19th International CODATA Conference
Category: Knowledge Discovery
Information Integration through Events
Kate
Beard (beard@spatial.maine.edu),
Professor and Chair, Department of Spatial Information Science and Engineering,
University of Maine, USA
Neal Pettigrew (nealp@maine.edu), Associate
Professor and GoMOOS Chief Scientist, School of Marine Science, University of
Maine, USA
Large amounts of environmental data are now being routinely collected and made
available, often on-line. A substantial proportion of such collections can be
regarded as spatio-temporal data that include recorded dates, times, and locations
of observations on environmental variables. We can expect an increasing number
of shared environmental data collections and a growing number of researchers
interested in combining such data to interpret ecological phenomena and their
interactions. In such a context there is a growing need for exploratory and
analytical tools for investigating diverse collections of environmental data
covering a range of spatial and temporal regimes. There are at least two significant
challenges in this analytical context. One challenge is the extreme heterogeneity
among data collections. Environmental data are collected by a wide range
of sensors and measurement protocols and thus differ with respect to measurement
units and scales, spatial, temporal and thematic resolution, and formats and
media as well as quality. The extreme heterogeneity of these data sets has tended
to limit exploration and analysis to within individual observation data streams
rather than across data streams and thus restricted opportunities for developing
a systems level perspective on ecosystems.
The second challenge is the lack of tools for exploring and analyzing combined
spatial and temporal dimensions. Current information systems tend to handle
one or the other dimensions but not both. Geographic information systems address
spatial pattern analysis but the full potential of spatio-temporal analysis
is limited by inadequate representation of the temporal dynamics.
This paper proposes an approach to overcoming environmental data heterogeneity
by creation of a common data type: a spatial temporal event and presents a graphical
exploratory framework for investigating the spatial temporal behaviors of such
events. The approach to event detection in sensor data streams is outlined using