research-article

A Data-driven Process Recommender Framework

Authors:

Richard A. Farneth,

Randall S. Burd,

Ivan MarsicAuthors Info & Claims

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 2111 - 2120

https://doi.org/10.1145/3097983.3098174

Published: 13 August 2017 Publication History

Abstract

We present an approach for improving the performance of complex knowledge-based processes by providing data-driven step-by-step recommendations. Our framework uses the associations between similar historic process performances and contextual information to determine the prototypical way of enacting the process. We introduce a novel similarity metric for grouping traces into clusters that incorporates temporal information about activity performance and handles concurrent activities. Our data-driven recommender system selects the appropriate prototype performance of the process based on user-provided context attributes. Our approach for determining the prototypes discovers the commonly performed activities and their temporal relationships. We tested our system on data from three real-world medical processes and achieved recommendation accuracy up to an F1 score of 0.77 (compared to an F1 score of 0.37 using ZeroR) with 63.2% of recommended enactments being within the first five neighbors of the actual historic enactments in a set of 87 cases. Our framework works as an interactive visual analytic tool for process mining. This work shows the feasibility of data-driven decision support system for complex knowledge-based processes.

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Cited By

Hermann JRusche SModer LWeibelzahl M(2024)Watt’s Next? Leveraging Process Flexibility for Power Cost OptimizationBusiness & Information Systems Engineering10.1007/s12599-024-00888-1Online publication date: 14-Aug-2024
https://doi.org/10.1007/s12599-024-00888-1
Gao CMarsic ISarcevic AGestrich-Thompson WBurd R(2023)Real-time Context-Aware Multimodal Network for Activity and Activity-Stage Recognition from Team Communication in Dynamic Clinical SettingsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35807987:1(1-28)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3580798
Guo CChen J(2023)Big Data Analytics in HealthcareKnowledge Technology and Systems10.1007/978-981-99-1075-5_2(27-70)Online publication date: 14-Jun-2023
https://doi.org/10.1007/978-981-99-1075-5_2
Show More Cited By

Index Terms

A Data-driven Process Recommender Framework
1. Applied computing
  1. Enterprise computing
    1. Business process management
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Similarity measures
    2. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Clustering
    2. Decision support systems
      1. Data analytics

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Published In

cover image ACM Conferences

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2017

2240 pages

ISBN:9781450348874

DOI:10.1145/3097983

General Chairs:
Stan Matwin
Dalhousie University
,
Shipeng Yu
LinkedIn
,
Faisal Farooq
IBM

Copyright © 2017 ACM.

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 ACM 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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Published: 13 August 2017

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National Institutes of Health

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KDD '17

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KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 13 - 17, 2017

NS, Halifax, Canada

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KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Hermann JRusche SModer LWeibelzahl M(2024)Watt’s Next? Leveraging Process Flexibility for Power Cost OptimizationBusiness & Information Systems Engineering10.1007/s12599-024-00888-1Online publication date: 14-Aug-2024
https://doi.org/10.1007/s12599-024-00888-1
Gao CMarsic ISarcevic AGestrich-Thompson WBurd R(2023)Real-time Context-Aware Multimodal Network for Activity and Activity-Stage Recognition from Team Communication in Dynamic Clinical SettingsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35807987:1(1-28)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3580798
Guo CChen J(2023)Big Data Analytics in HealthcareKnowledge Technology and Systems10.1007/978-981-99-1075-5_2(27-70)Online publication date: 14-Jun-2023
https://doi.org/10.1007/978-981-99-1075-5_2
Kubrak KMilani FNolte ADumas M(2023)Design and Evaluation of a User Interface Concept for Prescriptive Process MonitoringAdvanced Information Systems Engineering10.1007/978-3-031-34560-9_21(347-363)Online publication date: 8-Jun-2023
https://doi.org/10.1007/978-3-031-34560-9_21
Leemets JKubrak KMilani FNolte A(2023)Persuasive Visual Presentation of Prescriptive Business ProcessesResearch Challenges in Information Science: Information Science and the Connected World10.1007/978-3-031-33080-3_24(398-414)Online publication date: 23-May-2023
https://doi.org/10.1007/978-3-031-33080-3_24
Erdogan TTarhan A(2022)Multi-perspective process mining for emergency processHealth Informatics Journal10.1177/1460458222107719528:1(146045822210771)Online publication date: 23-Feb-2022
https://doi.org/10.1177/14604582221077195
Yang SSarcevic AFarneth RChen SAhmed OMarsic IBurd R(2022)An approach to automatic process deviation detection in a time-critical clinical processJournal of Biomedical Informatics10.1016/j.jbi.2018.07.02285:C(155-167)Online publication date: 21-Apr-2022
https://dl.acm.org/doi/10.1016/j.jbi.2018.07.022
Yari Eili MRezaeenour J(2022)A survey on recommendation in process miningConcurrency and Computation: Practice and Experience10.1002/cpe.730434:26Online publication date: 6-Sep-2022
https://doi.org/10.1002/cpe.7304
Dallagassa Mdos Santos Garcia CScalabrin EIoshii SCarvalho D(2021)Opportunities and challenges for applying process mining in healthcare: a systematic mapping studyJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-021-02894-713:1(165-182)Online publication date: 8-Feb-2021
https://doi.org/10.1007/s12652-021-02894-7
Garcia CMeincheim AFaria Junior EDallagassa MSato DCarvalho DSantos EScalabrin E(2019)Process mining techniques and applications – A systematic mapping studyExpert Systems with Applications: An International Journal10.1016/j.eswa.2019.05.003133:C(260-295)Online publication date: 1-Nov-2019
https://dl.acm.org/doi/10.1016/j.eswa.2019.05.003
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