Spatial Keyword Querying

Cao, Xin; Chen, Lisi; Cong, Gao; Jensen, Christian S.; Qu, Qiang; Skovsgaard, Anders; Wu, Dingming; Yiu, Man Lung

doi:10.1007/978-3-642-34002-4_2

Cited by 113 publications

(72 citation statements)

References 16 publications

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“…Lisi Chen [7] provide a survey of indices to store keywords as well as spatial location. Xin Cao [8] dealt with various spatial keyword queries. The concept of Boolean range query fall under the category of spatial keyword query.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Approach on Spatial Objects for Optimal Route Search Using Best Keyword Cover Query

2018

IJRTER

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Abstract-Optimal route search using spatial keyword query focus on keyword searching using best keyword cover query which is a form of spatial keyword query. It operates on spatial objects stored in spatial database and comes with algorithms that can retrieve results very fast. Best keyword cover query aims to find objects associated with keywords. The method proposed considers keyword rating, keyword relevance and spatial relevance. It also helps to retrieve data based on Boolean range query.Keywords-Spatial keyword query, spatial objects, spatial database, best keyword cover query.

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Section: Literature Surveymentioning

confidence: 99%

A Novel Approach on Spatial Objects for Optimal Route Search Using Best Keyword Cover Query

2018

IJRTER

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“…We touch upon topics covered in earlier work by the authors and others. Reference [2] offers an overview of the field and contains references to additional work.…”

Section: Spatial Challengesmentioning

confidence: 99%

Towards context-aware search and analysis on social media data

Derczynski

Yang

Jensen

2013

Proceedings of the 16th International Conference on Extending Database Technology

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Social media has changed the way we communicate. Social media data capture our social interactions and utterances in machine readable format. Searching and analysing massive and frequently updated social media data brings significant and diverse rewards across many different application domains, from politics and business to social science and epidemiology.A notable proportion of social media data comes with explicit or implicit spatial annotations, and almost all social media data has temporal metadata. We view social media data as a constant stream of data points, each containing text with spatial and temporal contexts. We identify challenges relevant to each context, which we intend to subject to context aware querying and analysis, specifically including longitudinal analyses on social media archives, spatial keyword search, local intent search, and spatio-temporal intent search. Finally, for each context, emerging applications and further avenues for investigation are discussed.

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“…Note that most of search engine queries with local intent contain only 1-2 keywords [1]; and it is reported in an analysis on a large Map query log [17], nearly all queries contain fewer than 5 words. Each set comprises 50 queries.…”

Section: Experimental Study 71 Experimental Settingsmentioning

confidence: 99%

“…In this work, we use the text relevance of an object to a given query as the object's weight, as it is used in existing studies of spatial keyword querying [1]. The text relevance of a region R to a query Q is computed using an information retrieval model.…”

Section: Indexing Structurementioning

confidence: 99%

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Retrieving regions of interest for user exploration

et al. 2014

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We consider an application scenario where points of interest (PoIs) each have a web presence and where a web user wants to identify a region that contains relevant PoIs that are relevant to a set of keywords, e.g., in preparation for deciding where to go to conveniently explore the PoIs. Motivated by this, we propose the length-constrained maximum-sum region (LCMSR) query that returns a spatial-network region that is located within a general region of interest, that does not exceed a given size constraint, and that best matches query keywords. Such a query maximizes the total weight of the PoIs in it w.r.t. the query keywords. We show that it is NP-hard to answer this query. We develop an approximation algorithm with a (5 + ε) approximation ratio utilizing a technique that scales node weights into integers. We also propose a more efficient heuristic algorithm and a greedy algorithm. Empirical studies on real data offer detailed insight into the accuracy of the proposed algorithms and show that the proposed algorithms are capable of computing results efficiently and effectively.

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Spatial Keyword Querying

Cited by 113 publications

References 16 publications

A Novel Approach on Spatial Objects for Optimal Route Search Using Best Keyword Cover Query

A Novel Approach on Spatial Objects for Optimal Route Search Using Best Keyword Cover Query

Towards context-aware search and analysis on social media data

Retrieving regions of interest for user exploration

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