Persistent Homology on Streaming Data
Summary
An approach for applying persistent homology techniques to continuously generated data streams through topological summaries and incremental processing.
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 methods for adapting persistent homology to environments where data arrives continuously rather than as a fixed dataset. The proposed framework enables topological analysis in dynamic systems and streaming applications.
The work helped establish foundational techniques for applying topological methods to operational systems, monitoring applications, and real-time analytical environments.
Citation
Moitra, A., Malott, N.O., Wilsey, P.A. Persistent Homology on Streaming Data. ICDMW 2020. DOI: 10.1109/ICDMW51313.2020.00090