Process Querying combines concepts from Big Data and Process Modeling and Analysis with Business Process Intelligence and Process Analytics to study techniques for retrieving and manipulating models of processes, both real-world and envisioned, to systematically organize and extract process-related information for subsequent use.

Process Querying

Process querying studies methods for automated management of real-world and envisioned processes, process models, process repositories, and process knowledge accumulated by modern organizations. To this end, process querying applies and contributes results in theoretical computer science fundamentals (e.g., distributed and parallel computing, model checking, and formal methods), software engineering, information systems, programming languages, workflow management, and business process management.

Scientific results that get devised as part of the process querying research initiative are published in academic book chapters, journal articles, and conference papers and implemented in a special-purpose (programming) language for automatic management of collections/repositories of process models/instances, which is called Process Query Language (PQL).

Process Querying research initiative spans a range of topics from theoretical studies of algorithms and the limits of computability of process querying techniques via practical issues of implementing process querying technologies in software to empirical studies on suitability and relevance of process querying.

The user interacts with process querying methods via process querying intents, where  a process querying intent is a (formally specified) request to manage process artifacts for a particular purpose and target audience.

Process Querying research initiative consists of the following areas:

  • Expressiveness. Studies the variety of concepts and principles that can be captured and/or exercised in the context of (behavioral) process querying with the goal of achieving full expressiveness, i.e., the ability to capture any process querying intent that calls for management of information related to process instances encoded in collections of process models.
  • Decidability and Complexity. Studies the amount of resources, e.g., computation time and storage space, required to implement process querying intents with the goal of devising techniques that require less resources to fulfill given intents.
  • Indexing. Studies data structures, called indices, which can be used to quickly attain process querying intents with the goal of minimizing space required to store indices and time for implementing process querying intents based on these indices.
  • Process Query Languages. Studies formal languages for capturing process querying intents in formats that can be interpreted by humans and/or machines, e.g., computers.
  • Empirical Evaluation and Validation. Studies whether various process querying intents meet the needs of users by designing and conducting proper experiments that assess different process querying factors in isolation as well as jointly, with the goal of obtaining statistically significant conclusions.
  • Label Management. Studies the use of activity/task labels captured in natural languages in attaining various process querying intents, which may involve reformulation of the intents based on label similarities for the purpose of improving usefulness and/or performance of process querying.
  • Approximate Process Querying. Studies techniques that exploit close and/or partial fulfillments of process querying intents with the goal of improving usefulness and/or performance of process querying, e.g., if a user wants to verify whether a given process querying intent can be fulfilled in 80% of process instances encoded in a process model.
  • Evidence-based Process Querying. Studies the use of execution logs in process querying with the goal of improving its usefulness and/or performance, e.g., in addition to querying over pre-modelled domain knowledge described in process models, a user may want to verify whether a given process querying intent is fulfilled in a pre-recorded event log that captures process instances that were observed in the real world.
  • Multi-perspective Process Querying. Studies the use of various process model perspectives, e.g., data, time, resource, etc., for the purpose of process querying.
  • Process Querying with Rich Annotations. Studies the use of rich ontology annotations of process models for the purpose of process querying.
  • Applications. Studies various ways of putting the theory of process querying in operation for achieving concrete outcomes in different areas of practical interest, e.g., process compliance, process reuse, process standardization, etc.