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In recent years, public health surveillance has become a priority, driven by concerns of possible bioterrorist attacks and disease outbreaks. Authorities argue that syndromic surveillance, or the monitoring of prediagnostic health-related data for early detection of nascent outbreaks, is crucial to preventing massive illness and death. Syndromic surveillance could prevent widespread illness and death, but public-health analysts face many technical barriers. To meet syndromic surveillance's complex operational and research needs, and as part of DARPA'S national biosurveillance technology program, we've developed BioSTORM (the biological spatio-temporal outbreak reasoning module). BioSTORM is an experimental end-to-end computational framework that integrates disparate data sources and deploys various analytic problem solvers to support public health analysts in interpreting surveillance data and identifying disease outbreaks. BioSTORM can help them by supporting ontology-based data integration and problem-solver deployment.