This project explores algorithmic techniques for archival, indexing and reuse of complex knowledge. Hierarchical graphs have emerged as a powerful knowledge representation structures, however existing database and data mining techniques are not able to manage the extreme combinatorial complexities required to compare, classify, index and cluster these structures as primitive data elements. Research challenges include (1) capturing the inner structure of graph based models, and their topological sub-structures, to use for pattern matching purposes; (2) classify the stability of these approximation techniques in the presence of noise; (3) identifying how to translate these techniques into suitable database mechanisms. The basis of the approach is a mapping of the topological structure of a graph into a low-dimensional vector space through an eigenvalue characterization. This SGER studies these problems in the context of collaborative and distributed engineering design, where teams of agents (human and computational) interact over the network to realize a product (e.g., software, electro-mechanical device, building, etc.). This process is modeled as a directed acyclic graph (DAG) that encodes the design modeling operations and decisions, as well as the flow of the information along the modeling time-steps. Knowledge acquisition agents will be fielded to capture these knowledge structures and a validation of the theoretical methodology for indexing and reuse of process knowledge will performed. Some of this work will be done in collaboration with Bentley Systems. If successful, these techniques will lead to dramatic new possibilities for database and information management systems: allowing them to efficiently store complex graphs as single large objects and identify useful patterns in and across large sets of these combinatorial structures.
