The Data-Theoretic methodology leverages expert opinion and theory-building techniques to guide data analytics algorithms for monitoring unstructured and heterogeneous organizational communications.
Find Out MoreThe Data-Theoretic is developed by the Socio-Technical Risk Analysis (SoTeRiA) Laboratory, Directed by Professor Zahra Mohagehgh, the Principal Investigator of the National Science Foundation (NSF) grant supporting this project (Grant No.SES/SBE-1535167). The Data-Theoretic (DT) approach has two primary parts: DT-BASE and DT-SITE. DT-BASE provides the theoretical causal modeling structure for guiding and training DT-SITE – which performs site-specific and automated data mining. In other words, DT-BASE is used to construct the causal models and to develop a preliminary and generic quantification for the causal models, and DT-SITE, a data-driven approach, is used to extract observable evidence from site-specific operational data to update the base causal model from DT-BASE. In the NSF sponsored research, DT-BASE and DT-SITE are integrated and updated using Bayesian approaches to provide the quantitative result for Probabilistic Risk Assessment (PRA).
Get Started!DT-BASE is a computational theory-building framework and an analyst interpretation toolkit for developing causal models that can be theoretically validated based on existing literature. DT-BASE guides the user/analyst through a subjective interpretation of literature/evidence to establish a baseline quantification. DT-BASE is a scientific social networking tool for collaborative model building, where collaborators can build and share theoretical models.
LAUNCH DT-BASEThe Data-Theoretic is developed by the Socio-Technical Risk Analysis (SoTeRiA) Laboratory, Directed by Professor Zahra Mohagehgh, the Principal Investigator of the National Science Foundation (NSF) grant supporting this project (Grant No. SES/SBE-1535167). The SoTeRiA Laboratory is advancing technologies to support Risk Analysis in diverse industries.
SoTeRiA REASEARCHSend us an email to learn more about Risk Analysis research and education at the University of Illinois!
This material is based in part upon work supported by the National Science Foundation under Grant No. SES/SBE-1535167. Any opinions findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
