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157 results on '"Senderovich, Arik"'

1. Measuring Rule-based LTLf Process Specifications: A Probabilistic Data-driven Approach

Author

Cecconi, Alessio, Barbaro, Luca, Di Ciccio, Claudio, and Senderovich, Arik

Subjects
Computer Science - Artificial Intelligence, Computer Science - Logic in Computer Science
Abstract

Declarative process specifications define the behavior of processes by means of rules based on Linear Temporal Logic on Finite Traces (LTLf). In a mining context, these specifications are inferred from, and checked on, multi-sets of runs recorded by information systems (namely, event logs). To this end, being able to gauge the degree to which process data comply with a specification is key. However, existing mining and verification techniques analyze the rules in isolation, thereby disregarding their interplay. In this paper, we introduce a framework to devise probabilistic measures for declarative process specifications. Thereupon, we propose a technique that measures the degree of satisfaction of specifications over event logs. To assess our approach, we conduct an evaluation with real-world data, evidencing its applicability in discovery, checking, and drift detection contexts.

Published
2023

2. Can I Trust My Simulation Model? Measuring the Quality of Business Process Simulation Models

Author

Chapela-Campa, David, Benchekroun, Ismail, Baron, Opher, Dumas, Marlon, Krass, Dmitry, and Senderovich, Arik

Subjects
Computer Science - Software Engineering, Computer Science - Machine Learning
Abstract

Business Process Simulation (BPS) is an approach to analyze the performance of business processes under different scenarios. For example, BPS allows us to estimate what would be the cycle time of a process if one or more resources became unavailable. The starting point of BPS is a process model annotated with simulation parameters (a BPS model). BPS models may be manually designed, based on information collected from stakeholders and empirical observations, or automatically discovered from execution data. Regardless of its origin, a key question when using a BPS model is how to assess its quality. In this paper, we propose a collection of measures to evaluate the quality of a BPS model w.r.t. its ability to replicate the observed behavior of the process. We advocate an approach whereby different measures tackle different process perspectives. We evaluate the ability of the proposed measures to discern the impact of modifications to a BPS model, and their ability to uncover the relative strengths and weaknesses of two approaches for automated discovery of BPS models. The evaluation shows that the measures not only capture how close a BPS model is to the observed behavior, but they also help us to identify sources of discrepancies., Comment: Preprint submitted to the 21st International Conference on Business Process Management (BPM 2023)

Published
2023

3. 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

4. Capacity Allocation for Clouds with Parallel Processing, Batch Arrivals, and Heterogeneous Service Requirements

Author

Furman, Eugene, Senderovich, Arik, Bergsma, Shane, and Beck, J. Christopher

Subjects
Statistics - Applications
Abstract

Problem Definition: Allocating sufficient capacity to cloud services is a challenging task, especially when demand is time-varying, heterogeneous, contains batches, and requires multiple types of resources for processing. In this setting, providers decide whether to reserve portions of their capacity to individual job classes or to offer it in a flexible manner. Methodology/results: In collaboration with Huawei Cloud, a worldwide provider of cloud services, we propose a heuristic policy that allocates multiple types of resources to jobs and also satisfies their pre-specified service level agreements (SLAs). We model the system as a multi-class queueing network with parallel processing and multiple types of resources, where arrivals (i.e., virtual machines and containers) follow time-varying patterns and require at least one unit of each resource for processing. While virtual machines leave if they are not served immediately, containers can join a queue. We introduce a diffusion approximation of the offered load of such system and investigate its fidelity as compared to the observed data. Then, we develop a heuristic approach that leverages this approximation to determine capacity levels that satisfy probabilistic SLAs in the system with fully flexible servers. Managerial Implications: Using a data set of cloud computing requests over a representative 8-day period from Huawei Cloud, we show that our heuristic policy results in a 20% capacity reduction and better service quality as compared to a benchmark that reserves resources. In addition, we show that the system utilization induced by our policy is superior to the benchmark, i.e., it implies less idling of resources in most instances. Thus, our approach enables cloud operators to both reduce costs and achieve better performance.

Published
2022

5. Can machines solve general queueing systems?

Author

Sherzer, Eliran, Senderovich, Arik, Baron, Opher, and Krass, Dmitry

Subjects
Computer Science - Machine Learning
Abstract

In this paper, we analyze how well a machine can solve a general problem in queueing theory. To answer this question, we use a deep learning model to predict the stationary queue-length distribution of an $M/G/1$ queue (Poisson arrivals, general service times, one server). To the best of our knowledge, this is the first time a machine learning model is applied to a general queueing theory problem. We chose $M/G/1$ queue for this paper because it lies "on the cusp" of the analytical frontier: on the one hand exact solution for this model is available, which is both computationally and mathematically complex. On the other hand, the problem (specifically the service time distribution) is general. This allows us to compare the accuracy and efficiency of the deep learning approach to the analytical solutions. The two key challenges in applying machine learning to this problem are (1) generating a diverse set of training examples that provide a good representation of a "generic" positive-valued distribution, and (2) representations of the continuous distribution of service times as an input. We show how we overcome these challenges. Our results show that our model is indeed able to predict the stationary behavior of the $M/G/1$ queue extremely accurately: the average value of our metric over the entire test set is $0.0009$. Moreover, our machine learning model is very efficient, computing very accurate stationary distributions in a fraction of a second (an approach based on simulation modeling would take much longer to converge). We also present a case-study that mimics a real-life setting and shows that our approach is more robust and provides more accurate solutions compared to the existing methods. This shows the promise of extending our approach beyond the analytically solvable systems (e.g., $G/G/1$ or $G/G/c$).

Published
2022

6. 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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7. Transformer Models for Activity Mining in Knowledge-Intensive Processes

Author

Khandaker, Faria, Senderovich, Arik, Yu, Eric, Carbajales, Sebastian, Chan, Allen, van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Cabanillas, Cristina, editor, Garmann-Johnsen, Niels Frederik, editor, and Koschmider, Agnes, editor

Published
2023
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13. Cut to the Trace! Process-Aware Partitioning of Long-Running Cases in Customer Journey Logs

Author

Bernard, Gaël, Senderovich, Arik, Andritsos, Periklis, 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, La Rosa, Marcello, editor, Sadiq, Shazia, editor, and Teniente, Ernest, editor

Published
2021
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14. An Ising Framework for Constrained Clustering on Special Purpose Hardware

Author

Cohen, Eldan, Senderovich, Arik, Beck, J. Christopher, 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, Hebrard, Emmanuel, editor, and Musliu, Nysret, editor

Published
2020
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15. Socially-Aware Business Process Redesign

Author

Senderovich, Arik, Schippers, Joop J., Reijers, Hajo A., 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. Process Minding: Closing the Big Data Gap

Author

Gal, Avigdor, 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, 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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18. Supervised ML for Solving the GI / GI /1 Queue.

Author

Baron, Opher, Krass, Dmitry, Senderovich, Arik, and Sherzer, Eliran

Subjects
DATA libraries, SUPERVISED learning, QUEUING theory, DATA science, MACHINE learning, ANALYTICAL solutions
Abstract

We apply supervised learning to a general problem in queueing theory: using a neural net, we develop a fast and accurate predictor of the stationary system-length distribution of a GI/GI/1 queue—a fundamental queueing model for which no analytical solutions are available. To this end, we must overcome three main challenges: (i) generating a large library of training instances that cover a wide range of arbitrary interarrival and service time distributions, (ii) labeling the training instances, and (iii) providing continuous arrival and service distributions as inputs to the neural net. To overcome (i), we develop an algorithm to sample phase-type interarrival and service time distributions with complex transition structures. We demonstrate that our distribution-generating algorithm indeed covers a wide range of possible positive-valued distributions. For (ii), we label our training instances via quasi-birth-and-death(QBD) that was used to approximate PH/PH/1 (with phase-type arrival and service process) as labels for the training data. For (iii), we find that using only the first five moments of both the interarrival and service times distribution as inputs is sufficient to train the neural net. Our empirical results show that our neural model can estimate the stationary behavior of the GI/GI/1—far exceeding other available methods in terms of both accuracy and runtimes. History: Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: O. Baron received financial support from the Natural Sciences and Engineering Research Council of Canada (NERC) [Grant 458051]. D. Krass received financial support from the NERC [Grant 458395]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0263) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0263). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Published
2024
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23. How Much Event Data Is Enough? A Statistical Framework for Process Discovery

Author

Bauer, Martin, Senderovich, Arik, Gal, Avigdor, Grunske, Lars, Weidlich, Matthias, 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, Krogstie, John, editor, and Reijers, Hajo A., editor

Published
2018
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24. Fusion-Based Process Discovery

Author

Dahari, Yossi, Gal, Avigdor, Senderovich, Arik, Weidlich, Matthias, 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, Krogstie, John, editor, and Reijers, Hajo A., editor

Published
2018
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26. Intra and Inter-case Features in Predictive Process Monitoring: A Tale of Two Dimensions

Author

Senderovich, Arik, Di Francescomarino, Chiara, Ghidini, Chiara, Jorbina, Kerwin, Maggi, Fabrizio Maria, 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, Carmona, Josep, editor, Engels, Gregor, editor, and Kumar, Akhil, editor

Published
2017
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27. Temporal Network Representation of Event Logs for Improved Performance Modelling in Business Processes

Author

Senderovich, Arik, Weidlich, Matthias, Gal, Avigdor, 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, Carmona, Josep, editor, Engels, Gregor, editor, and Kumar, Akhil, editor

Published
2017
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29. Process Mining Workshops

Author

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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30. Discovering Queues from Event Logs with Varying Levels of Information

Author

Senderovich, Arik, Leemans, Sander J. J., Harel, Shahar, Gal, Avigdor, Mandelbaum, Avishai, van der Aalst, Wil 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, Reichert, Manfred, editor, and Reijers, Hajo A., editor

Published
2016
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31. P-Folder: Optimal Model Simplification for Improving Accuracy in Process Performance Prediction

Author

Senderovich, Arik, Shleyfman, Alexander, Weidlich, Matthias, Gal, Avigdor, Mandelbaum, Avishai, 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, La Rosa, Marcello, editor, Loos, Peter, editor, and Pastor, Oscar, editor

Published
2016
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32. In Log and Model We Trust? A Generalized Conformance Checking Framework

Author

Rogge-Solti, Andreas, Senderovich, Arik, Weidlich, Matthias, Mendling, Jan, Gal, Avigdor, 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, La Rosa, Marcello, editor, Loos, Peter, editor, and Pastor, Oscar, editor

Published
2016
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33. The ROAD from Sensor Data to Process Instances via Interaction Mining

Author

Senderovich, Arik, Rogge-Solti, Andreas, Gal, Avigdor, Mendling, Jan, Mandelbaum, Avishai, 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, Nurcan, Selmin, editor, Soffer, Pnina, editor, Bajec, Marko, editor, and Eder, Johann, editor

Published
2016
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36. Service Analysis and Simulation in Process Mining

Author

Senderovich, Arik, van der Aalst, Wil, Series editor, Mylopoulos, John, Series editor, Rosemann, Michael, Series editor, Shaw, Michael J., Series editor, Szyperski, Clemens, Series editor, Fournier, Fabiana, editor, and Mendling, Jan, editor

Published
2015
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37. Data-Driven Performance Analysis of Scheduled Processes

Author

Senderovich, Arik, Rogge-Solti, Andreas, Gal, Avigdor, Mendling, Jan, Mandelbaum, Avishai, Kadish, Sarah, Bunnell, Craig 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, Motahari-Nezhad, Hamid Reza, editor, Recker, Jan, editor, and Weidlich, Matthias, editor

Published
2015
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38. Discovery and Validation of Queueing Networks in Scheduled Processes

Author

Senderovich, Arik, Weidlich, Matthias, Gal, Avigdor, Mandelbaum, Avishai, Kadish, Sarah, Bunnell, Craig 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, Zdravkovic, Jelena, editor, Kirikova, Marite, editor, and Johannesson, Paul, editor

Published
2015
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40. Mining Resource Scheduling Protocols

Author

Senderovich, Arik, Weidlich, Matthias, Gal, Avigdor, Mandelbaum, Avishai, 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, Sadiq, Shazia, editor, Soffer, Pnina, editor, and Völzer, Hagen, editor

Published
2014
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41. Queue Mining – Predicting Delays in Service Processes

Author

Senderovich, Arik, Weidlich, Matthias, Gal, Avigdor, Mandelbaum, Avishai, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Jarke, Matthias, editor, Mylopoulos, John, editor, Quix, Christoph, editor, Rolland, Colette, editor, Manolopoulos, Yannis, editor, Mouratidis, Haralambos, editor, and Horkoff, Jennifer, editor

Published
2014
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