Knowledge discovery from transportation network data

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Author

Christopher Clifton

Tech report number

CERIAS TR 2005-132

Entry type

conference

Abstract

ransportation and logistics are a major sector of the economy, however data analysis in this domain has remained largely in the province of optimization. The potential of data mining and knowledge discovery techniques is largely untapped. Transportation networks are naturally represented as graphs. This paper explores the problems in mining of transportation network graphs: we hope to find how current techniques both succeed and fail on this problem, and from the failures, we hope to present new challenges for data mining. Experimental results from applying both existing graph mining and conventional data mining techniques to real transportation network data are provided, including new approaches to making these techniques applicable to the problems. Reasons why these techniques are not appropriate are discussed. We also suggest several challenging problems to precipitate research and galvanize future work in this area.

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Date

2005 – 04

URL

http://ieeexplore.ieee.org/sea ... pper.jsp?arnumber=1410216

Address

Tokyo, Japan

Key alpha

Clifton

Note

The 21st International Conference on Data Engineering (ICDE 2005) April 5-8, 2005 in Tokyo, Japan

Publication Date

2005-04-01

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@Conference{ Clifton,
	title = "Knowledge discovery from transportation network data",
	author = "Christopher Clifton",
	year = "2005",
	month = "04",
	address = "Tokyo, Japan",
	note = "The 21st International Conference on Data Engineering (ICDE 2005)
April 5-8, 2005 in Tokyo, Japan",
	abstract = "ransportation and logistics are a major sector of the economy, however data analysis in this domain has remained largely in the province of optimization. The potential of data mining and knowledge discovery techniques is largely untapped. Transportation networks are naturally represented as graphs. This paper explores the problems in mining of transportation network graphs: we hope to find how current techniques both succeed and fail on this problem, and from the failures, we hope to present new challenges for data mining. Experimental results from applying both existing graph mining and conventional data mining techniques to real transportation network data are provided, including new approaches to making these techniques applicable to the problems. Reasons why these techniques are not appropriate are discussed. We also suggest several challenging problems to precipitate research and galvanize future work in this area.",
	url = "http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=1410216",
}