Local correlation detection with linearity enhancement in streaming data

Xie, Qing; Shang, Shuo; Yuan, Bo; Pang, Chaoyi; Zhang, Xiangliang

doi:10.1145/2505515.2505746

Cited by 22 publications

(13 citation statements)

References 28 publications

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“…Third, we may also study how to integrate textual data to routing problems [32, 33, 40-42, 52, 56, 68] as well as spatio-textual routing problem. Fourth, we may study how to integrate streaming data sampling methods and pattern analysis [12,13,16,17,50,53,54,58,67] integrating with spatio-textual data.…”

Section: Systems For Term-based Searchingmentioning

confidence: 99%

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: Systems For Term-based Searchingmentioning

confidence: 99%

Spatio-temporal top-k term search over sliding window

et al. 2018

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“…How to use social media data to achieve a better recommendation is an interesting direction. The following related papers [2][3][4][12][13][14]16,17,23,[40][41][42] provide some inspirations. In the future, it is also of interest to study routing problem with geo-social data [18, 20, 32-34, 38, 39, 49].…”

Section: Related Workmentioning

confidence: 99%

Discovery of accessible locations using region-based geo-social data

Wang

Zhu

et al. 2018

World Wide Web

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Geo-social data plays a significant role in location discovery and recommendation. In this light, we propose and study a novel problem of discovering accessible locations in spatial networks using region-based geo-social data. Given a set Q of query regions, the top-k accessible location discovery query (kALDQ) finds k locations that have the highest spatial-density correlations to Q. Both the spatial distances between locations and regions and the POI (point of interest) density within the regions are taken into account. We believe that this type of kALDQ query can bring significant benefit to many applications such as travel planning, facility allocation, and urban planning. Three challenges exist in kALDQ: (1) how to model the spatial-density correlation practically, (2) how to prune the search space effectively, and (3) how to schedule the searches from multiple query regions. To tackle the challenges and process kALDQ effectively and efficiently, we first define a series of spatial and density metrics to model the spatial-density correlation. Then we propose a novel three-phase solution with a pair of upper and lower bounds of the spatial-density correlation and a heuristic scheduling strategy to sched

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“…Various indexing techniques for querying the correlations of static time-series data stored in a centralized system have been proposed in [11], [12], [20], [23]. Such techniques are not suitable for our dynamic environment, where the index maintenance cost incurs high processing latency.…”

Section: Related Workmentioning

confidence: 99%

SigCO: Mining significant correlations via a distributed real-time computation engine

Guo

Calbimonte

Zhuang

et al. 2015

2015 IEEE International Conference on Big Data (Big Data)

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Abstract-The dramatic rise of time-series data produced in a variety of contexts, such as stock markets, mobile sensing, sensor networks, data centre monitoring, etc., has fuelled the development of large-scale distributed real-time computation systems (e.g., Apache Storm, Samza, Spark Streaming, S4, etc.). However, it is still unclear how certain time series mining tasks could be performed using such new emerging systems. In this paper, we focus on the task of efficiently discovering statistically significant correlations among a large number of time series via a distributed realtime computation engine. We propose a framework referred to as SigCO. In SigCO, we put forward a novel partition-aware data shuffling, which is able to adaptively shuffle time series data only to the relevant nodes of the distributed real-time computation engine. On the other hand, in SigCO we design a δ-hypercube structure based correlation computation approach which is capable of pruning unnecessary correlation computations. Finally, our extensive experimental evaluations on real and synthetic datasets establish that SigCO outperforms the baseline approaches in terms of diverse performance metrics.

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Local correlation detection with linearity enhancement in streaming data

Cited by 22 publications

References 28 publications

Spatio-temporal top-k term search over sliding window

Spatio-temporal top-k term search over sliding window

Discovery of accessible locations using region-based geo-social data

SigCO: Mining significant correlations via a distributed real-time computation engine

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