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Business Process Management Based on Big Data Analytics
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Business Process Management Based on Big Data Analytics

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Social Science".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 670

Special Issue Editors

Dr. Jorge Oliveira e Sá

E-Mail Website
Guest Editor
ALGORITMI Research Centre, Department of Information Systems, University of Minho, 4800-058 Guimaraes, Portugal
Interests: business intelligence, business process management (BPM), data analytics, information visualization, big data
Dr. José Luis Mota Pereira

E-Mail Website
Guest Editor
ALGORITMI Research Centre, Department of Information Systems, University of Minho, 4800-058 Guimaraes, Portugal
Interests: organizational decision support, business process improvement and big data solutions

Special Issue Information

Dear Colleagues,

This Special Issue of Systems focuses on business process management (BPM) based on big data analytics. This is an emerging topic that leverages the vast amounts of data generated by modern organizations to optimize, monitor, and improve business processes. By integrating big data analytics into BPM, organizations can extract valuable insights from structured and unstructured data to enable better decision making, process automation, continuous process improvement, and operational efficiency by tracking key performance indicators (KPIs) in real time, enabling proactive adjustments and more precise alignment with strategic goals.

Integrating artificial intelligence (AI) into this synergy amplifies its impact by enabling advanced predictive modeling, intelligent process automation, and improved decision making, as well as anticipating process inefficiencies and uncovering hidden patterns in data that would otherwise go unnoticed. As organizations across industries embrace AI, it is increasingly seen as a transformative tool that enables greater agility, customer focus, and competitiveness in today's data-driven landscape.

For this Special Issue, we are seeking papers that are relevant to the BPM community, as well as to big data analytics researchers and practitioners in various industries. Papers are sought in the following areas (but are not limited to these):

  • Real-time process monitoring and optimization;
  • Predictive analytics in BPM;
  • Process mining and big data;
  • Data-driven decision making;
  • BPM automation and AI integration;
  • Customer-centric process personalization;
  • Security and privacy in data-driven BPM.

Dr. Jorge Oliveira e Sá
Dr. José Luis Mota Pereira
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • business process management
  • big data
  • decision making
  • data analytics
  • AI integration

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Published Papers (1 paper)

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Research

27 pages, 3702 KiB  
Article
Domain Knowledge-Enhanced Process Mining for Anomaly Detection in Commercial Bank Business Processes
by Yanying Li, Zaiwen Ni and Binqing Xiao
Systems 2025, 13(7), 545; https://doi.org/10.3390/systems13070545 - 4 Jul 2025
Viewed by 237
Abstract
Process anomaly detection in financial services systems is crucial for operational compliance and risk management. However, traditional process mining techniques frequently neglect the detection of significant low-frequency abnormalities due to their dependence on frequency and the inadequate incorporation of domain-specific knowledge. Therefore, we [...] Read more.
Process anomaly detection in financial services systems is crucial for operational compliance and risk management. However, traditional process mining techniques frequently neglect the detection of significant low-frequency abnormalities due to their dependence on frequency and the inadequate incorporation of domain-specific knowledge. Therefore, we develop an enhanced process mining algorithm by incorporating a domain-specific follow-relationship matrix derived from standard operating procedures (SOPs). We empirically evaluated the effectiveness of the proposed algorithm based on real-world event logs from a corporate account-opening process conducted from January to December 2022 in a Chinese commercial bank. Additionally, we employed large language models (LLMs) for root cause analysis and process optimization recommendations. The empirical results demonstrate that the E-Heuristic Miner significantly outperforms traditional machine learning methods and process mining algorithms in process anomaly detection. Furthermore, the integration of LLMs provides promising capabilities in semantic reasoning and offers explainable optimization suggestions, enhancing decision-making support in complex financial scenarios. Our study significantly improves the precision of process anomaly detection in financial contexts by incorporating banking-specific domain knowledge into process mining algorithms. Meanwhile, it extends theoretical boundaries and the practical applicability of process mining in intelligent, semantic-aware financial service management. Full article
(This article belongs to the Special Issue Business Process Management Based on Big Data Analytics)
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