Spatio-temporal top-k term search over sliding window

Chen, Lisi; Shang, Shuo; Yao, Bin; Zheng, Kai

doi:10.1007/s11280-018-0606-x

Cited by 17 publications

(10 citation statements)

References 63 publications

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“…The experiment uses a computer with a 3.4 GHZ dual-core CPU and 32 GB of memory. Following the general setting of the traditional top-k query system, we assume that the index is stored in memory to support real-time response [16]. When we compare SPR with TMG, they are always in the same dataset state.…”

Section: Methodsmentioning

confidence: 99%

An Efficient Top-k Query Scheme Based on Multilayer Grouping

et al. 2019

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The top-k query is to find the k data that has the highest scores from a candidate dataset. Sorting is a common method to find out top-k results. However, most of existing methods are not efficient enough. To remove this issue, we propose an efficient top-k query scheme based on multilayer grouping. First, we find the reference item by computing the average score of the candidate dataset. Second, we group the candidate dataset into three datasets: winner set, middle set and loser set based on the reference item. Third, we further group the winner set to the second-layer three datasets according to k value. And so on, until the data number of winner set is close to k value. Meanwhile, if k value is larger than the data number of winner set, we directly return the winner set to the user as a part of top-k results almost without sorting. In this case, we also return the top results with the highest scores from the middle set almost without sorting. Based on above innovations, we almost minimize the sorting. Experimental results show that our scheme significantly outperforms the current classical method on the performance of memory consumption and top-k query.

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Section: Methodsmentioning

confidence: 99%

An Efficient Top-k Query Scheme Based on Multilayer Grouping

et al. 2019

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“…Note that if w i exists in min-heap H[s], we need to update w i to reflect the new LTP score (Lines 11-13). If the current LTP score of w i exceeds that of w t , we pop w t and push w i onto the min-heap (Lines [14][15][16]. This completes the update of the top-k result of s, which is maintained by the min-heap.…”

Section: Subscription Matchingmentioning

confidence: 99%

“…After updating M , we need to update the min-heap H. Specifically, if w is in H, we update H based on the new TP score of w computed by Equation 18 (lines 13-14). Else we retrieve the top element (term) w t in H and compare the current TP scores between w t and w (lines [16][17]. In particular, if the current TP score of w t is smaller than that of w, we replace w t with w in H and update H (lines 18-19).…”

Section: Update Of Tp Indexmentioning

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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“…Chen et al [21] study the top-k term search method over massive volumes of geo-tagged streaming data. The method can be widely used in discovering the most frequent nearby terms from tremendous stream data.…”

Section: B Research On Spatial Preferencementioning

confidence: 99%

A Novel Range Search Scheme Based on Frequent Computing for Edge-Cloud Collaborative Computing in CPSS

Cui

Yang

et al. 2020

IEEE Access

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Due to the rapid advances of Information and Communication Technologies (ICT), especially 5G and Artificial Intelligence (AI), the Internet of Everything is gradually becoming a reality, and human beings living environments are becoming smarter and smarter. Every day there will be generated large amounts of data in Humans-Machines-Things hybrid space, which is also called Cyber-Physical-Social Systems (CPSSs). Today, the city we live in has become a data-driven society. However, how to effectively mine valuable information from these massive data to provide proactive and personalized services for human beings is a challenging problem. Thus, top-k search remains an important topic of ongoing research. In this paper, we focus on a basic problem of geo-tagged data: find the top-k frequent terms among the geo-tagged data in a specific region from the cloud. We first construct a Region Tree Index (RTI) for geo-tagged data. Then the list storage structure is proposed to Store Sorted Terms and Weights (SSTW) in RTI. And then an efficient kTermsSearch algorithm is presented to compute top-k frequent terms in a given region. Finally, extensive experiments verify the validity of the proposed scheme. INDEX TERMS Cyber-physical-social system (CPSS), frequent computing, top-k search, edge-cloud collaborative computing.

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Spatio-temporal top-k term search over sliding window

Cited by 17 publications

References 63 publications

An Efficient Top-k Query Scheme Based on Multilayer Grouping

An Efficient Top-k Query Scheme Based on Multilayer Grouping

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

A Novel Range Search Scheme Based on Frequent Computing for Edge-Cloud Collaborative Computing in CPSS

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