Digital data can occur in diverse forms; it may occur as database records with numerical fields, as raw text documents or image files, or as website traffic log files. Data mining is the automatic discovery of interesting patterns, associations,changes, anomalies, rules, and statistically significant structures and events in data.

A key feature, often an overwhelming feature, of the data is its sheer magnitude. The rapidly expanding internet already contains more than 1 billion web pages, and typical warehouse and web traffic data can occupy terabytes of disk space. It is clear that data mining tools must be efficient and scalable if they are to serve any practical purpose. Parallel computing can help in satisfying the demands on computing cycles and memory storage imposed by these large data sets.

The main focus of this project is to develop scalable solutions for large-scale data analysis. The main thrust is in exploring and developing efficient, parallel, mathematical and statistical methods that can mine large data sets and deliver results in a timely manner. In particular, new clustering techniques that partition data into disjoint partitions, the new method of concept decompositions for dimensionality reduction, improved computation of principal components analysis, efficient classification schemes for folding in newly arriving unlabeled data into known classes, and effective visualization of multidimensional data will be investigated.

Another focus is to adapt the data analyses tools developed to the application area of text mining. A completely parallel text mining system that is capable of (a) efficient preprocessing of text data into numerical data, (b) clustering large unlabeled document collections, (c) classifying unlabeled documents into a known concept hierarchy and (d) visualization of document & word relationships will be built. This system will allow the user to easily navigate, assimilate, search and organize the contents of very large document collections; we hope to process up to 100 million documents on a 128-processor cluster of workstations. Many of the text mining algorithms we develop will scale linearly with the size of the data. In this scenario, it becomes important to avoid I/O bottlenecks, exploit memory hierarchies of modern processors and hide network latencies.

The educational plan consists of three components: (i) a teaching philosophy that emphasizes the scientific method in undergraduate and graduate education, by incorporating new technologies for in-class and web-based offline instruction; (ii) a focus on multidisciplinary education with a commitment to develop centralized web-oriented primers designed to quickly acquaint students with desired pre-requisites; and (iii) curriculum development for two courses; the first, a scientific computing course for non-CS undergraduates as part of UT Austin's new "Elements of Computing" program, and the second, a new course on large-scale data mining for graduate students.

National Science Foundation (NSF)
Division of Computer and Communication Foundations (CCF)
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Almadena Y. Chtchelkanova
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University of Texas Austin
United States
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