Location-Based Top-k Term Querying over Sliding Window

Xu, Ying; Chen, Lisi; Yao, Bin; Shang, Shuo; Zhu, Shengqi; Zheng, Kai; Li, Fang

doi:10.1007/978-3-319-68783-4_21

Cited by 20 publications

(10 citation statements)

References 41 publications

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“…Because of the sliding-window scenario of LkTQ and STkTQ, the indexing mechanism must be able to handle geo-textual data streams with high arrival rate. This paper expands on a previous study [55]. In particular, based on the LkTQ we define a novel query STkTQ for discovering bursty and trending terms over a stream of geo-textual objects.…”

Section: Introductionmentioning

confidence: 86%

Spatio-temporal top-k term search over sliding window

et al. 2018

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In part due to the proliferation of GPS-equipped mobile devices, massive volumes of geo-tagged streaming text messages are becoming available on social media. It is of great interest to discover most frequent nearby terms from such tremendous stream data. In this paper, we present novel indexing, updating, and query processing techniques that are capable of discovering topk most frequent nearby terms over a sliding window. Specifically, given a query location and a set of geo-tagged messages within a sliding window, we study the problem of searching for the top-k terms by considering term frequency, spatial proximity, and term freshness. We develop a novel and efficient mechanism to solve the problem, including a quad-tree based indexing structure, indexing update technique, and a best-first based searching algorithm. An empirical study is conducted to show that our proposed techniques are efficient and fit for users' requirements through varying a number of parameters.

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Section: Introductionmentioning

confidence: 86%

Spatio-temporal top-k term search over sliding window

et al. 2018

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“…The queries are similar to previously covered Euclidean space queries except that they target streaming data. Location-based term queries [105,131,139,151] This type of query focuses on term frequencies in object streams. Objects are filtered by spatial and textual constraints, upon which term frequencies are extracted from the remaining objects.…”

Section: Publish/subscribe Systemsmentioning

confidence: 99%

Location- and keyword-based querying of geo-textual data: a survey

2021

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With the broad adoption of mobile devices, notably smartphones, keyword-based search for content has seen increasing use by mobile users, who are often interested in content related to their geographical location. We have also witnessed a proliferation of geo-textual content that encompasses both textual and geographical information. Examples include geo-tagged microblog posts, yellow pages, and web pages related to entities with physical locations. Over the past decade, substantial research has been conducted on integrating location into keyword-based querying of geo-textual content in settings where the underlying data is assumed to be either relatively static or is assumed to stream into a system that maintains a set of continuous queries. This paper offers a survey of both the research problems studied and the solutions proposed in these two settings. As such, it aims to offer the reader a first understanding of key concepts and techniques, and

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“…Wang et al [59] identify local top-k maximal frequent keyword co-occurrence patterns over streams of geo-tagged tweets. Other studies on spatio-temporal search include counting-based term queries [16,64], spatial keyword search over geo-textual data streams [15,66], route planning [8,14,24,47,[52][53][54]62], and trajectory search [46,[48][49][50][51]71].…”

Section: Spatio-temporal Searchmentioning

confidence: 99%

Top-k term publish/subscribe for geo-textual data streams

et al. 2020

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Massive amounts of data that contain spatial, textual, and temporal information are being generated at a rapid pace. With streams of such data, which includes checkins and geo-tagged tweets, available, users may be interested in being kept up-to-date on which terms are popular in the streams in a particular region of space. To enable this functionality, we aim at efficiently processing two types of general top-k term subscriptions over streams of spatio-temporal documents: Region-based Top-k Spatial-Temporal Term (RST) subscriptions and Similarity-based Top-k Spatial-Temporal Term (SST) subscriptions. RST subscriptions continuously maintain the top-k most popular trending terms within a user-defined region. SST subscriptions free users from defining a region and maintain top-k locally popular terms based on a ranking function that com-

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Location-Based Top-k Term Querying over Sliding Window

Cited by 20 publications

References 41 publications

Spatio-temporal top-k term search over sliding window

Spatio-temporal top-k term search over sliding window

Location- and keyword-based querying of geo-textual data: a survey

Top-k term publish/subscribe for geo-textual data streams

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