A filter-and-refine approach to mine spatiotemporal co-occurrences
Karthik Ganesan Pillai,Rafal A. Angryk,Berkay Aydin +2 more
- pp 104-113
TLDR
A novel and effective filter-and-refine algorithm to efficiently find prevalent STCOPs in massive spatiotemporal data repositories with polygon shapes that move and evolve over time is introduced.Abstract:
Spatiotemporal co-occurrence patterns (STCOPs) represent the subsets of event types that occur together in both space and time. However, the discovery of STCOPs in data sets with extended spatial representations that evolve over time is computationally expensive because of the necessity to calculate interest measures to assess the co-occurrence strength, and the number of candidates for STCOPs growing exponentially with the number of spatiotemporal event types. In this paper, we introduce a novel and effective filter-and-refine algorithm to efficiently find prevalent STCOPs in massive spatiotemporal data repositories with polygon shapes that move and evolve over time. We provide theoretical analysis of our approach, and follow this investigation with a practical evaluation of our algorithm effectiveness on three real-life data sets and one artificial data set.read more
Citations
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Spatio-Temporal Data Mining: A Survey of Problems and Methods
TL;DR: A broad survey of this relatively young field of spatio-temporal data mining is presented, and literature is classified into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining.
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Spatiotemporal Data Mining: A Computational Perspective
Shashi Shekhar,Zhe Jiang,Reem Y. Ali,Emre Eftelioglu,Xun Tang,Venkata M. V. Gunturi,Xun Zhou +6 more
TL;DR: This survey reviews recent computational techniques and tools in spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families, focusing on several major pattern families.
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Spatio-Temporal Data Mining: A Survey of Problems and Methods
TL;DR: A broad survey of this relatively young field of spatio-temporal data mining is presented and literature is classified into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining.
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Generative Adversarial Networks for Spatio-temporal Data: A Survey
Nan Gao,Hao Xue,Wei Shao,Sichen Zhao,Kyle Kai Qin,Arian Prabowo,Mohammad Saiedur Rahaman,Flora D. Salim +7 more
TL;DR: A comprehensive review of the recent developments of GANs for spatio-temporal data and the common practices for evaluating the performance of spatio/temporal applications with GAns is conducted.
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Mining association rules between sets of items in large databases
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