Efficient Algorithms for Flock Detection in Large Spatio-Temporal Data

Mhatre, Jui; Agrawal, Harsha; Sen, Sumit

doi:10.1007/978-3-030-37188-3_18

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…1. Designing faster algorithms for ST clustering using that can process massive amounts of data and thus enable large-scale studies like clustering of trajectories [11]. 2.…”

Section: Discussionmentioning

confidence: 99%

“…While most of these techniques extend spatial clustering by treating time as another dimension, the specific requirements of the application dictate the parameters and thresholds. For example, different parameters and adaptation of the algorithms are used in identification of disease clusters compared to identification of flocks, convoys, and swarms [11] For the analysis in this paper, we employ a spatial scan statistic that has been developed to test for geographical clusters and to identify their approximate location [12]. The spatial scan statistic imposes a circular window on the map and lets the center of the circle move over the area so that at different positions the window includes different sets of neighboring areas.…”

Section: St Clustering Algorithmsmentioning

confidence: 99%

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Spatio-temporal Variances of COVID-19 Active Cases and Genomic Sequence Data in India

Sen

2021

Intelligent Sustainable Systems

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Active cases of the COVID-19 pandemic have been reported for more than a year, and separately, there have been significant efforts to collect genome sequencing data during this period to track mutations and evolving strains. While both these datasets can be independently analyzed over space and time, the pattern and variances as evidenced by clustering of these datasets during two different waves of the epidemic in India show important differences. Quantification of these differences can help characterize relative need for collection of genomic data. Differences in the clusters are evident both spatially and temporally, and there are varying distances between such clusters as well. While similarity metrics and techniques have been developed in the context of spatio-temporal datasets, especially in moving objects, we demonstrate the limitations of such methods in analyzing epidemiological data. Finally, we highlight the challenges of such analysis in massive datasets and performance constraints at variant spatial and temporal scales.

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“…1. Designing faster algorithms for ST clustering using that can process massive amounts of data and thus enable large-scale studies like clustering of trajectories [11]. 2.…”

Section: Discussionmentioning

confidence: 99%

Section: St Clustering Algorithmsmentioning

confidence: 99%

Spatio-temporal Variances of COVID-19 Active Cases and Genomic Sequence Data in India

Sen

2021

Intelligent Sustainable Systems

Self Cite

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Discovering Moving Flock Patterns in Movement Data: A Reeb Graph-Based Approach

Zhang,

Van de Weghe

2024

Applied Sciences

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Movement pattern mining is a key focus in movement data research, with moving flock patterns holding particular significance due to their potential to reveal valuable insights across various domains. This research aimed to discover moving flock patterns in movement data. To achieve this, we first developed a more precise definition of a moving flock by refining the existing definitions. Then, we proposed a taxonomy of flock patterns, enabling the derivation of distinct types of moving flock patterns. Finally, we developed a Reeb graph-based approach to discover desired moving flock patterns. The effectiveness of the approach was validated using movement data obtained from a real football match. In the results, 72 and 94 moving flock patterns were discovered under different cases, respectively. Moreover, the proportion of desired moving flock patterns in these cases was 29.17% and 24.47%, respectively. The results show that the proposed approach effectively detects the desired moving flock patterns, and the findings provide insightful information to sports professionals.

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Efficient Algorithms for Flock Detection in Large Spatio-Temporal Data

Cited by 2 publications

References 16 publications

Spatio-temporal Variances of COVID-19 Active Cases and Genomic Sequence Data in India

Spatio-temporal Variances of COVID-19 Active Cases and Genomic Sequence Data in India

Discovering Moving Flock Patterns in Movement Data: A Reeb Graph-Based Approach

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