Conformance Checking Based on Multi-Perspective Declarative Process Models (1503.04957v1)

Abstract: Process mining is a family of techniques that aim at analyzing business process execution data recorded in event logs. Conformance checking is a branch of this discipline embracing approaches for verifying whether the behavior of a process, as recorded in a log, is in line with some expected behaviors provided in the form of a process model. The majority of these approaches require the input process model to be procedural (e.g., a Petri net). However, in turbulent environments, characterized by high variability, the process behavior is less stable and predictable. In these environments, procedural process models are less suitable to describe a business process. Declarative specifications, working in an open world assumption, allow the modeler to express several possible execution paths as a compact set of constraints. Any process execution that does not contradict these constraints is allowed. One of the open challenges in the context of conformance checking with declarative models is the capability of supporting multi-perspective specifications. In this paper, we close this gap by providing a framework for conformance checking based on MP-Declare, a multi-perspective version of the declarative process modeling language Declare. The approach has been implemented in the process mining tool ProM and has been experimented in three real life case studies.

• The paper introduces MP-Declare, an extension of declarative process modeling, and a conformance checking framework using MFOTL to incorporate control-flow, data, and temporal perspectives.
• Experimental validation shows the framework scales linearly with trace, constraint, and event numbers, successfully processing logs up to 5 million events and three real-life cases with reasonable average execution times.
• This multi-perspective approach enhances model expressivity for complex environments and offers organizations a practical diagnostic tool for improved business compliance and potential real-time process monitoring.

Conformance Checking with Multi-Perspective Declarative Process Models

The paper discusses advanced methodologies for conformance checking within the field of process mining, incorporating multi-perspective declarative process models. The core of the research revolves around MP-Declare, an extension of the declarative process modeling language Declare, which allows analysts to specify constraints that encompass control-flow, data dependencies, and temporal considerations.

Overview and Methodology

Process mining techniques, traditionally reliant on procedural models, often struggle in environments characterized by high variability due to their rigidity. Declarative models, by contrast, offer a more flexible alternative, accommodating a wide range of behaviors through a compact set of constraints. MP-Declare further enhances this flexibility by integrating multiple perspectives, enabling the definition of constraints across data and temporal dimensions.

The paper outlines a conformance checking framework for declarative models that utilizes Metric First-Order Linear Temporal Logic (MFOTL) to specify multi-perspective constraints. This logic facilitates the temporal and data perspective in the formalization, allowing for a granular approach to model compliance rooted in logic.

Numerical Results and Experimental Validation

The algorithmic implementation demonstrates scalability, successfully processing artificial logs containing up to 5 million events and three real-life case studies with intricate business processes. The framework's computational complexity is linear concerning the number of traces, constraints, and events within each trace. Average execution times across various conditions remain reasonable, illustrating the framework's potential deployment in practical scenarios.

Implications and Future Directions

Introducing multi-perspective specifications implicates significant theoretical and practical advancements. Theoretically, the synthesis of MFOTL with process mining enriches the model's expressivity, accommodating complex environments more effectively than procedural defaults. Practically, organizations may deploy MP-Declare to achieve enhanced business compliance, potentially utilizing conformance checking as a diagnostic tool for detecting inefficiencies.

The paper suggests further developments in applying these methodologies within real-time monitoring systems to identify process deviations instantaneously, providing a proactive rather than retrospective view. This ongoing evolution could transform organizational process management, opening pathways for immediate corrective action in dynamic business contexts.

In summary, this paper provides a robust analytical framework for conformance checking using multi-perspective declarative models, demonstrating significant applicability in both research and industry-related endeavors. Its contributions mark an important step towards accommodating complex, variable environments characteristic of contemporary business processes.