A single quadtree-based algorithm for top-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="mml37" display="inline" overflow="scroll" altimg="si1.gif"><mml:mi>k</mml:mi></mml:math>spatial keyword query

Hong, Hsiang-Jen; Chiu, Ge-Ming; Tsai, Wan-Yu

doi:10.1016/j.pmcj.2017.09.009

Cited by 9 publications

(3 citation statements)

References 34 publications

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“…In order to organize spatial and textual information, many hybrid index structures have been proposed. The spatial indices can be categorized into three main categories: tree based (R-tree, quad-tree) [13,14,15,16], grid based [17,18], and space filling curve based [19]. In contrast, most textual indices use an inverted file structure.…”

Section: Related Workmentioning

confidence: 99%

“…Inserting an individual post o i into a spatial index is typically performed by traversing the tree from its root node to find the leaf node that contains the location of o i [16,4], i.e., first executing a query for o i . However, this approach is not sufficiently responsive for real-time stream processing with high arrival rates, because it incurs an additional load of thousands of queries per second.…”

Section: Index Insertion In the Hybrid Quad-treementioning

confidence: 99%

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Efficient top-k recently-frequent term querying over spatio-temporal textual streams

Dam

Chester

Nørvåg

et al. 2021

Information Systems

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Section: Related Workmentioning

confidence: 99%

Section: Index Insertion In the Hybrid Quad-treementioning

confidence: 99%

Efficient top-k recently-frequent term querying over spatio-temporal textual streams

Dam

Chester

Nørvåg

et al. 2021

Information Systems

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“…The experimental results show that, in general, it is always among the best indexes for the three types of queries. Taking the S2I as a basic component, some other indexes have been proposed in [32]- [34].…”

Section: Related Workmentioning

confidence: 99%

cBiK: A Space-Efficient Data Structure for Spatial Keyword Queries

et al. 2020

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A vast amount of geo-referenced data is being generated by mobile devices and other sensors increasing the importance of spatio-textual analyses on such data. Due to the large volume of data, the use of indexes to speed up the queries that facilitate such analyses is imperative. Many disk resident indexes have been proposed for different types of spatial keyword queries, but their efficiency is harmed by their high I/O costs. In this work, we propose cBiK, the first spatio-textual index that uses compact data structures to reduce the size of the structure, hence facilitating its usage in main memory. Our experimental evaluation, shows that this approach needs half the space and is more than one order of magnitude faster than a disk resident state-of-the-art index. Also, we show that our approach is competitive even in a scenario where the disk resident data structure is warmed-up to fit in main memory. INDEX TERMS Bitmap, compact data structure, kNN, spatial keyword query, spatio-textual data.

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Collective POI Querying Based on Multiple Keywords and User Preference

Liu

et al. 2019

Database Systems for Advanced Applications

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A single quadtree-based algorithm for top-kspatial keyword query

Cited by 9 publications

References 34 publications

Efficient top-k recently-frequent term querying over spatio-temporal textual streams

Efficient top-k recently-frequent term querying over spatio-temporal textual streams

cBiK: A Space-Efficient Data Structure for Spatial Keyword Queries

Collective POI Querying Based on Multiple Keywords and User Preference

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