Publication Date

Spring 6-14-2016

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

T. Y. Lin

Second Advisor

Jon Pearce

Third Advisor

James Casaletto

Abstract

This paper presents an efficient algorithm to extract knowledge from high-dimensionality, high- complexity datasets using algebraic topology, namely simplicial complexes. Based on concept of isomorphism of relations, our method turn a relational table into a geometric object (a simplicial complex is a polyhedron). So, conceptually association rule searching is turned into a geometric traversal problem. By leveraging on the core concepts behind Simplicial Complex, we use a new technique (in computer science) that improves the performance over existing methods and uses far less memory. It was designed and developed with a strong emphasis on scalability, reliability, and extensibility. This paper also investigate the possibility of Hadoop integration and the challenges that come with the framework.

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