Spatio-temporal pattern detection in spatio-temporal graphs

IF 0.2 Q4 REMOTE SENSING
Kamaldeep Singh Oberoi, Géraldine Del Mondo
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引用次数: 0

Abstract

Spatio-temporal (ST) graphs have been used in many application domains to model evolving ST phenomenon. Such models represent the underlying structure of the phenomenon in terms of its entities and different types of spatial interactions between them. The reason behind using graph-based models to represent ST phenomenon is due to the existing well-established graph analysis tools and algorithms which can be directly applied to analyze the phenomenon under consideration. In this paper, considering the use case of two distinct, highly dynamic phenomena - invasive team sports, with a focus on handball and urban road traffic, we propose a spatio-temporal graph model applicable to both these phenomena. Different types of entities and spatial relations which make up these phenomena are highlighted to formalize the graph. Furthermore, the idea of graph-based pattern detection in both these phenomena is explored. Different types of ST patterns for both ST phenomena are discussed and the problem of pattern detection is formalized as the problem of subgraph isomorphism for dynamic graphs. Finally, the results of our algorithm to detect random ST patterns in random ST graphs are presented. The ideas discussed in this paper are applicable to other ST phenomena as well.
时空图中的时空模式检测
时空图已经在许多应用领域中用于模拟演化的时空现象。这些模型从实体和它们之间不同类型的空间相互作用方面代表了现象的潜在结构。之所以使用基于图的模型来表示ST现象,是因为现有完善的图分析工具和算法可以直接用于分析所考虑的现象。本文考虑了两种不同的、高度动态的现象——侵入性团队运动的用例,并以手球和城市道路交通为重点,提出了一种适用于这两种现象的时空图模型。不同类型的实体和构成这些现象的空间关系被突出显示,以形式化图形。此外,本文还探讨了这两种现象中基于图的模式检测的思想。讨论了两种ST现象的不同类型的ST模式,并将模式检测问题形式化为动态图的子图同构问题。最后,给出了我们的算法在随机ST图中检测随机ST模式的结果。本文讨论的观点也适用于其他ST现象。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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