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  • Schultheis A, Jilg D, Malburg L, Bergweiler S and Bergmann R. (2024). Towards Flexible Control of Production Processes: A Requirements Analysis for Adaptive Workflow Management and Evaluation of Suitable Process Modeling Languages. Processes. 10.3390/pr12122714. 12:12. (2714).

    https://www.mdpi.com/2227-9717/12/12/2714

  • Skouti T, Seiger R, Furrer F and Strahringer S. (2024). RBPMN: the value of roles for business process modeling. Software and Systems Modeling (SoSyM). 23:6. (1375-1406). Online publication date: 1-Dec-2024.

    https://doi.org/10.1007/s10270-024-01202-z

  • Monti F, Mathew J, Leotta F, Koschmider A and Mecella M. (2024). On the application of process management and process mining to Industry 4.0. Software and Systems Modeling (SoSyM). 23:6. (1407-1419). Online publication date: 1-Dec-2024.

    https://doi.org/10.1007/s10270-024-01175-z

  • Karthika R, Deborah L, Zheng W, Alqahtani F, Tolba A, Krishnan B and Bansal R. (2024). Semantic-Rich Recommendation System for Medical Emergency Response System. International Journal on Semantic Web & Information Systems. 20:1. (1-18). Online publication date: 9-Nov-2024.

    https://doi.org/10.4018/IJSWIS.341231

  • Jayashree K, Muralidharan S, Sathya V, Rajakumaran M and Nalayini C. (2024). Digital Twins Tools and Technologies in Smart Manufacturing. Artificial Intelligence‐Enabled Digital Twin for Smart Manufacturing. 10.1002/9781394303601.ch7. (125-141). Online publication date: 15-Oct-2024.

    https://onlinelibrary.wiley.com/doi/10.1002/9781394303601.ch7

  • Lespérance Y, De Giacomo G, Rostamigiv M and Khan S. Abstraction of situation calculus concurrent game structures. Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence. (10624-10634).

    https://doi.org/10.1609/aaai.v38i9.28933

  • Alhozaimy S, Menascé D and Albanese M. (2023). Design and modeling of moving target defense in workflow-based applications. Cluster Computing. 10.1007/s10586-023-03998-9. 27:1. (945-958). Online publication date: 1-Feb-2024.

    https://link.springer.com/10.1007/s10586-023-03998-9

  • Monti F, Silo L, Favorito M, De Giacomo G, Leotta F and Mecella M. Orchestration of Services in Smart Manufacturing through Automated Synthesis. IEEE Transactions on Services Computing. 10.1109/TSC.2024.3495521. (1-14).

    https://ieeexplore.ieee.org/document/10748384/

  • Leotta F, Monti F and Silo L. Services in Industry 4.0. Modeling and Composition for Agile Supply Chains. Service-Oriented Computing – ICSOC 2023 Workshops. (350-357).

    https://doi.org/10.1007/978-981-97-0989-2_31

  • Malburg L, Klein P and Bergmann R. (2023). Converting semantic web services into formal planning domain descriptions to enable manufacturing process planning and scheduling in industry 4.0. Engineering Applications of Artificial Intelligence. 10.1016/j.engappai.2023.106727. 126. (106727). Online publication date: 1-Nov-2023.

    https://linkinghub.elsevier.com/retrieve/pii/S0952197623009119

  • Banihashemi B, De Giacomo G and Lespérance Y. Abstraction of nondeterministic situation calculus action theories. Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. (3112-3122).

    https://doi.org/10.24963/ijcai.2023/347

  • De Giacomo G, Favorito M, Leotta F, Mecella M and Silo L. (2023). Digital twin composition in smart manufacturing via Markov decision processes. Computers in Industry. 10.1016/j.compind.2023.103916. 149. (103916). Online publication date: 1-Aug-2023.

    https://linkinghub.elsevier.com/retrieve/pii/S0166361523000660

  • Malburg L, Hoffmann M and Bergmann R. (2023). Applying MAPE-K control loops for adaptive workflow management in smart factories. Journal of Intelligent Information Systems. 61:1. (83-111). Online publication date: 1-Aug-2023.

    https://doi.org/10.1007/s10844-022-00766-w

  • Guldner A, Hoffmann M, Lohr C, Machhamer R, Malburg L, Morgen M, Rodermund S, Schäfer F, Schaupeter L, Schneider J, Theusch F, Bergmann R, Dartmann G, Kuhn N, Naumann S, Timm I, Vette-Steinkamp M and Weyers B. (2023). A framework for AI-based self-adaptive cyber-physical process systems. it - Information Technology. 10.1515/itit-2023-0001. 65:3. (113-128). Online publication date: 28-Jun-2023.. Online publication date: 1-Jun-2023.

    https://www.degruyter.com/document/doi/10.1515/itit-2023-0001/html

  • Mezni H. (2023). Web service adaptation: A decade’s overview. Computer Science Review. 10.1016/j.cosrev.2023.100535. 48. (100535). Online publication date: 1-May-2023.

    https://linkinghub.elsevier.com/retrieve/pii/S1574013723000023

  • Mangler J, Grüger J, Malburg L, Ehrendorfer M, Bertrand Y, Benzin J, Rinderle-Ma S, Serral Asensio E and Bergmann R. (2023). DataStream XES Extension: Embedding IoT Sensor Data into Extensible Event Stream Logs. Future Internet. 10.3390/fi15030109. 15:3. (109).

    https://www.mdpi.com/1999-5903/15/3/109

  • Monti F, Silo L, Leotta F and Mecella M. (2023). Services in Smart Manufacturing: Comparing Automated Reasoning Techniques for Composition and Orchestration. Service-Oriented Computing. 10.1007/978-3-031-45728-9_5. (69-83).

    https://link.springer.com/10.1007/978-3-031-45728-9_5

  • Malburg L, Brand F and Bergmann R. (2023). Adaptive Management of Cyber-Physical Workflows by Means of Case-Based Reasoning and Automated Planning. Enterprise Design, Operations, and Computing. EDOC 2022 Workshops. 10.1007/978-3-031-26886-1_5. (79-95).

    https://link.springer.com/10.1007/978-3-031-26886-1_5

  • Alkhabbas F, Alsadi M, Alawadi S, Awaysheh F, Kebande V and Moghaddam M. (2022). ASSERT: A Blockchain-Based Architectural Approach for Engineering Secure Self-Adaptive IoT Systems. Sensors. 10.3390/s22186842. 22:18. (6842).

    https://www.mdpi.com/1424-8220/22/18/6842

  • Song W, Chen F, Jacobsen H and Zhang C. Identifying a Minimum Sequence of High-Level Changes Between Workflows. IEEE Transactions on Services Computing. 10.1109/TSC.2021.3054036. 15:4. (2425-2438).

    https://ieeexplore.ieee.org/document/9335480/

  • Elahraf A, Afzal A, Akhtar A, Shafiq B, Vaidya J, Shamail S and Adam N. A Framework for Dynamic Composition and Management of Emergency Response Processes. IEEE Transactions on Services Computing. 10.1109/TSC.2020.3030211. 15:4. (2018-2031).

    https://ieeexplore.ieee.org/document/9220846/

  • Seiger R, Malburg L, Weber B and Bergmann R. (2022). Integrating process management and event processing in smart factories: A systems architecture and use cases. Journal of Manufacturing Systems. 10.1016/j.jmsy.2022.05.012. 63. (575-592). Online publication date: 1-Apr-2022.

    https://linkinghub.elsevier.com/retrieve/pii/S0278612522000814

  • Hoffmann M, Malburg L and Bergmann R. (2022). ProGAN: Toward a Framework for Process Monitoring and Flexibility by Change via Generative Adversarial Networks. Business Process Management Workshops. 10.1007/978-3-030-94343-1_4. (43-55).

    https://link.springer.com/10.1007/978-3-030-94343-1_4

  • Voorberg S, Eshuis R, van Jaarsveld W and van Houtum G. (2021). Decisions for information or information for decisions? Optimizing information gathering in decision-intensive processes. Decision Support Systems. 151:C. Online publication date: 1-Dec-2021.

    https://doi.org/10.1016/j.dss.2021.113632

  • Chika Eleonu H. A Framework and Tool Support for Managing a Family of Business Process Variants. Proceedings of the 4th International Conference on Information Science and Systems. (81-89).

    https://doi.org/10.1145/3459955.3460604

  • Marrella A and Chakraborti T. (2021). Applications of Automated Planning for Business Process Management. Business Process Management. 10.1007/978-3-030-85469-0_4. (30-36).

    https://link.springer.com/10.1007/978-3-030-85469-0_4

  • Simões R, Melo G, Brito e Abreu F and Oliveira T. (2021). Towards Understanding Quality-Related Characteristics in Knowledge-Intensive Processes - A Systematic Literature Review. Quality of Information and Communications Technology. 10.1007/978-3-030-85347-1_15. (197-207).

    https://link.springer.com/10.1007/978-3-030-85347-1_15

  • Heinrich B, Schiller A, Schön D and Szubartowicz M. (2020). Adapting process models via an automated planning approach. Journal of Decision Systems. 10.1080/12460125.2020.1800976. 29:4. (223-259). Online publication date: 1-Oct-2020.

    https://www.tandfonline.com/doi/full/10.1080/12460125.2020.1800976

  • Malburg L, Seiger R, Bergmann R and Weber B. (2020). Using Physical Factory Simulation Models for Business Process Management Research. Business Process Management Workshops. 10.1007/978-3-030-66498-5_8. (95-107).

    http://link.springer.com/10.1007/978-3-030-66498-5_8

  • Chakraborti T, Agarwal S, Khazaeni Y, Rizk Y and Isahagian V. (2020). D3BA: A Tool for Optimizing Business Processes Using Non-deterministic Planning. Business Process Management Workshops. 10.1007/978-3-030-66498-5_14. (181-193).

    http://link.springer.com/10.1007/978-3-030-66498-5_14

  • Marrella A, Mecella M, Pernici B and Plebani P. (2019). A design-time data-centric maturity model for assessing resilience in multi-party business processes. Information Systems. 10.1016/j.is.2018.11.002. 86. (62-78). Online publication date: 1-Dec-2019.

    https://linkinghub.elsevier.com/retrieve/pii/S0306437917306063

  • Agostinelli S, Maggi F, Marrella A and Mecella M. (2019). Verifying Petri Net-Based Process Models using Automated Planning 2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop (EDOCW). 10.1109/EDOCW.2019.00021. 978-1-7281-4598-3. (44-53).

    https://ieeexplore.ieee.org/document/8907279/

  • Catarci T, Leotta F, Marrella A, Mecella M and Sharf M. (2019). Process-Aware Enactment of Clinical Guidelines through Multimodal Interfaces. Computers. 10.3390/computers8030067. 8:3. (67).

    https://www.mdpi.com/2073-431X/8/3/67

  • Leotta F, Marrella A and Mecella M. IoT for BPMers. Challenges, Case Studies and Successful Applications. Business Process Management. (16-22).

    https://doi.org/10.1007/978-3-030-26619-6_3

  • Tsigkanos C, Murturi I and Dustdar S. Dependable Resource Coordination on the Edge at Runtime. Proceedings of the IEEE. 10.1109/JPROC.2019.2917314. 107:8. (1520-1536).

    https://ieeexplore.ieee.org/document/8733059/

  • Mass J, Chang C and Srirama S. (2019). Edge Process Management: A Case Study on Adaptive Task Scheduling in Mobile IoT. Internet of Things. 10.1016/j.iot.2019.100051. (100051). Online publication date: 1-Apr-2019.

    https://linkinghub.elsevier.com/retrieve/pii/S2542660518300775

  • Seiger R, Huber S, Heisig P and Aβmann U. (2019). Toward a framework for self-adaptive workflows in cyber-physical systems. Software and Systems Modeling (SoSyM). 18:2. (1117-1134). Online publication date: 1-Apr-2019.

    https://doi.org/10.1007/s10270-017-0639-0

  • Sid I, Reichert M and Ghomari A. (2019). Enabling flexible task compositions, orders and granularities for knowledge-intensive business processes. Enterprise Information Systems. 10.1080/17517575.2018.1556815. 13:3. (376-423). Online publication date: 16-Mar-2019.

    https://www.tandfonline.com/doi/full/10.1080/17517575.2018.1556815

  • Graja I, Kallel S, Guermouche N, Cheikhrouhou S and Hadj Kacem A. (2019). Modelling and verifying time-aware processes for cyber-physical environments. IET Software. 10.1049/iet-sen.2018.5034. 13:1. (36-48). Online publication date: 1-Feb-2019.

    https://digital-library.theiet.org/content/journals/10.1049/iet-sen.2018.5034

  • BICOCCHI N, CABRI G, MANDREOLI F and MECELLA M. (2019). Dynamic digital factories for agile supply chains: An architectural approach. Journal of Industrial Information Integration. 10.1016/j.jii.2019.02.001. Online publication date: 1-Feb-2019.

    https://linkinghub.elsevier.com/retrieve/pii/S2452414X18301274

  • Seiger R, Heisig P and Aßmann U. (2019). Retrofitting of Workflow Management Systems with Self-X Capabilities for Internet of Things. Business Process Management Workshops. 10.1007/978-3-030-11641-5_34. (433-444).

    https://link.springer.com/10.1007/978-3-030-11641-5_34

  • Marrella A. (2018). Automated Planning for Business Process Management. Journal on Data Semantics. 10.1007/s13740-018-0096-0.

    http://link.springer.com/10.1007/s13740-018-0096-0

  • Babar Z, Lapouchnian A, Yu E, Chan A and Carbajales S. (2018). Modeling and Analyzing Process Architecture for Context-Driven Adaptation: Designing Cognitively-Enhanced Business Processes for Enterprises 2018 IEEE 22nd International Enterprise Distributed Object Computing Conference (EDOC). 10.1109/EDOC.2018.00018. 978-1-5386-4139-2. (58-67).

    https://ieeexplore.ieee.org/document/8536149/

  • Marrella A and Catarci T. Measuring the Learnability of Interactive Systems Using a Petri Net Based Approach. Proceedings of the 2018 Designing Interactive Systems Conference. (1309-1319).

    https://doi.org/10.1145/3196709.3196744

  • Seiger R, Assmann U and Huber S. (2018). A Case Study for Workflow-Based Automation in the Internet of Things 2018 IEEE International Conference on Software Architecture Companion (ICSA-C). 10.1109/ICSA-C.2018.00011. 978-1-5386-6585-5. (11-18).

    https://ieeexplore.ieee.org/document/8432166/

  • Marrella A. (2018). What Automated Planning Can Do for Business Process Management. Business Process Management Workshops. 10.1007/978-3-319-74030-0_1. (7-19).

    http://link.springer.com/10.1007/978-3-319-74030-0_1

  • Marrella A and Lespérance Y. (2017). A planning approach to the automated synthesis of template-based process models. Service Oriented Computing and Applications. 11:4. (367-392). Online publication date: 1-Dec-2017.

    https://doi.org/10.1007/s11761-017-0215-z

  • Leoni M and Marrella A. (2017). Aligning Real Process Executions and Prescriptive Process Models through Automated Planning. Expert Systems with Applications: An International Journal. 82:C. (162-183). Online publication date: 1-Oct-2017.

    https://doi.org/10.1016/j.eswa.2017.03.047

  • Giacomo G, Maggi F, Marrella A and Patrizi F. On the disruptive effectiveness of automated planning for LTL-based trace alignment. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. (3555-3561).

    /doi/10.5555/3298023.3298085

  • Marrella A, Mecella M, Pernici B and Plebani P. (2017). Design-time Models for Resiliency. Conceptual Modeling Perspectives. 10.1007/978-3-319-67271-7_8. (105-120).

    http://link.springer.com/10.1007/978-3-319-67271-7_8