CAREER: A Scalable Framework for Mining Scientific and Biomedical Data

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This project involves the development of a scalable framework for mining large, dynamic, biomedical and scientific datasets. This project has two research goals. The first research goal involves the development of novel methods to accurately model the relevant spatial or structural relationships embedded in such data, in particular the use of graph-based methods to model structure and geometric orthogonal polynomials to model shape. The second research goal involves the development of parallel and incremental algorithms, in conjunction with novel cluster file system support, to effectively and efficiently mine such data. A key feature of this work is the use of real-life large scale datasets as testbeds, specifically, data produced by molecular dynamics simulations to study the evolution of defects in materials, bio-molecular structure data to study structure-activity relationships, and clinical eye disease data to study the onset and progression of Keratoconus and Glaucoma disease patterns. The educational component of this project seeks to foster, and promote a new inter-disciplinary graduate and undergraduate curriculum in data mining at the Ohio State University. Co-learning, a novel method by which students across disciplines can learn from one another and leverage each other's strengths, will be employed through the design of suitable inter-disciplinary large-scale exploratory data mining class projects. This project will have a significant impact on how large biomedical and scientific datasets are efficiently explored and analyzed, and will enable scientists and clinicians to gain an effective understanding of the underlying scientific process involved, thereby extending the state-of-the-art in these domains.