Discovery of Spatiotemporal Patterns in Mobile Environment
Lecture notes in computer sciencePublished 14 December 2005
Vu Thi Hong Nhan, Jeong Hee, Keun Ho Ryu
Citations3
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
This study aims to propose algorithms for mining spatiotemporal patterns in mobile environment utilizing two algorithms called All_MOP and Max-MOP, applicable to location-based services such as tourist service, traffic service, and so on.
Abstract
The converge of location-aware devices, GIS functionalities and the increasing accuracy and availability of positioning technologies pave the way to a range of new types of location-based services. The field of spatiotemporal data mining where relationships are defined by spatial and temporal aspect of data is encountering big challenges since the increased search space of knowledge. In this study, we aim to propose algorithms for mining spatiotemporal patterns in mobile environment. Moving patterns are generated utilizing two algorithms called All_MOP and Max_MOP. The first one mines all frequent patterns and the other discovers only maximal frequent patterns. Our approach is applicable to location-based services such as tourist service, traffic service, and so on.
Keywords
Computer Science
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