research-article

Big data meets process mining: implementing the alpha algorithm with map-reduce

Authors:

Joerg Evermann,

Ghazal AssadipourAuthors Info & Claims

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

Pages 1414 - 1416

https://doi.org/10.1145/2554850.2555076

Published: 24 March 2014 Publication History

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Abstract

Process mining is an approach to extract process models from event logs. Given the distributed nature of modern information systems, event logs are likely to be distributed across different physical machines. Map-Reduce is a scalable approach for efficient computations on distributed data. In this paper we present the design of a Map-Reduce implementation of the Alpha process mining algorithm, to take advantage of the scalability of the Map-Reduce approach. We provide a experimental results that show the performance and scalability of our implementation.

References

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Cited By

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Rinderle-Ma SMangler JRitter DRinderle-Ma SMangler JRitter D(2024)Process ChoreographyFundamentals of Information Systems Interoperability10.1007/978-3-031-48322-6_8(227-258)Online publication date: 19-Apr-2024
https://doi.org/10.1007/978-3-031-48322-6_8
Nour Eldin AAssy NKobeissi MBaudot JGaaloul W(2022)Enabling Multi-process Discovery on Graph DatabasesCooperative Information Systems10.1007/978-3-031-17834-4_7(112-130)Online publication date: 25-Sep-2022
https://doi.org/10.1007/978-3-031-17834-4_7
Zayoud MKotb YIonescu S(2019)$\beta$ Algorithm: A New Probabilistic Process Learning Approach for Big Data in HealthcareIEEE Access10.1109/ACCESS.2019.29226357(78842-78869)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2922635
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Big data meets process mining: implementing the alpha algorithm with map-reduce

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Information & Contributors

Information

Published In

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

March 2014

1890 pages

ISBN:9781450324694

DOI:10.1145/2554850

Conference Chairs:
Yookun Cho
Seoul National University, Korea
,
Sung Y. Shin
South Dakota State University
,
Program Chairs:
Sangwook Kim
Kyungpook National University, Korea
,
Chih-Cheng Hung
Southern Polytechnic State University
,
Jiman Hong
Soongsil University, South Korea

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 March 2014

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SAC 2014

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SIGAPP

SAC 2014: Symposium on Applied Computing

March 24 - 28, 2014

Gyeongju, Republic of Korea

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SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

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Rinderle-Ma SMangler JRitter DRinderle-Ma SMangler JRitter D(2024)Process ChoreographyFundamentals of Information Systems Interoperability10.1007/978-3-031-48322-6_8(227-258)Online publication date: 19-Apr-2024
https://doi.org/10.1007/978-3-031-48322-6_8
Nour Eldin AAssy NKobeissi MBaudot JGaaloul W(2022)Enabling Multi-process Discovery on Graph DatabasesCooperative Information Systems10.1007/978-3-031-17834-4_7(112-130)Online publication date: 25-Sep-2022
https://doi.org/10.1007/978-3-031-17834-4_7
Zayoud MKotb YIonescu S(2019)$\beta$ Algorithm: A New Probabilistic Process Learning Approach for Big Data in HealthcareIEEE Access10.1109/ACCESS.2019.29226357(78842-78869)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2922635
Loreti DChesani FCiampolini AMello PBinder WCortellessa VKoziolek ASmirni EPoess M(2017)Distributed Compliance Monitoring of Business Processes over MapReduce ArchitecturesProceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion10.1145/3053600.3053616(79-84)Online publication date: 18-Apr-2017
https://dl.acm.org/doi/10.1145/3053600.3053616
Subramanian SVijayalakshmi SVenkataraman BVenkumar PRathikaa Sre R(2017)CCCa Framework - Classification System in Big Data Environment with Clustering and Cache ConceptsProceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016)10.1007/978-3-319-60618-7_5(44-53)Online publication date: 19-Aug-2017
https://doi.org/10.1007/978-3-319-60618-7_5
Thaler TNorek SDe Angelis VMaurer DFettke PLoos P(2017)Mining the Usability of Process-Oriented Business Software: The Case of the ARIS Designer of Software AGBusiness Process Management Cases10.1007/978-3-319-58307-5_16(291-310)Online publication date: 11-Aug-2017
https://doi.org/10.1007/978-3-319-58307-5_16
Evermann J(2016)Scalable Process Discovery Using Map-ReduceIEEE Transactions on Services Computing10.1109/TSC.2014.23675259:3(469-481)Online publication date: 1-May-2016
https://doi.org/10.1109/TSC.2014.2367525
Hinkka MLehto THeljanko K(2016)Assessing Big Data SQL Frameworks for Analyzing Event Logs2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)10.1109/PDP.2016.26(101-108)Online publication date: Feb-2016
https://doi.org/10.1109/PDP.2016.26
Malli Subramanian SSoundarajan V(2016)SC‐OCR: similarity‐based clustering and optimum cache replacement approachConcurrency and Computation: Practice and Experience10.1002/cpe.391629:4Online publication date: 2-Aug-2016
https://doi.org/10.1002/cpe.3916

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