DESKS: Direction-Aware Spatial Keyword Search

Li, Guoliang; Feng, Jianhua; Xu, Jing

doi:10.1109/icde.2012.93

Cited by 99 publications

(48 citation statements)

References 24 publications

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“…Both kinds of algorithms are also extended with the restriction of query keywords [7][8][9], where the core operation is how to find the nearest neighbor o for a given query point q.…”

Section: Related Workmentioning

confidence: 99%

“…Given a query point q on a road network G, a kNN query finds k nearest neighbors (kNN) to q [1,2], or on the contrary, finds one nearest neighbor v for k query points, such that the sum of their distances to v is minimal [3][4][5][6], which is a hot research issue during the past years [1][2][3][4][5][6][7][8][9][10][11][12][13], due to its numerous applications in practice. For example, a tourist may want to search for k nearest hotels while walking in a city, a driver may want to find out k nearest gas stations during driving.…”

Section: Introductionmentioning

confidence: 99%

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Efficient kNN Computation for Multiple Query Points on Road Network

Tang¹

2016

IJGDC

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“…Both kinds of algorithms are also extended with the restriction of query keywords [7][8][9], where the core operation is how to find the nearest neighbor o for a given query point q.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient kNN Computation for Multiple Query Points on Road Network

Tang¹

2016

IJGDC

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“…Spatial Keyword Search. There are many studies on spatial keyword search [4], [5], [7], [8], [15], [16], [21], [23], [25], [26], [29], [32], [33], [34], [35], [36]. The first problem is knn based keyword search, which, given a location and a set of keywords, finds top-k nearest neighbors by considering the distance and textual relevancy.…”

Section: Related Workmentioning

confidence: 99%

Efficient Filtering Algorithms for Location-Aware Publish/Subscribe

Wang

et al. 2015

IEEE Trans. Knowl. Data Eng.

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Abstract-Location-based services have been widely adopted in many systems. Existing works employ a pull model or user-initiated model, where a user issues a query to a server which replies with location-aware answers. To provide users with instant replies, a push model or server-initiated model is becoming an inevitable computing model in the next-generation location-based services. In the push model, subscribers register spatio-textual subscriptions to capture their interests, and publishers post spatio-textual messages. This calls for a high-performance location-aware publish/subscribe system to deliver publishers' messages to relevant subscribers. In this paper, we address the research challenges that arise in designing a location-aware publish/subscribe system. We propose an R-tree based index by integrating textual descriptions into R-tree nodes. We devise efficient filtering algorithms and effective pruning techniques to achieve high performance. Our method can support both conjunctive queries and ranking queries. We discuss how to support dynamic updates efficiently. Experimental results show our method achieves high performance which can filter 500 messages in a second for 10 million subscriptions on a commodity computer

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“…For example, a person wants to find a restaurant within 10 minutes walking distance. Such queries, known as spatial keyword queries, have been extensively studied in recent years [30,59,64,80,83,121,125,131]. Typically, given a set of geo-textual objects, a spatial keyword query takes a location and a set of keywords as arguments and returns top-k objects that are spatially and textually relevant to these arguments.…”

Section: Problem Statementmentioning

confidence: 99%

“…A prototypical spatial keyword query takes a set of keywords and a location as input and finds geo-textual objects that are spatially and textually relevant. In literature, a wide range of work has already been proposed that study different aspects of spatial keyword search [15,17,18,23,30,33,37,42,61,64,116,118,119]. In this section, we introduce three types of spatial keyword queries: top-k spatial keyword queries, continuous spatial keyword queries and travel route search.…”

Section: Spatial Keyword Queriesmentioning

confidence: 99%

Efficient spatial keyword query processing on geo-textual data

Zheng¹

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The past decades have witnessed a transformation from a desktop-based web to a predominantly mobile web, where more often than not, users access the web from mobile devices. As a result, a huge volume of geo-textual web objects that have both geographical location and textual description have been generated. In literature, there have been lots of efforts in enabling efficient processing largescale geo-textual data under a variety of problem settings. In spite of the remarkable progress in this field, unsolved challenges remain. In this PhD thesis, I investigate a few interesting but challenging problems of this area. The contribution of this thesis can be summarized in three aspects. First, a set of new query predicates has been defined with a target of more diversified data types (e.g., activity trajectories), underlying spaces (e.g., road network) and query semantics, by which users can acquire their interested results more easily and effectively. Second, from a technical point of view, I have developed I/O efficient indexing structures and search algorithms to assure that a query can be answered within a reasonably small amount of time especially when dealing with massive geo-textual objects, which is not uncommon in real application scenarios. Last but not least, extensive empirical studies have been conducted based on real and large-scale datasets, which uncovered interesting patterns, rules and trends. These insights have shown directions for further improving my methodologies and also shed lights on future research. Below is a brief description of the contributions:First, I study the keyword-oriented queries on activity trajectories (KOAT). The activity trajectory is semantically enriched trajectory data, which embeds the information about behaviors of the moving objects. Therefore, searching activity trajectory is able to support a variety of applications for a better quality of location-based services. This work aims to return k trajectories that contain the most relevant keywords to the query and yield the least travel effort in the meantime. The main difference between this work and conventional spatial keyword query is that no query location exists in KOAT, which means the search area cannot be localized. To improve the query performance, a spatial-textual ranking function is first proposed between query keywords and activity trajectories. Then a best-first search algorithm based on a hybrid index structure is developed by applying effective pruning rules and efficient refinement strategies.Second, the problem of keyword-aware continuous k nearest neighbour search on road networks is studied, which computes the k nearest vertices that contain the query keywords issued by a moving object, and maintains the results continuously as it is moving on the road network. In particular, a iii framework, called labeling approach for continuous query (LARC), is proposed for processing the query with both low computation and communication overheads. First, a keyword-based pivot tree (KP-tree) is proposed ...

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DESKS: Direction-Aware Spatial Keyword Search

Cited by 99 publications

References 24 publications

Efficient kNN Computation for Multiple Query Points on Road Network

Efficient kNN Computation for Multiple Query Points on Road Network

Efficient Filtering Algorithms for Location-Aware Publish/Subscribe

Efficient spatial keyword query processing on geo-textual data

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