TigerGraph Process Mining

Welcome to the TigerGraph Process Mining website.

Graph databases are an excellent fit for process mining applications because they are designed to handle highly interconnected data, making it easy to model and analyze complex business processes. Unlike traditional relational databases, which can struggle with the intricate relationships and dependencies inherent in process data, graph databases like TigerGraph excel at traversing relationships quickly and efficiently. This allows for real-time analysis of process flows, enabling organizations to identify bottlenecks, deviations, and optimization opportunities with greater precision.

The ability to easily scale and handle large volumes of event data further enhances the suitability of graph databases for process mining, providing a powerful tool for uncovering actionable insights in even the most complex process landscapes.

Predicting Workflow Times

Machine learning, particularly when combined with Graph Neural Networks (GNNs), can significantly enhance process mining by predicting the time required to complete a business process. GNNs are specifically designed to work with graph-structured data, making them ideal for capturing the dependencies and sequential patterns within complex processes. By training GNN models on historical process data, organizations can accurately forecast the duration of future processes based on their current state and past performance. This predictive capability allows businesses to proactively manage their workflows, optimize resource allocation, and anticipate potential delays, leading to more efficient and reliable operations.