%

“…This query aims to find spatial objects by taking into account both spatial proximity and relevance of keywords. Several types of spatial keyword search, i.e., collective spatial keyword query [5], m-closest keywords search [6], best keyword cover search [7], group top-k spatial keyword query [8] and etc., are studied deeply and applied widely in many scenarios to provide efficient spatial keyword query.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reverse Spatial Visual Top-$k$ Query

Zhu

Song

et al. 2020

IEEE Access

View full text Add to dashboard Cite

With the wide application of mobile Internet techniques an location-based services (LBS), massive multimedia data with geo-tags has been generated and collected. In this paper, we investigate a novel type of spatial query problem, named reverse spatial visual top-k query (RSVQ k) that aims to retrieve a set of geo-images that have the query as one of the most relevant geo-images in both geographical proximity and visual similarity. Existing approaches for reverse top-k queries are not suitable to address this problem because they cannot effectively process unstructured data, such as image. To this end, firstly we propose the definition of RSVQ k problem and introduce the similarity measurement. A novel hybrid index, named VR 2-Tree is designed, which is a combination of visual representation of geo-image and R-Tree. Besides, an extension of VR 2-Tree, called CVR 2-Tree is introduced and then we discuss the calculation of lower/upper bound, and then propose the optimization technique via CVR 2-Tree for further pruning. In addition, a search algorithm named RSVQ k algorithm is developed to support the efficient RSVQ k query. Comprehensive experiments are conducted on four geo-image datasets, and the results illustrate that our approach can address the RSVQ k problem effectively and efficiently. INDEX TERMS Geo-image, reverse top-k query, spatial visual query, hybrid index.

show abstract

Section: A Datasets and Workload 1) Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reverse Spatial Visual Top-$k$ Query

Zhu

Song

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Rocha-Junior and Norvag [5] addressed the problem of top-k spatial keyword query in road network for the first time. Spatial keyword queries with multiple query points was studied in [6], [12]. Cong and Jensen [14] offered an overview of different types of keyword queries based on geo-textual data.…”

Section: A the Spatial Keyword Querymentioning

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

“…In a study parallel to ours, the group top-k spatial keyword query has been proposed recently [23]. is paper presents a branchand-bound technique to retrieve the top-k spatial keyword objects for only one group of queries.…”

Section: Related Workmentioning

confidence: 99%

The Flexible Group Spatial Keyword Query

Ahmad

Kamal

Ali

et al. 2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

We present a new class of service for location based social networks, called the Flexible Group Spatial Keyword ery, which enables a group of users to collectively find a point of interest (POI) that optimizes an aggregate cost function combining both spatial distances and keyword similarities. In addition, our query service allows users to consider the trade-offs between obtaining a suboptimal solution for the entire group and obtaining an optimimized solution but only for a subgroup.We propose algorithms to process three variants of the query: (i) the group nearest neighbor with keywords query, which finds a POI that optimizes the aggregate cost function for the whole group of size n, (ii) the subgroup nearest neighbor with keywords query, which finds the optimal subgroup and a POI that optimizes the aggregate cost function for a given subgroup size m (m ≤ n), and (iii) the multiple subgroup nearest neighbor with keywords query, which finds optimal subgroups and corresponding POIs for each of the subgroup sizes in the range [m, n]. We design query processing algorithms based on branch-and-bound and best-first paradigms. Finally, we provide theoretical bounds and conduct extensive experiments with two real datasets which verify the effectiveness and efficiency of the proposed algorithms.

show abstract

Efficient Group Top-k Spatial Keyword Query Processing

Cited by 10 publications

References 15 publications

Reverse Spatial Visual Top-$k$ Query

Reverse Spatial Visual Top-$k$ Query

Top-K Collective Spatial Keyword Queries

The Flexible Group Spatial Keyword Query

Contact Info

Product

Resources

About