Efficient Discovery of Traveling Companion from Evolving Trajectory Data Stream

Puntheeranurak, Sutheera; Shein, Thi Thi; Imamura, Makoto

doi:10.1109/compsac.2018.00069

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…To reduce computational time complexity in the clustering phase, we apply the micro-group based clustering algorithm that we proposed previously [11]. Moreover, to avoid the overhead intersection between candidate and cluster in candidate extension, we define the following lemmas and a definition.…”

Section: Loose Group Companion Discovery Frameworkmentioning

confidence: 99%

“…Therefore, we developed a microgroup based clustering algorithm for discovering traveling companion to reduce running time. We have published details of this algorithm [11].…”

Section: Introductionmentioning

confidence: 99%

“…It calls the buddy-based loose companion discovery that still has high computational time complexity in the buddy-based clustering, and the candidate extension.Thus, we focus on finding the loose group companions efficiently from trajectory data streams. To address these problems, we adopt our micro-group based clustering and introduce pruning strategies in candidate extension for loose group companion discovery.This paper is an extension of an efficient traveling companion discovery framework [11] by using micro-group based clustering in the discovery of loose group companions. Our main contributions are:…”

Section: Introductionmentioning

confidence: 99%

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Discovery of Loose Group Companion From Trajectory Data Streams

2020

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The general usability of location tracking devices has been generated a high volume of spatial-temporal data in the form of trajectory. Exploring useful knowledge from these trajectory data can contribute to understanding many real-world applications, such as traffic monitoring and weather forecasting. The main task of trajectory data analysis is the tracking of an object group movement pattern. Existing algorithms, studying the evolving structure of moving object trajectories, have high computational complexity, particularly when tracking loose group companions. To address this problem, we describe a loose group companion tracking framework over trajectory data streams in an incremental manner, which reduces computational time. Loose group companion is the moving objects group that travels together. However, some members are allowed to leave at some timestamp. A crucial part of our framework is the micro-group based loose group companion discovery. It follows a moving object group and then incrementally detects the loose group companions. We validated our techniques using two real vehicle data sets and one synthetic data set. Our approach was, on average, 45% faster than previous algorithms.INDEX TERMS Group pattern, loose group companion, trajectory data stream, moving object clustering, spatial-temporal pattern.

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Section: Loose Group Companion Discovery Frameworkmentioning

confidence: 99%

“…Therefore, we developed a microgroup based clustering algorithm for discovering traveling companion to reduce running time. We have published details of this algorithm [11].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discovery of Loose Group Companion From Trajectory Data Streams

2020

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“…Finally, several studies [1,6,7,24,25,26,27,34] investigate clustering on streaming trajectories. Although clustering is the first step of co-movement pattern mining, these existing efforts provide centralized methods, which are unable to contend with support large-scale steaming trajectories.…”

Section: Co-movement Pattern Miningmentioning

confidence: 99%

Real-time distributed co-movement pattern detection on streaming trajectories

et al. 2019

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With the widespread deployment of mobile devices with positioning capabilities, increasingly massive volumes of trajectory data are being collected that capture the movements of people and vehicles. This data enables co-movement pattern detection, which is important in applications such as trajectory compression and future-movement prediction. Existing co-movement pattern detection studies generally consider historical data and thus propose offline algorithms. However, applications such as future movement prediction need real-time processing over streaming trajectories. Thus, we investigate real-time distributed co-movement pattern detection over streaming trajectories. Existing off-line methods assume that all data is available when the processing starts. Nevertheless, in a streaming setting, unbounded data arrives in real time, making pattern detection challenging. To this end, we propose a framework based on Apache Flink, which is designed for efficient distributed streaming data processing. The framework encompasses two phases: clustering and pattern enumeration. To accelerate the clustering, we use a range join based on two-layer indexing, and provide techniques that eliminate unnecessary verifications. To perform pattern enumeration efficiently, we present two methods FBA and VBA that utilize id-based partitioning. When coupled with bit compression and candidate-based enumeration techniques, we reduce the enumeration cost from exponential to linear. Extensive experiments offer insight into the efficiency of the proposed framework and its constituent techniques compared with existing methods.

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“…Various movement patterns, such as convoys [2], travel companionships [3], collective motion [4], and trajectory clustering [5], have been extensively studied and applied in diverse contexts, from urban computing to location prediction and tra c management, solidifying trajectory data as a central theme in data mining [6].…”

Section: Introductionmentioning

confidence: 99%

Detection of organized group movements in trajectory data

Silva,

Maia

2024

Preprint

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Background Georeferencing sensors are available in people, animals, or objects to generate trajectory data for relevant civilian and military applications. A common task in these applications is detecting and recognizing organized group movement patterns. Methods based on Shannon Entropy and Spectral Clustering are two widely used general approaches, however, they fail to detect organized group movements for various motion patterns.Methods To address these limitations, this article proposes a Network Inference-based approach followed by clustered data analysis and evaluation of cluster quality using the DBSCAN and Silhouette Coefficient algorithms.Results In our experiments, we compared our approach against Shannon Entropy and Spectral Clustering on simulated and real data, using the organization index median in a run, as the metric. Specifically, the proposed method had superior performance in three simulated scenarios, which were Ants, Wolf Sheep Predation, and Flocking, and in two real data sets, which are Fish School Trajectories and PETS09-S2L1.Conclusion Our approach presents a promising solution for identifying organized group movements, critical for emergency decision-making and resource optimization in practical applications.

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Efficient Discovery of Traveling Companion from Evolving Trajectory Data Stream

Cited by 13 publications

References 14 publications

Discovery of Loose Group Companion From Trajectory Data Streams

Discovery of Loose Group Companion From Trajectory Data Streams

Real-time distributed co-movement pattern detection on streaming trajectories

Detection of organized group movements in trajectory data

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