14 results on '"process querying"'
Search Results
2. A natural language querying interface for process mining.
- Author
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Barbieri, Luciana, Madeira, Edmundo, Stroeh, Kleber, and van der Aalst, Wil
- 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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
3. Control-Flow-Based Querying of Process Executions from Partially Ordered Event Data
- Author
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Schuster, Daniel, Martini, Michael, van Zelst, Sebastiaan J., van der Aalst, Wil M. P., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Troya, Javier, editor, Medjahed, Brahim, editor, Piattini, Mario, editor, Yao, Lina, editor, Fernández, Pablo, editor, and Ruiz-Cortés, Antonio, editor
- Published
- 2022
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4. Towards a Natural Language Conversational Interface for Process Mining
- Author
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Barbieri, Luciana, Madeira, Edmundo Roberto Mauro, Stroeh, Kleber, van der Aalst, Wil M. P., van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Munoz-Gama, Jorge, editor, and Lu, Xixi, editor
- Published
- 2022
- Full Text
- View/download PDF
5. Cortado: A dedicated process mining tool for interactive process discovery
- Author
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Daniel Schuster, Sebastiaan J. van Zelst, and Wil M.P. van der Aalst
- Subjects
Process mining ,Process discovery ,Hybrid intelligence ,Process querying ,Conformance checking ,Data visualization ,Computer software ,QA76.75-76.765 - Abstract
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.
- Published
- 2023
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6. Process Mining Workshops
- Author
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Montali, Marco, Senderovich, Arik, and Weidlich, Matthias
- Subjects
process mining ,process discovery ,process analytics ,process querying ,conformance checking ,predictive process monitoring ,data science ,knowledge graphs ,event data ,streaming analytics ,machine learning ,deep learning ,business process management ,health informatics ,thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining ,thema EDItEUR::K Economics, Finance, Business and Management::KJ Business and Management::KJQ Business mathematics and systems ,thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning ,thema EDItEUR::U Computing and Information Technology::UB Information technology: general topics ,thema EDItEUR::U Computing and Information Technology::UB Information technology: general topics::UBH Digital and information technologies: Health and safety aspects - Abstract
This open access book constitutes revised selected papers from the International Workshops held at the 4th International Conference on Process Mining, ICPM 2022, which took place in Bozen-Bolzano, Italy, during October 23–28, 2022. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 42 papers included in this volume were carefully reviewed and selected from 89 submissions. They stem from the following workshops: – 3rd International Workshop on Event Data and Behavioral Analytics (EDBA) – 3rd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) – 3rd International Workshop on Responsible Process Mining (RPM) (previously known as Trust, Privacy and Security Aspects in Process Analytics) – 5th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) – 3rd International Workshop on Streaming Analytics for Process Mining (SA4PM) – 7th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) – 1st International Workshop on Education meets Process Mining (EduPM) – 1st International Workshop on Data Quality and Transformation in Process Mining (DQT-PM)
- Published
- 2023
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7. 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
8. 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
- Subjects
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
9. Process Mining Workshops. ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23-28, 2022, Revised Selected Papers.
- Author
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Montali, Marco, Montali, Marco, Senderovich, Arik, and Weidlich, Matthias
- Subjects
Business mathematics & systems ,Data mining ,Health & safety aspects of IT ,Information technology: general issues ,Machine learning ,business process management ,conformance checking ,data science ,deep learning ,event data ,health informatics ,knowledge graphs ,machine learning ,predictive process monitoring ,process analytics ,process discovery ,process mining ,process querying ,streaming analytics - Abstract
Summary: This open access book constitutes revised selected papers from the International Workshops held at the 4th International Conference on Process Mining, ICPM 2022, which took place in Bozen-Bolzano, Italy, during October 23-28, 2022. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 42 papers included in this volume were carefully reviewed and selected from 89 submissions. They stem from the following workshops: - 3rd International Workshop on Event Data and Behavioral Analytics (EDBA) - 3rd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) - 3rd International Workshop on Responsible Process Mining (RPM) (previously known as Trust, Privacy and Security Aspects in Process Analytics) - 5th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) - 3rd International Workshop on Streaming Analytics for Process Mining (SA4PM) - 7th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) - 1st International Workshop on Education meets Process Mining (EduPM) - 1st International Workshop on Data Quality and Transformation in Process Mining (DQT-PM)
10. Process Mining Workshops
- Author
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Munoz-Gama, Jorge and Lu, Xixi
- Subjects
Process Mining ,Process Discovery ,Process Analytics ,Process Querying ,Conformance Checking ,Predictive Process Monitoring ,Data Science ,Event Data ,Streaming Analytics ,Machine Learning ,Decision Support Systems ,Business Process Management ,Information Systems ,Petri Nets ,Open Access ,bic Book Industry Communication::U Computing & information technology::UN Databases::UNF Data mining ,bic Book Industry Communication::K Economics, finance, business & management::KJ Business & management::KJQ Business mathematics & systems ,bic Book Industry Communication::U Computing & information technology::UB Information technology: general issues - Abstract
This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included.
- Published
- 2022
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11. Natural language querying of process execution data.
- Author
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Kobeissi, Meriana, Assy, Nour, Gaaloul, Walid, Defude, Bruno, Benatallah, Boualem, and Haidar, Bassem
- Subjects
- *
NATURAL languages , *ELECTRONIC data processing , *DATABASES , *GRAPH algorithms , *QUERY languages (Computer science) - Abstract
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]
- Published
- 2023
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12. 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
13. 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
- Subjects
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
14. Process querying
- Author
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Alistair Barros, Wil M. P. van der Aalst, Artem Polyvyanyy, Chun Ouyang, and Process Science
- Subjects
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
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