CAREER: A Scalable Framework for Mining Scientific and Biomedical Data

该项目涉及开发一个可扩展的框架，用于挖掘大型、动态、生物医学和科学数据集。这个项目有两个研究目标。第一个研究目标是开发新的方法来准确地模拟嵌入在这些数据中的相关空间或结构关系，特别是使用基于图的方法来模拟结构和几何正交多项式来模拟形状。第二个研究目标涉及并行和增量算法的发展，结合新的集群文件系统支持，有效和高效地挖掘这些数据。这项工作的一个关键特点是使用现实生活中的大规模数据集作为实验平台，具体来说，通过分子动力学模拟产生的数据来研究材料缺陷的演变，生物分子结构数据来研究结构-活性关系，以及临床眼病数据来研究锥角膜和青光眼疾病模式的发生和进展。这个项目的教育部分寻求在俄亥俄州立大学培养和促进一个新的跨学科的研究生和本科生数据挖掘课程。通过设计合适的跨学科大型探索性数据挖掘课程项目，采用共同学习这一新颖的方法，使跨学科的学生可以相互学习并利用彼此的优势。该项目将对如何有效地探索和分析大型生物医学和科学数据集产生重大影响，并将使科学家和临床医生能够有效地了解所涉及的潜在科学过程，从而扩展这些领域的最新技术。

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.