Computer Science Subproject

From Minas

Team

• Paulo Pinheiro da Silva (Lead, CS)
• Vladik Kreinovich (CS)
• Ann Gates (CS)

Goal

To gain users’ confidence in workflow execution results by enhancing results with provenance information, trust recommendations, and levels of uncertainty.

Summary

Provenance, trust, and uncertainty about the results of cyber-infrastructure-based applications, e.g., scientific workflows, are essential if scientists are to believe and, thus accept, these results. This subproject will address the complex problem of using provenance as the key enabler for integrating trust management and uncertainty management in distributed environments like the grid. The subproject will support research leading to a uniform way of representing uncertainty and trust. Uncertainty models embedded in provenance will be comprehensive enough to support the computation of several dimensions of uncertainty including error, accuracy, and reliability. Trust models embedded in provenance will be rich enough to support the computation of trust recommendations that can describe several aspects of trust including distrust, partial trust, ignorance, and inconsistencies.