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207 results on '"Kalenkova, A."'

1. Agent Miner: An Algorithm for Discovering Agent Systems from Event Data

Author

Tour, Andrei, Polyvyanyy, Artem, Kalenkova, Anna, and Senderovich, Arik

Subjects
Computer Science - Multiagent Systems
Abstract

Process discovery studies ways to use event data generated by business processes and recorded by IT systems to construct models that describe the processes. Existing discovery algorithms are predominantly concerned with constructing process models that represent the control flow of the processes. Agent system mining argues that business processes often emerge from interactions of autonomous agents and uses event data to construct models of the agents and their interactions. This paper presents and evaluates Agent Miner, an algorithm for discovering models of agents and their interactions from event data composing the system that has executed the processes which generated the input data. The conducted evaluation using our open-source implementation of Agent Miner and publicly available industrial datasets confirms that our algorithm can provide insights into the process participants and their interaction patterns and often discovers models that describe the business processes more faithfully than process models discovered using conventional process discovery algorithms.

Published
2022

2. Performance Analysis: Discovering Semi-Markov Models From Event Logs

Author

Kalenkova, Anna, Mitchell, Lewis, and Roughan, Matthew

Subjects
Computer Science - Databases
Abstract

Process mining is a well-established discipline of data analysis focused on the discovery of process models from information systems' event logs. Recently, an emerging subarea of process mining - stochastic process discovery has started to evolve. Stochastic process discovery considers frequencies of events in the event data and allows for more comprehensive analysis. In particular, when durations of activities are presented in the event log, performance characteristics of the discovered stochastic models can be analyzed, e.g., the overall process execution time can be estimated. Existing performance analysis techniques usually discover stochastic process models from event data and then simulate these models to evaluate their execution times. These methods rely on empirical approaches. This paper proposes analytical techniques for performance analysis allowing for the derivation of statistical characteristics of the overall processes' execution times in the presence of arbitrary time distributions of events modeled by semi-Markov processes. The proposed methods can significantly simplify the what-if analysis of processes by providing solutions without resorting to simulation., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Published
2022

3. Automated Repair of Process Models with Non-Local Constraints Using State-Based Region Theory

Author

Kalenkova, Anna, Carmona, Josep, Polyvyanyy, Artem, and La Rosa, Marcello

Subjects
Computer Science - Artificial Intelligence, Computer Science - Formal Languages and Automata Theory
Abstract

State-of-the-art process discovery methods construct free-choice process models from event logs. Consequently, the constructed models do not take into account indirect dependencies between events. Whenever the input behaviour is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for enhancing free-choice process models by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from the performance of existing process discovery methods and the accuracy of the employed fundamental synthesis techniques. We prove that the proposed approach preserves fitness with respect to the event log while improving the precision when indirect dependencies exist. The approach has been implemented and tested on both synthetic and real-life datasets. The results show its effectiveness in repairing models discovered from event logs.

Published
2021

5. Agent Miner: An Algorithm for Discovering Agent Systems from Event Data

Author

Tour, Andrei, Polyvyanyy, Artem, Kalenkova, Anna, Senderovich, Arik, 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, Di Francescomarino, Chiara, editor, Burattin, Andrea, editor, Janiesch, Christian, editor, and Sadiq, Shazia, editor

Published
2023
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6. Entropia: A Family of Entropy-Based Conformance Checking Measures for Process Mining

Author

Polyvyanyy, Artem, Alkhammash, Hanan, Di Ciccio, Claudio, García-Bañuelos, Luciano, Kalenkova, Anna, Leemans, Sander J. J., Mendling, Jan, Moffat, Alistair, and Weidlich, Matthias

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Formal Languages and Automata Theory, Computer Science - Information Theory
Abstract

This paper presents a command-line tool, called Entropia, that implements a family of conformance checking measures for process mining founded on the notion of entropy from information theory. The measures allow quantifying classical non-deterministic and stochastic precision and recall quality criteria for process models automatically discovered from traces executed by IT-systems and recorded in their event logs. A process model has "good" precision with respect to the log it was discovered from if it does not encode many traces that are not part of the log, and has "good" recall if it encodes most of the traces from the log. By definition, the measures possess useful properties and can often be computed quickly., Comment: 4 pages

Published
2020

9. Structural and Behavioral Biases in Process Comparison Using Models and Logs

Author

Kalenkova, Anna, Polyvyanyy, Artem, Rosa, Marcello La, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ghose, Aditya, editor, Horkoff, Jennifer, editor, Silva Souza, Vítor E., editor, Parsons, Jeffrey, editor, and Evermann, Joerg, editor

Published
2021
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14. A Spectrum of Entropy-Based Precision and Recall Measurements Between Partially Matching Designed and Observed Processes

Author

Kalenkova, Anna, Polyvyanyy, Artem, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Kafeza, Eleanna, editor, Benatallah, Boualem, editor, Martinelli, Fabio, editor, Hacid, Hakim, editor, Bouguettaya, Athman, editor, and Motahari, Hamid, editor

Published
2020
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15. A Framework for Estimating Simplicity of Automatically Discovered Process Models Based on Structural and Behavioral Characteristics

Author

Kalenkova, Anna, Polyvyanyy, Artem, La Rosa, Marcello, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Fahland, Dirk, editor, Ghidini, Chiara, editor, Becker, Jörg, editor, and Dumas, Marlon, editor

Published
2020
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16. Automated Repair of Process Models Using Non-local Constraints

Author

Kalenkova, Anna, Carmona, Josep, Polyvyanyy, Artem, La Rosa, Marcello, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Janicki, Ryszard, editor, Sidorova, Natalia, editor, and Chatain, Thomas, editor

Published
2020
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26. E-Government Services: Comparing Real and Expected User Behavior

Author

Kalenkova, A. A., Ageev, A. A., Lomazova, I. A., van der Aalst, W. M. P., van der Aalst, Wil M. P., Series editor, Mylopoulos, John, Series editor, Rosemann, Michael, Series editor, Shaw, Michael J., Series editor, Szyperski, Clemens, Series editor, Teniente, Ernest, editor, and Weidlich, Matthias, editor

Published
2018
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27. On the Efficient Application of Aho-Corasick Algorithm in Process Mining

Author

Konchagin, Andrey M., Kalenkova, Anna A., Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, van der Aalst, Wil M.P., editor, Ignatov, Dmitry I., editor, Khachay, Michael, editor, Kuznetsov, Sergei O., editor, Lempitsky, Victor, editor, Lomazova, Irina A., editor, Loukachevitch, Natalia, editor, Napoli, Amedeo, editor, Panchenko, Alexander, editor, Pardalos, Panos M., editor, Savchenko, Andrey V., editor, and Wasserman, Stanley, editor

Published
2018
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28. Constructing Regular Expressions from Real-Life Event Logs

Author

Tarantsova, Polina D., Kalenkova, Anna A., Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, van der Aalst, Wil M. P., editor, Batagelj, Vladimir, editor, Glavaš, Goran, editor, Ignatov, Dmitry I., editor, Khachay, Michael, editor, Kuznetsov, Sergei O., editor, Koltsova, Olessia, editor, Lomazova, Irina A., editor, Loukachevitch, Natalia, editor, Napoli, Amedeo, editor, Panchenko, Alexander, editor, Pardalos, Panos M., editor, Pelillo, Marcello, editor, and Savchenko, Andrey V., editor

Published
2018
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34. Application of Genetic Algorithms for Finding Edit Distance between Process Models

Author

Anna A. Kalenkova and Danil A. Kolesnikov

Subjects
минимальное редакционное расстояние между графами, извлечение и анализ процессов, bpmn (business process model and notation), генетический алгоритм, Information technology, T58.5-58.64
Abstract

Finding graph-edit distance (graph similarity) is an important task in many computer science areas, such as image analysis, machine learning, chemicalinformatics. Recently, with the development of process mining techniques, it became important to adapt and apply existing graph analysis methods to examine process models (annotated graphs) discovered from event data. In particular, finding graph-edit distance techniques can be used to reveal patterns (subprocesses), compare discovered process models. As it was shown experimentally and theoretically justified, exact methods for finding graph-edit distances between discovered process models (and graphs in general) are computationally expensive and can be applied to small models only. In this paper, we present and assess accuracy and performance characteristics of an inexact genetic algorithm applied to find distances between process models discovered from event logs. In particular, we find distances between BPMN (Business Process Model and Notation) models discovered from event logs by using different process discovery algorithms. We show that the genetic algorithm allows us to dramatically reduce the time of comparison and produces results which are close to the optimal solutions (minimal graph edit distances calculated by the exact search algorithm).

Published
2018
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35. Comparing process models in the BPMN 2.0 XML format

Author

Sergey Ivanov and Anna Kalenkova

Subjects
business process modeling, business process comparision, bpmn 2.0 (business process model and notation), xml (extensible markup language), process mining, Electronic computers. Computer science, QA75.5-76.95
Abstract

Comparing business process models is one of the most significant challenges for business and systems analysts. The complexity of the problem is explained by the fact there is a lack of tools that can be used for comparing business process models. Also there is no universally accepted standard for modeling them. EPC, YAWL, BPEL, XPDL and BPMN are only a small fraction of available notations that have found acceptance among developers. Every process modeling standard has its advantages and disadvantages, but almost all of them comprise an XML schema, which defines process serialization rules. Due to the fact that XML naturally represents hierarchical and reference structure of business process models, these models can be compared using their XML representations. In this paper we propose a generic comparison approach, which is applicable to XML representations of business process models. Using this approach we have developed a tool, which currently supports BPMN 2.0 [1] (one of the most popular business process modeling notations), but can be extended to support other business process modeling standards.

Published
2018
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39. Transition Systems Reduction: Balancing Between Precision and Simplicity

Author

Shershakov, Sergey A., Kalenkova, Anna A., Lomazova, Irina A., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Koutny, Maciej, editor, Kleijn, Jetty, editor, and Penczek, Wojciech, editor

Published
2017
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47. Process Discovery Using Localized Events

Author

van der Aalst, Wil M. P., Kalenkova, Anna, Rubin, Vladimir, Verbeek, Eric, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Devillers, Raymond, editor, and Valmari, Antti, editor

Published
2015
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49. Process Model Discovery: A Method Based on Transition System Decomposition

Author

Kalenkova, Anna A., Lomazova, Irina A., van der Aalst, Wil M. P., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ciardo, Gianfranco, editor, and Kindler, Ekkart, editor

Published
2014
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50. Automated Repair of Process Models with Non-local Constraints Using State-Based Region Theory

Author

Kalenkova, Anna, Carmona, Josep, Polyvyanyy, Artem, La Rosa, Marcello, Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, and Universitat Politècnica de Catalunya. ALBCOM - Algorísmia, Bioinformàtica, Complexitat i Mètodes Formals

Subjects
FOS: Computer and information sciences, Event logs, Free-choice Petri nets, Process enhancement, Algebra and Number Theory, Formal Languages and Automata Theory (cs.FL), Computer Science - Artificial Intelligence, Petri nets, Computer Science - Formal Languages and Automata Theory, Region state-based synthesis, Transition systems, Theoretical Computer Science, Artificial Intelligence (cs.AI), Informàtica::Informàtica teòrica [Àrees temàtiques de la UPC], Computational Theory and Mathematics, Petri, Xarxes de, Process mining, Mineria de dades, Data mining, Information Systems
Abstract

State-of-the-art process discovery methods construct free-choice process models from event logs. Consequently, the constructed models do not take into account indirect dependencies between events. Whenever the input behaviour is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for enhancing free-choice process models by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from the performance of existing process discovery methods and the accuracy of the employed fundamental synthesis techniques. We prove that the proposed approach preserves fitness with respect to the event log while improving the precision when indirect dependencies exist. The approach has been implemented and tested on both synthetic and real-life datasets. The results show its effectiveness in repairing models discovered from event logs. This work was partly supported by the Australian Research Council Discovery Project DP180102839. This work was supported by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R.

Published
2022

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