Piecewise Computation of Persistent Homology
Summary
A scalable framework for decomposing persistent homology computations into smaller components that can be processed more efficiently.
Research Context
This publication reflects graduate research conducted at the University of Cincinnati College of Engineering and Applied Science. It is included here to document the technical foundation behind Convergent Analytics' work in industrial analytics, applied AI, high-performance computing, and topological data analysis.
This peer-reviewed conference publication was produced as part of graduate research conducted at the University of Cincinnati College of Engineering and Applied Science.
The paper explores piecewise decomposition strategies for persistent homology computation, enabling large topological analysis problems to be partitioned into more manageable computational tasks.
The approach advances ongoing efforts to improve scalability and practical deployment of topological methods for increasingly large and complex datasets.
Citation
Singh, R.P., Malott, N.O., Wilsey, P.A. Piecewise Computation of Persistent Homology. IEEE Big Data 2024.