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In spite of recent advances in process mining, making this new technology accessible to non-technical users remains a challenge. Process maps and dashboards still seem to frighten many line of business professionals. In order to democratize this technology, we propose a natural language querying interface that allows non-technical users to retrieve relevant information and insights about their processes by simply asking questions in plain English. In this work we propose a reference architecture to support questions in natural language and provide the right answers by integrating to existing process mining tools. We combine classic natural language processing techniques (such as entity recognition and semantic parsing) with an abstract logical representation for process mining queries. We also provide a compilation of real natural language questions and an implementation of the architecture that interfaces to an existing commercial tool: Everflow. We also introduce a taxonomy for process mining related questions, and use that as a background grid to analyze the performance of this experiment. Finally, we point to potential future work opportunities in this field. [ABSTRACT FROM AUTHOR]

Process discovery is an essential discipline within process mining, which deals with the data-driven generation of insights into operational processes. From event data that capture historical process executions, process discovery algorithms learn a process model describing the execution of the various activities involved. Such discovered models are crucial artifacts used by many process mining techniques. Most existing process discovery approaches can be classified as conventional—they function like a black-box approach and often learn models of poor quality from event data. Cortado is a software tool dedicated to interactive process discovery that lets users gradually learn process models from event data. Cortado leverages domain knowledge and insights extracted from data to develop process models in an interactive manner gradually. We describe Cortado’s architecture and functionalities that contribute to the overall goal of interactive process discovery.

Organizations can benefit from the use of practices, techniques, and tools from the area of business process management. Through the focus on processes, they create process models that require management, including support for versioning, refactoring and querying. Querying thus far has primarily focused on structural properties of models rather than on exploiting behavioral properties capturing aspects of model execution. While the latter is more challenging, it is also more effective, especially when models are used for auditing or process automation. The focus of this paper is to overcome the challenges associated with behavioral querying of process models in order to unlock its benefits. The first challenge concerns determining decidability of the building blocks of the query language, which are the possible behavioral relations between process tasks. The second challenge concerns achieving acceptable performance of query evaluation. The evaluation of a query may require expensive checks in all process models, of which there may be thousands. In light of these challenges, this paper proposes a special-purpose programming language, namely Process Query Language (PQL) for behavioral querying of process model collections. The language relies on a set of behavioral predicates between process tasks, whose usefulness has been empirically evaluated with a pool of process model stakeholders. This study resulted in a selection of the predicates to be implemented in PQL, whose decidability has also been formally proven. The computational performance of the language has been extensively evaluated through a set of experiments against two large process model collections. [ABSTRACT FROM AUTHOR]

Process-oriented data analysis techniques allow organizations to understand how their processes operate, where modifications are needed and where enhancements are possible. A recurrent task in any process analysis technique is querying. Process data querying allows analysts to easily explore the data with the intent of getting insights about the execution of business processes. The current generation of process query languages targets data scientists. However, there is a need to a query language to support domain analysts who may be inexperienced with database technologies. This paper addresses this challenge by proposing a natural language interface that assists the end-users in querying the stored event data. The interface takes a natural language query from the user, automatically constructs a corresponding structured query to be executed over the stored event data. We use graph based storage techniques, namely labeled property graphs, which allow to explicitly model event data relationships. As an executable query language, we use the Cypher language which is widely used for querying property graphs. The approach has been implemented and evaluated using two publicly available event logs. • We proposed a natural language interface for querying process execution data from natural language. • We presents a Labeled Graph metamodel for stroing process data. • We proposed a hybrid pipline to automatically constructing Cypher queries from natural language. • Our NLI system is hybrid and combines machine learning and rule-based approaches. • We defined a set of general intent patterns that are domain-independent. • We evaluated the proposed system with more than 530 natural language queries. [ABSTRACT FROM AUTHOR]