2015 AssemblerEfficientDiscoveryofSp

• (Zhang et al., 2015) ⇒ Chao Zhang, Yu Zheng, Xiuli Ma, and Jiawei Han. (2015). “Assembler: Efficient Discovery of Spatial Co-evolving Patterns in Massive Geo-sensory Data.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783394

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Cited By

• http://scholar.google.com/scholar?q=%222015%22+Assembler%3A+Efficient+Discovery+of+Spatial+Co-evolving+Patterns+in+Massive+Geo-sensory+Data
• http://dl.acm.org/citation.cfm?id=2783258.2783394&preflayout=flat#citedby

Quotes

Author Keywords

• Co-evolving pattern; data mining; sensor network; spatiotemporal data

Abstract

Recent years have witnessed the wide proliferation of geo-sensory applications wherein a bundle of sensors are deployed at different locations to cooperatively monitor the target condition. Given massive geo-sensory data, we study the problem of mining spatial co-evolving patterns (SCPs), i.e., groups of sensors that are spatially correlated and co-evolve frequently in their readings. SCP mining is of great importance to various real-world applications, yet it is challenging because (1) the truly interesting evolutions are often flooded by numerous trivial fluctuations in the geo-sensory time series; and (2) the pattern search space is extremely large due to the spatio-temporal combinatorial nature of SCP. In this paper, we propose a two-stage method called Assember. In the first stage, Assember filters trivial fluctuations using wavelet transform and detects frequent evolutions for individual sensors via a segment-and-group approach. In the second stage, Assember generates SCPs by assembling the frequent evolutions of individual sensors. Leveraging the spatial constraint, it conceptually organizes all the SCPs into a novel structure called the SCP search tree, which facilitates the effective pruning of the search space to generate SCPs efficiently. Our experiments on both real and synthetic data sets show that Assember is effective, efficient, and scalable.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 AssemblerEfficientDiscoveryofSp	Yu Zheng Chao Zhang Xiuli Ma Jiawei Han			Assembler: Efficient Discovery of Spatial Co-evolving Patterns in Massive Geo-sensory Data				10.1145/2783258.2783394		2015