7 results on '"process querying"'
Search Results
2. IMPERATIVE MODELS TO DECLARATIVE CONSTRAINTS : Generating Control-Flow Constraints from Business Process Models
- Author
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Bergman Thörn, Arvid
- Subjects
Process Mining ,Datavetenskap (datalogi) ,Computer Sciences ,Conformance checking ,Imperative modelling ,Business Process Management ,Declarative constraints ,Process querying - Abstract
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.
- Published
- 2023
3. A natural language querying interface for process mining
- Author
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Luciana Barbieri, Edmundo Madeira, Kleber Stroeh, Wil van der Aalst, and Publica
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Artificial Intelligence ,Computer Networks and Communications ,Hardware and Architecture ,Natural language interface ,Process mining ,Software ,Process querying ,Taxonomy ,Information Systems - Abstract
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.
- Published
- 2022
4. An intent-based natural language interface for querying process execution data
- Author
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Walid Gaaloul, Bassem Haidar, Bruno Defude, Meriana Kobeissi, Nour Assy, Algorithmes, Composants, Modèles Et Services pour l'informatique répartie (ACMES-SAMOVAR), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Institut Polytechnique de Paris (IP Paris), Département Informatique (INF), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Télécom SudParis (TSP), and Lebanese University [Beirut] (LU)
- Subjects
Information retrieval ,Natural language user interface ,Computer science ,Interface (Java) ,Event (computing) ,Natural language interface ,Database schema ,Process mining ,InformationSystems_DATABASEMANAGEMENT ,[SCCO.COMP]Cognitive science/Computer science ,computer.file_format ,Cypher language ,Query language ,Graph database ,Executable ,computer ,Natural language ,Process querying - Abstract
International audience; Process mining techniques allow organizations to discover, monitor and improve their as-is processes by analyzing the process execution data, aka event data, recorded by their information systems. A recurrent task in process mining is querying. Querying allows users to get insights into specific executions of their processes and to retrieve relevant data. Existing process querying techniques require end users to be knowledgeable of the query language and the database schema. However, a key success factor for process analysis is to make querying accessible to business experts who may be inexperienced in database querying. This paper addresses this challenge by proposing a natural language interface (NLI) for querying event data. The interface allows users to formulate their questions in natural language and to automatically translate the questions into a structured query that can be executed over a database. 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 a publicly available event log.
- Published
- 2021
5. Preface to the Special Issue on Process Querying and Declarative, Decision and Hybrid Approaches to Processes 2019
- Author
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Artem Polyvyanyy, Jan Vanthienen, Tijs Slaats, Arthur H. M. ter Hofstede, Claudio Di Ciccio, and Søren Debois
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process mining ,process querying ,declarative modelling ,Artificial Intelligence ,Computer Networks and Communications ,Computer science ,Process (engineering) ,business.industry ,Software engineering ,business ,Information Systems - Published
- 2021
6. Process querying
- Author
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Alistair Barros, Wil M. P. van der Aalst, Artem Polyvyanyy, Chun Ouyang, and Process Science
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Information Systems and Management ,Process modeling ,Computer science ,Business process ,Process mining ,Process intelligence ,02 engineering and technology ,Management Information Systems ,Business intelligence ,Business process management ,Business process discovery ,Business Process Model and Notation ,Enterprise system ,Arts and Humanities (miscellaneous) ,Business analytics ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Developmental and Educational Psychology ,Information system ,Use case ,Process science ,080600 INFORMATION SYSTEMS ,Process querying ,9. Industry and infrastructure ,business.industry ,Artifact-centric business process model ,Business process modeling ,Business operations ,Data science ,Process analytics ,Analytics ,020201 artificial intelligence & image processing ,Process management ,business ,Information Systems - Abstract
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.
- Published
- 2017
7. Process querying in Apromore
- Author
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Artem Polyvyanyy, Luigi Corno, Raffaele Conforti, Simon Raboczi, Marcello La Rosa, and Giancarlo Fortino
- Subjects
080500 DISTRIBUTED COMPUTING ,process querying ,process retrieval ,Apromore ,process query language ,080600 INFORMATION SYSTEMS ,process model repository - Abstract
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.
- Published
- 2015
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