12.09.2025.
BPIE Research Group – Business Process, Data Science & Intelligent Enterprise Systems
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Description of Activities

The BPIE Research Group (Business Process, Data Science & Intelligent Enterprise Systems) focuses on advancing digital transformation through the integration of business process management, data science methodologies, and intelligent enterprise system design. The research group extends traditional ERP strategies by embedding adaptive, data-centric, and scalable solutions that enhance enterprise-wide decision-making and operational efficiency. Core activities combine advanced analytics, generative technologies, natural language understanding, and enterprise data engineering to address complex challenges in modern enterprise environments.Emphasis is placed on developing interoperable, sustainable, and innovation-driven architectures aligned with both academic research directions and industry needs. The research group investigates intelligent enterprise architectures across multiple layers, including service-oriented and microservice-based systems, cloud-native infrastructures, and data-centric platforms. Research covers business process orchestration, real-time data processing, and adaptive system behavior supported by intelligent automation and advanced data-driven computational approaches. Particular attention is given to data intelligence, explainability, and human-centric system design in enterprise contexts.

By integrating perspectives from both academic research directions and industry needs. The research group investigates the transition to the SAP Business Technology Platform (BTP) to maintain a Clean Core, ensuring that customizations do not hinder system upgrades. By leveraging BTP's side-by-side extensibility and integration services, we examine how AI-driven automation and data orchestration can enhance business agility while keeping the core ERP system stable and upgradeable.

The research group analyzes natural language understanding (NLU) and rapidly evolving GUI technologies including generative AI and LLM powered interfaces, tracking their adoption from mainframe to client server eras and into modern contexts. Barriers to adoption are investigated, particularly where cutting-edge interfaces introduced first in educational settings can accelerate digital transformation and sustainability in business. Cognitive Information Systems and human computer interaction research underpin adaptive decision support environments that cultivate transparency, personalization, and trust through dynamic infocommunication loops. Expertise in process management, software optimization, best practices, and data visualization fuels novel BI and AI driven visual techniques that enhance insight and usability.

Dynamic development spans AI assisted code generation, data mining, and rigorous testing across UI, backend, database, and secure communications. Research also integrates edge computing for real-time performance gains. Advanced analytics and predictive modeling forecast business environment changes, enabling proactive adjustments to software and architecture. Intelligent version control and DevOps practices optimize performance indicators, ensuring alignment with cloud strategy, data privacy, security, and sustainability targets.

Furthermore, the group focuses on agentic Artificial Intelligence and adaptive AutoML frameworks. This includes leveraging LLM reasoning for hyperparameter optimization and drift-aware representation learning in time-series forecasting, ensuring that our models remain robust and explainable across diverse domains such as finance and industrial analytics.

Supporting the group's broader digital transformation goals, this data-driven study on SAP implementation in SMEs examines how small and medium-sized enterprises customize standard SAP ERP systems. Analysis of multiple projects reveals common areas requiring adaptation, such as financial reporting, inventory workflows, and user role management. Identifying these recurring needs helps SMEs anticipate challenges, avoid costly late-stage changes, and streamline implementation. The insights lead to practical guidelines that improve the predictability, efficiency, and alignment of ERP systems with SME business needs.

Research Interests

  • Generative Models and LLM Integration

  • Advanced Analytics & Predictive Modeling

  • Natural Language Understanding

  • Sustainability & Innovation in ERP

  • In‑Memory Database Architectures

  • Enterprise Architecture Best Practices

  • Commercial & Open‑Source Technology Stacks

  • Big Data, Machine Learning, and IoT in Enterprise Contexts

  • Smart Automation & Intelligent Orchestration

  • Cloud‑Native and DevOps‑Driven ERP Deployment

  • Cognitive Information Systems & HCI

  • AI‑ and BI‑Powered Enterprise Functions

  • End‑to‑End Digital Transformation

  • Explainable AI and Low-code technologies in ERP systems

Methods and Service Concepts

  • Advanced Statistical and Machine Learning Techniques

  • Data Intelligence and Visualization

  • In‑Memory and Edge‑Computing Architectures

  • Performance Optimization & Scalability Testing

  • Dynamic Development with AI‑Assisted Code Generation

  • Cognitive System Design and Evaluation

  • Service‑Oriented and Microservice Frameworks

  • Secure, Compliant Communications

Research Staff

  • Tamas Orosz, PhD, Habil, Associate Professor

  • Arafat Md Easin, PhD Candidate

  • Asuah Georgina, PhD Candidate

  • Munkácsi Imre, PhD Candidate

  • Attila Márton Putnoki, PhD Candidate

  • Attila Selmeci, PhD Candidate

  • Bouressace Kawkab, PhD Student

  • Dominik Banka, PhD Student

  • Bochra Jendoubi, PhD Student

  • Ons Saadallah, PhD Student

Projects

  • Agricultural SAP implementation, 2023

  • SAP Manufacturing Execution & Industry 4.0, 2022

  • DATA-EDIH (European Digital Innovation Hub)

Projects Key Researh focus Areas

  • Agentic AI & Intelligent Decision Support

  • Adaptive Data-Driven Modeling & Time-Series Forecasting

Important publications in the field

  • Putnoki, A. M., & Orosz, T. G. (2026). Cognitive and Artificial Intelligence Evaluation Framework. Artificial Intelligence Review.

  • Putnoki, A. M., Philipp, D., & Orosz, T. G. (2026). Cognitive Information System Alignment-Aware Decision-Support Framework. IEEE Access.

  • Putnoki, A. M., & Orosz, T. G. (2026). Cognitive and Artificial Intelligence Evaluation Framework. Artificial Intelligence Review.

  • Putnoki, A. M., Philipp, D., & Orosz, T. G. (2026). Cognitive Information System Alignment-Aware Decision-Support Framework. IEEE Access.

  • Putnoki, A., & Orosz, T. (2026). Cognitive Information Systems and Visual Interfaces: Enhancing Decisions through Personalization. Acta Technica Jaurinensis

  • Munkácsi, I., Orosz, T., & Alexy, M. (2023, October). Implementation Challenges of Industry-Specific ERP System Solutions by Utilizing Best Practices. In The International Conference on Recent Innovations in Computing.

  • Asuah, Georgina ; Arfat, Md Easin ; Tamas, Orosz. “Optimizing SAP Machine Learning-based Solutions through Custom API Integration“, Acta Cybernetica, 2025.

  • Arafat Md, Easin, Kawkab Bouressace, Georgina Asuah, and Andreea Gabriela Tănase. "A Rule-Based Machine Learning Approach for Multi-Class Customer Churn Prediction in O2C Process" In The 19th International Conference on Business Excellence, Springer. 2025

  • Georgina, Asuah. “Teaching SAP Analytics Cloud (SAC): Benefits and Challenges.” In Proceedings of the 1st SAP UA Community Conference: Central and Eastern Europe, pp. 26-29. Budapest, Hungary: ELTE Informatikai Kar, 2024. ISBN 978-963-489-736-1.

  • Kawkab, Bouressace ; Barbara, Hegyi. “Combining SAP with IoT for Real-Time Data Quality Monitoring.” In Proceedings of the 1st SAP UA Community Conference: Central and Eastern Europe, pp. 30-33. Budapest, Hungary: ELTE Informatikai Kar, 2024. ISBN 978-963-489-736-1.

  • Arafat Md, Easin. “Advancing AI-Driven Integration and Analytics through Intelligent Automation and Generative Models.” In Proceedings of the 1st SAP UA Community Conference: Central and Eastern Europe, pp. 55-60. Budapest, Hungary: ELTE Informatikai Kar, 2024. ISBN 978-963-489-736-1

  • Selmeci, A., “Databases, interfaces, data driven planning in ERP environments”, In Proceedings of the 1st SAP UA Community Conference: Central and Eastern Europe, pp. 60-68. Budapest, Hungary: ELTE Informatikai Kar, 2024. ISBN 978-963-489-736-1.

  • Selmeci, A., "Sustainable configuration in an SAP environment", In Proceedings of the 1st SAP UA Community Conference: Central and Eastern Europe, pp. 69-77. Budapest, Hungary: ELTE Informatikai Kar, 2024. ISBN 978-963-489-736-1.

  • Easin, A. M., Sourav, S., & Tamás, O. (2024, September). An intelligent llm-powered personalized assistant for digital banking using langgraph and chain of thoughts. In 2024 IEEE 22nd Jubilee International Symposium on Intelligent Systems and Informatics (SISY) (pp. 625-630). IEEE.

  • Asuah, G., Easin, A. M., & Tamás, O. (2024, July). Optimizing SAP Machine Learning-based Solutions through Custom API Integration. In THE 14TH CONFERENCE OF PHD STUDENTS IN COMPUTER SCIENCE (p. 11).

  • Easin, A. M., & Tamás, O. (2024, July). Enhancing SAP Ecosystem: Harmonizing Open-Source Technologies for Integration and Innovation. In THE 14TH CONFERENCE OF PHD STUDENTS IN COMPUTER SCIENCE (p. 7).

  • Munkácsi, I., Angyalné, M. A., & Orosz, T. G. (2024, July). Optimizing SAP S/4HANA On-Premise with Cloud-Ready Extensions: a Clean-Core system. In THE 14TH CONFERENCE OF PHD STUDENTS IN COMPUTER SCIENCE (p. 51).

  • Putnoki, A. M., & Orosz, T. (2023, October). Artificial Intelligence and Cognitive Information Systems: Revolutionizing Business with Generative Artificial Intelligence and Robotic Process Automation. In The International Conference on Recent Innovations in Computing (pp. 39-70). Singapore: Springer Nature Singapore.

  • Easin Arafat, M., Asuah, G., Saha, S., & Orosz, T. (2023, October). Empowering Real-Time Insights Through LLM, LangChain, and SAP HANA Integration. In The International Conference on Recent Innovations in Computing (pp. 483-495). Singapore: Springer Nature Singapore.

  • Munkácsi, I., Orosz, T., & Alexy, M. (2023, October). Implementation Challenges of Industry-Specific ERP System Solutions by Utilizing Best Practices. In The International Conference on Recent Innovations in Computing (pp. 469-482). Singapore: Springer Nature Singapore.

  • Selmeci, A., 2015, Adaptive Version Control in ERP Environments, In: 10th International Symposium on Applied Informatics and Related Areas (AIS), 2015, Székesfehérvár Hungary, IEEE.

  • Selmeci, A. and Orosz, T., 2015, May. Trends and followers in GUI development for business applications with implications at University Education. In 2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics (pp. 243-251). IEEE.

  • Selmeci, A. and Orosz, T., 2014. Effective end-user interfaces for various business needs. Acta Technica Jaurinensis, 7(2), pp.207-223.

  • Selmeci, A. and Orosz, T., 2014, January. Modification free extension of standard software. In 2014 IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 185-190). IEEE.

  • Orosz, T., 2011, June. Analysis of SAP Development tools and methods. In 2011 15th IEEE International Conference on Intelligent Engineering Systems (pp. 439-443). IEEE.

  • Selmeci, A. and Orosz, T., 2012, September. Usage of SOA and BPM changes the roles and the way of thinking in development. In 2012 IEEE 10th Jubilee International Symposium on Intelligent Systems and Informatics (pp. 265-271). IEEE.

Contact

Tamas Orosz – orosztamas@inf.elte.hu

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