Rethinking Conformance Checking: Speed and Precision with LP

Conformance checking is a important technique for ensuring that observed processes align with normative models. Traditionally, this has relied on an A*-based heuristic search. However, anyone who's applied it knows it can bog down with lengthy traces or significant deviations. But what if there's a better way?

Enter Linear Programming

The recent development in conformance checking reimagines the task using a totally unimodular linear program (LP). By redefining the problem on the reachability graph of a synchronous product, this approach exploits the network-flow structure. It effectively sidesteps the computational quagmire of integer variables and branch-and-bound searches. This isn't just theoretical, LP relaxation guarantees the existence of an integral optimal solution.

Real-World Performance

Now, let's talk numbers. An extensive empirical evaluation covered over 2.1 million instances from both real-world and synthetic benchmarks. The A*-based method still shines for short, well-conforming traces. Yet, it's the LP formulation that truly excels in longer traces with deviations, the scenarios where conformance checking holds the most value.

Color me skeptical, but can we really say one method is universally better? The findings suggest a more nuanced view. When both A* and LP approaches are combined, simple algorithm-selection guidelines lead to an average runtime saving of 38.6%. Notably, the selection accuracy reaches 96% compared to always opting for A*. That's not just impressive, it's transformative.

Why This Matters

Why should anyone care about this? Because efficiency in conformance checking isn't just a technical nicety, it's a competitive advantage. Faster, more accurate processes mean better decision-making and less time wasted in debugging mismatches. Imagine the implications for industries reliant on complex process models like manufacturing or finance.

So, the next time you're facing a conformance checking problem, ask yourself: Do you trust the old guard of A* uncritically, or are you ready to embrace a methodology that, when applied wisely, offers a genuine leap forward?