CAREER: Application-Oriented Large-Scale Parallel Data Mining

这个项目的目标是开发一个大规模的并行数据挖掘系统（PDMS），它可以操作非常大的科学数据库。该研究采用以应用为导向的方法，从三个科学领域：生物信息学（蛋白质结构预测）、天文学（稀有物体识别）和材料信息学（“虚拟”材料设计）。有两个相互冲突的目标必须得到满足：通用性和专用性。PDMS工具包必须是通用的，因为它可以支持一系列常见的数据挖掘任务，如关联、序列、分类和聚类，但要可用，它必须支持特异性或特定于领域的定制。PDMS系统基于一种新颖的三层架构，包括前端接口和查询工具，中间层是常见的高级挖掘算法，后端系统是一组核心的数据挖掘“原始操作”，与数据库系统紧密集成，并提供峰值并行或分布式性能。面向应用的方法为推进跨学科教育工作提供了极好的机会，并鼓励这些领域的思想和算法的交叉施肥。开设大型数据挖掘系统的设计以及数据挖掘在科学领域的应用等新课程。该项目的成果将有助于开发更多通用数据挖掘工具的研究，这些工具能够在知识发现过程的所有阶段利用高性能并行和分布式技术，并为生物信息学、天文学和材料科学等重要科学应用开发定制工具。

The goal of this project is to develop a large-scale parallel data mining system (PDMS), which can manipulate very large scientific databases. The research pursues an application-oriented approach with input from three scientific domains: bioinformatics (protein structure prediction), astronomy (rare object identification) and materials informatics ("virtual" material design). There are two conflicting objectives that must be satisfied: genericity and specificity. The PDMS toolkit must be generic in that it can support a range of common data mining tasks such as associations, sequences, classification and clustering, yet to be usable it must support specificity or domain-specific customization. The PDMS system is based on a novel three-tiered architecture consisting of a front-end interface and query tool, a middle layer of common high-level mining algorithms, and a back-end system consisting of a core set of data mining "primitive operations", tightly integrated with a database system, and delivering peak parallel or distributed performance. The application-oriented approach produces excellent opportunities to advance inter-disciplinary educational efforts, and encourages the cross-fertilization of ideas and algorithms across these areas. New courses will be offered on the design of large scale data mining systems as well as applications of data mining in scientific domains. The results of this project will aid research in developing more generic data mining tools that are able to leverage high performance parallel and distributed techniques in all the phases of the knowledge discovery process, and in developing customized tools for important scientific applications like bioinformatics, astronomy and materials science.