SBIR-STTR Award

We propose assess the feasibility of using homological signatures as a search and organizational tool for interacting with relational databases. Specifically, they will be applied to outputs of the Mapper methodology applied to tables in a database, so as to identify scale choices which produce informative outputs. These outputs will then be used to understand the tables from a scientific point of view, and potentially to partition the tables in the hope of making queries more efficient.

Keywords:
Persistence, Homology, Graphs, Visualization, Data Analysis, Computational Topology

Ayasdi, a leader in the new field of Topological Data Analysis, proposes to extend the previous effort in utilizing persistent homology in exploring data fusion. As part of this work, we propose both to deploy existing propriety Ayasdi technology and to develop new analytical approaches to interpret and provide users with a deeper understanding of disparate data sources. This work will include a development of persistent tools for fusing multiple data streams. In particular, we intend to explore two use cases: 1) Looking at multiple modalities of the same source (brain), for example a. physiological (electric impulses, etc.) b. genetic c. anatomical 2) Looking at the same modality of different data sources (brains). The final demonstration would include an automated analysis strategy for topological networks representing multi-modal data, employing persistence in identifying the networks that best characterize the underlying geometry of the data.