Archived Talks

University of Pittsburgh Department of Biomedical Informatics Lecture Series

“AMIA Presentations”
Speakers: Ming-Chi Tsai and Xia Jiang

Friday, November 2, 2007
11:00 AM – 12:00 NOON
Room M-184 VALE [?]
200 Meyran Avenue

An Evaluation of Biosurveillance GridDynamic Algorithm Distribution Across Multiple Computer Nodes

Ming-Chi Tsai, MS
Doctoral Student, Carnegie Mellon University

Abstract: Performing fast data analysis to detect disease outbreaks plays a critical role in real-time biosurveillance. In this paper, we described and evaluated an Algorithm Distribution Manager Service (ADMS) based on grid technologies, which dynamically partition and distribute detection algorithms across multiple computers. We compared the execution time to perform the analysis on a single computer and on a grid network (3 computing nodes) with and without using dynamic algorithm distribution. We found that algorithms with long runtime completed approximately three times earlier in distributed environment than in a single computer while short runtime algorithms performed worse in distributed environment. A dynamic algorithm distribution approach also performed better than static algorithm distribution approach. This pilot study shows a great potential to reduce lengthy analysis time through dynamic algorithm partitioning and parallel processing, and provides the opportunity of distributing algorithms from a client to remote computers in a grid network.

A Recursive Algorithm for Spatial Cluster Detection

Xia Jiang, MS
Doctoral Candidate, University of Pittsburgh

Abstract: Spatial cluster detection involves finding spatial subregions of some larger region where clusters of some event are occurring. For example, in the case of disease outbreak detection, we want to find clusters of disease cases so as to pinpoint where the outbreak is occurring. When doing spatial cluster detection, we must first articulate the subregions of the region being analyzed. A simple approach is to represent the entire region by an ηχη grid. Then we let every subset of cells in the grid represent a subregion. With this representation, the number of subregions is equal to ₂n²-1. If is not small, it is intractable to check every subregion. The time complexity of checking all the subregions that are rectangles is θ(n⁴). Neill et al. performed Bayesian spatial cluster detection by only checking every rectangle. In the current paper, we develop a recursive algorithm which searches a richer set of subregions. We provide results of simulation experiments evaluating the detection power and accuracy of the algorithm.

For more information: jxc3@pitt.edu or 412.647.7113