TITLE:
A Unified Approach to Spatial Outliers with
Applications to Traffic Data Analysis
PRESENTER:
Shashi Shekhar
AFFILIATION:
Computer Sc. Dept., CTS and AHPCRC, Univ. of Minnesota.
SLIDES:
graph-based outliers (pdf)
,
spatial outliers (pdf)
TECHNICAL ABSTRACT:
Spatial outliers represent locations which are significantly different
from their neighborhoods even though they may not be significantly
different from the entire population.
Identification of spatial
outliers can lead to the discovery of
unexpected, interesting, and implicit knowledge,
such as local instability.
In this paper, we first provide a general definition
of S-outliers for spatial outliers. This definition subsumes the
traditional definitions of spatial outliers.
Second, we characterize the computation
structure of spatial outlier detection methods and
present scalable algorithms.
Third, we provide a cost model of
the proposed algorithms.
Finally, we experimentally evaluate
our algorithms using a large realworld dataset
representing the traffic measurements from the highway networks
in Twincities (Minneapolis-St. Paul). Interesting spatial outliers detected
in this dataset include bad sensors, and missing data.
KEYWORDS:
Storage Outliers, Spatial Databases, Traffic Measurement,
Intelligent Transportation Systems.
NOTE:
Some of the results discussed in this talk appeared in a paper
in ACM SIGKDD Intl Conf. on Data M