|
RESEARCH SPONSORS
- 2000-2003, National Science Foundation, Advanced Algorithms for Spatial-Temporal Interactions in Distributed GIS,
NSF Grant No. IIS-0081434
Abstract: This research focuses on the analysis of spatial interactions in distributed GIS environments. Data related to systems of spatial interactions
includes spatial flows and locational and attribute data pertaining to origins and destinations. Given that these datasets are distributed across
multiple nodes of a computer network, this research aims to: 1) study mechanisms of data partitioning and develop a metadata structure that
describes the partition; 2) develop decomposable algorithms for gravity modeling that minimize communication cost; and 3) develop
efficient algorithms for learning and classifying flow patterns using distributed data sources. The approach is to let the databases reside at
their native sites. The algorithms dynamically decompose themselves into partial computations that are executed at individual database sites,
and local results are composed to obtain the same global results that would have been obtained if the databases had been moved to a common
site. The algorithms can find the decompositions to match the distribution of data across the network. This research will have significant
impact on many problems that need to process distributed data. For example, it can enable GIS systems to analyze spatial-temporal datasets
distributed over the Internet without having to move the databases to a common site.
- 1999-2000, Defense Advanced Research Projects Agency (sub-contract through New York University), Smart Parallel Search Extension for MSTAR
- 1998-2001, National Science Foundation, Acquisition of a Research Network for Distributed Computing, NSF Grant No. EIA-9871345
Abstract: The Department of Electrical and Computer Engineering at the University of Cincinnati, in cooperation with the College of
Engineering and the State of Ohio, proposes to acquire a high-speed research network of computer workstations. The testbed will be used to
support the needs of a variety for distributed computing systems and network protocol research projects. The testbed will include a cluster of
high-performance workstations locally connected by a high-speed LAN, with wide area connectivity provided to the Statewide ATM
research network OCARNet. The projects supported by the proposed network will exploit fundamental interconnections between theory,
systems and applications of distributed computing, and include: validation of routing schemes in distributed computing environments;
optimistic compiler optimizations; distributed decision support and data mining, and development of a collaborative computing environment.
- 1998-2001, Defense Advanced Research Projects Agency (sub-contract through Mission Research Corporation), Unified Design of 1-D Automatic Target Recognition Systems for Surveillance and Attack
- 1998, Air Force Office of Scientific Research, A Study of Intra-Class Variability in the Context of Automatic Target Recognition
- 1993-1996, National Science Foundation, Learning Domain Structure and Reasoning with it in Environments of Uncertain Knowledge, NSF Grant No. IIS-9308868
Abstract: This research addresses the issue of automated hypothesization of situation models that are needed as contexts for reasoning and decision
making. The three different but related aspects that are addressed by this research are: 1. Learning from domain data the qualitative
relationships among domain attribute and their associated probabilistic uncertainty knowledge; 2. Integration of uncertainty knowledge and
other known causal qualitative relationships of the domain into a single graphical knowledge structure; and 3. Hypothesization of small and
interesting situation models from the intermediate representation of domain knowledge acquired in the preceding step. The learning of
qualitative relationships from databases is extended to include acquisition of temporal dependencies among attributes, and also to the cases
when the available data is in the form of multi- databases. This learning method is also sought to be applied to the problem of incomplete
databases. The knowledge based dynamic hypothesization of situation models is sought to be extended to apply to path planning and temporal
modeling problems. The issue of specifying a situation model at varying levels of precision and certainty are also examined.
Return to MILAB Home
|