2010 TropicalCycloneEventSequenceSim

• (Ho et al., 2010) ⇒ Shen-Shyang Ho, Wenqing Tang, and W. Timothy Liu. (2010). “Tropical Cyclone Event Sequence Similarity Search via Dimensionality Reduction and Metric Learning.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835824

Subject Headings:

Notes

• Categories and Subject Descriptors: I.2.6 Computing Methodologies: Artificial Intelligence — Learning: Parameter Learning; I.5.4 Computing Methodologies: Pattern Recognition — Applications; J.2 Computer Applications: Physical Sciences and Engineering — Earth and atmospheric sciences.
• General Terms: Algorithm, Design

Cited By

• http://scholar.google.com/scholar?q=%22Tropical+cyclone+event+sequence+similarity+search+via+dimensionality+reduction+and+metric+learning%22+2010
• http://portal.acm.org/citation.cfm?id=1835824&preflayout=flat#citedby

Quotes

Author Keywords

Metric learning, parameter learning, spatio-temporal data mining, mining multi-dimensional data sequences, similarity search, ensemble method, dimensionality reduction, embedding method, atmospheric events, tropical cyclones.

Abstract

The Earth Observing System Data and Information System (EOSDIS) is a comprehensive data and information system which archives, manages, and distributes Earth science data from the EOS spacecrafts. One non-existent capability in the EOSDIS is the retrieval of satellite sensor data based on weather events (such as tropical cyclones) similarity query output.

In this paper, we propose a propose a framework to solve the similarity search problem given user-defined instance-level constraints for tropical cyclone events, represented by arbitrary length multidimensional spatio-temporal data sequences. A critical component for such a problem is the similarity/metric function to compare the data sequences. We describe a novel Longest Common Subsequence (LCSS) parameter learning approach driven by nonlinear dimensionality reduction and distance metric learning. Intuitively, arbitrary length multidimensional data sequences are projected into a fixed dimensional manifold for LCSS parameter learning. Similarity search is achieved through consensus among the (similar) instance-level constraints based on ranking orders computed using the LCSS-based similarity measure.

 Experimental results using a combination of synthetic and real tropical cyclone event data sequences are presented to demonstrate the feasibility of our parameter learning approach and its robustness to variability in the instance constraints. We, then, use a similarity query example on real tropical cyclone event data sequences from 2000 to 2008 to discuss (i) a problem of scientific interest, and (ii) challenges and issues related to the weather event similarity search problem.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 TropicalCycloneEventSequenceSim	Shen-Shyang Ho Wenqing Tang W. Timothy Liu			Tropical Cyclone Event Sequence Similarity Search via Dimensionality Reduction and Metric Learning		KDD-2010 Proceedings		10.1145/1835804.1835824		2010