As event sequence data grow in prominence, sequential pattern mining algorithms have been widely adopted to discover interesting patterns in data. For instance, e-commerce websites use these algorithms large-scale clickstream data to understand the common paths taken by customers. In the healthcare domain, sequential pattern mining algorithms open the door to investigating the sheer volume of patients in a hospital. In this project, we develop a technique that supports analysts' iterative workflow during data exploration while utilizing pattern mining algorithms' capability to extract potentially interesting patterns from large-scale data.
The Computational Enterprise Science Lab focuses on the design, analysis, and management of complex enterprise systems (e.g. organizations, supply chains, business ecosystems) using information visualization, modeling/simulation, and system science approaches.
