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This paper presents BPMN2Constraints, a tool that compiles BPMN diagrams into sets of declarative con-straints that can then, for example, be used for conformance checking. Notably, BPMN2Constraints doesnot rely on Petri net replay for generating the constraints; by generating constraints directly from a con-trol flow graph extracted from the BPMN model, the tool avoids indirection. BPMN2Constraints cangenerate constraints in several languages: DECLARE, finite-trace linear temporal logic, and SIGNAL, aproprietary process querying language.

This paper presents BPMN2Constraints, a tool that compiles BPMN diagrams into sets of declarative con-straints that can then, for example, be used for conformance checking. Notably, BPMN2Constraints doesnot rely on Petri net replay for generating the constraints; by generating constraints directly from a con-trol flow graph extracted from the BPMN model, the tool avoids indirection. BPMN2Constraints cangenerate constraints in several languages: DECLARE, finite-trace linear temporal logic, and SIGNAL, aproprietary process querying language.

This paper presents BPMN2Constraints, a tool that compiles BPMN diagrams into sets of declarative con-straints that can then, for example, be used for conformance checking. Notably, BPMN2Constraints doesnot rely on Petri net replay for generating the constraints; by generating constraints directly from a con-trol flow graph extracted from the BPMN model, the tool avoids indirection. BPMN2Constraints cangenerate constraints in several languages: DECLARE, finite-trace linear temporal logic, and SIGNAL, aproprietary process querying language.

This paper presents BPMN2Constraints, a tool that compiles BPMN diagrams into sets of declarative con-straints that can then, for example, be used for conformance checking. Notably, BPMN2Constraints doesnot rely on Petri net replay for generating the constraints; by generating constraints directly from a con-trol flow graph extracted from the BPMN model, the tool avoids indirection. BPMN2Constraints cangenerate constraints in several languages: DECLARE, finite-trace linear temporal logic, and SIGNAL, aproprietary process querying language.

In complex information systems, it is often crucial to evaluate whether a sequence of activities obtained from a system log complies with behavioural rules. This process of evaluation is called conformance checking, and the most classical approach to specifying the behavioural rules is in the form of flow chartlike process diagrams, e.g., in the Business Process Model and Notation (BPMN) language. Traditionally, control flow constraints are extracted using Petri net replay-based approaches. Though, with the use of industrial process query languages such as Signavio Analytics Language (SIGNAL) that allows for temporal row matching, the possibility of performing conformance checking using temporal constraints opens up. To this end, this thesis presents a parser for extracting control-flow objects from BPMN-based business process models and a compiler for generating both linear temporal logic-like rules as well as SIGNAL queries. The parser succeeds at parsing all industry models and most academic models; the exceptions in the latter case can presumably be traced back to edge cases and unidiomatic modelling. The constraints generated by the compiler are in some, but not in all cases, identical to constraints extracted via Petri net replay as an intermediate step, indicating some differences in the formal interpretation of BPMN control flow. In conclusion, the implementation and evaluation of the parser and compiler indicate that it is feasible to move directly from business user-oriented process models to declarative, query language-based constraints, cutting out the Petri net-replay middleman and hence facilitating elegant and more efficient process data querying.

This paper presents BPMN2Constraints, a tool that compiles BPMN diagrams into sets of declarative con-straints that can then, for example, be used for conformance checking. Notably, BPMN2Constraints doesnot rely on Petri net replay for generating the constraints; by generating constraints directly from a con-trol flow graph extracted from the BPMN model, the tool avoids indirection. BPMN2Constraints cangenerate constraints in several languages: DECLARE, finite-trace linear temporal logic, and SIGNAL, aproprietary process querying language.

In complex information systems, it is often crucial to evaluate whether a sequence of activities obtained from a system log complies with behavioural rules. This process of evaluation is called conformance checking, and the most classical approach to specifying the behavioural rules is in the form of flow chartlike process diagrams, e.g., in the Business Process Model and Notation (BPMN) language. Traditionally, control flow constraints are extracted using Petri net replay-based approaches. Though, with the use of industrial process query languages such as Signavio Analytics Language (SIGNAL) that allows for temporal row matching, the possibility of performing conformance checking using temporal constraints opens up. To this end, this thesis presents a parser for extracting control-flow objects from BPMN-based business process models and a compiler for generating both linear temporal logic-like rules as well as SIGNAL queries. The parser succeeds at parsing all industry models and most academic models; the exceptions in the latter case can presumably be traced back to edge cases and unidiomatic modelling. The constraints generated by the compiler are in some, but not in all cases, identical to constraints extracted via Petri net replay as an intermediate step, indicating some differences in the formal interpretation of BPMN control flow. In conclusion, the implementation and evaluation of the parser and compiler indicate that it is feasible to move directly from business user-oriented process models to declarative, query language-based constraints, cutting out the Petri net-replay middleman and hence facilitating elegant and more efficient process data querying.

Process models constitute valuable artifacts for organizations. A process model formally captures the way an organization works internally and interacts with its customers and partners. Over time, more models may be created as business practices evolve (leading to different versions of models) or an organization expands, e.g., through mergers or acquisitions. It is not uncommon for large organizations to have to manage thousands of process models. Retrieval of process models with desired properties then poses a significant challenge, particularly when one is concerned with finding models that describe certain process scenarios, i.e., sequences of tasks captured in models. This paper proposes a method for automated process model retrieval based on scenario compatibility. A process model is retrieved if it has the potential to perform the specified process scenario. To allow for scenarios to be underspecified, wildcards may be used in their description. The paper reports on a formal language for scenario-based process querying, its implementation, and evaluation in the context of industrial and synthetic process models. The results show that the technique works in (close to) real time.

Highlights - A framework for designing process querying methods is proposed - The framework is positioned for broader Process Analytics and Business Intelligence - The framework is grounded in use cases from the Business Process Management field - The framework is informed by and validated via a systematic literature review - The framework structures the state of the art and points to gaps in existing research Abstract The volume of process-related data is growing rapidly: more and more business operations are being supported and monitored by information systems. Industry 4.0 and the corresponding industrial Internet of Things are about to generate new waves of process-related data, next to the abundance of event data already present in enterprise systems. However, organizations often fail to convert such data into strategic and tactical intelligence. This is due to the lack of dedicated technologies that are tailored to effectively manage the information on processes encoded in process models and process execution records. Process-related information is a core organizational asset which requires dedicated analytics to unlock its full potential. This paper proposes a framework for devising process querying methods, i.e., techniques for the (automated) management of repositories of designed and executed processes, as well as models that describe relationships between processes. The framework is composed of generic components that can be configured to create a range of process querying methods. The motivation for the framework stems from use cases in the field of Business Process Management. The design of the framework is informed by and validated via a systematic literature review. The framework structures the state of the art and points to gaps in existing research. Process querying methods need to address these gaps to better support strategic decision-making and provide the next generation of Business Intelligence platforms.

This paper demonstrates the integration and usage of Process Query Language (PQL), a special-purpose programming language for querying large collections of process models based on process model behavior, in the Apromore open-source process model repository. The resulting environment provides a unique user experience when carrying out process model querying tasks. The tool is useful for researchers and practitioners working with large process model collections, and specifically for those with an interest in model retrieval tasks as part of process compliance, process redesign and process standardization initiatives.