Biblio

Business process mining of a large-scale project has many benefits such as finding vulnerabilities, improving processes, collecting data for data science, generating more clear and simple representation, etc. The general way of process mining is to turn event data such as application logs into insights and actions. Observing logs broad enough to depict the whole business logic scenario of a large project can become very costly due to difficult environment setup, unavailability of users, presence of not reachable or hardly reachable log statements, etc. Using static source code analysis to extract logs and arranging them perfect runtime execution order is a potential way to solve the problem and reduce the business process mining operation cost.

Contemporary enterprise systems focus primarily on performance and development/maintenance costs. Dealing with cyber-threats and system compromise is relegated to good coding (i.e., defensive programming) and secure environment (e.g., patched OS, firewalls, etc.). This approach, while a necessary start, is not sufficient. Such security relies on no missteps, and compromise only need a single flaw; consequently, we must design for compromise and mitigate its impact. One approach is to utilize fine-grained modularization and isolation. In such a system, decomposition ensures that compromise of a single module presents limited and known risk to data/resource theft and denial. We propose mechanisms for automating such modular composition and consider its system performance impact.