The Internet-of-Things Meets Business Process Management. A Manifesto
Open accessAbstract
The Internet of Things (IoT) refers to a network of connected devices that collects and exchanges data through the Internet. These things can be artificial or natural and interact as autonomous agents that form a complex system. In turn, business process management (BPM) was established to analyze, discover, design, implement, execute, monitor, and evolve collaborative business processes within and across organizations. While the IoT and BPM have been regarded as separate topics in research and in practice, we strongly believe that, on the one hand, the management of IoT applications will greatly benefit from BPM concepts, methods, and technologies. On the other hand, the IoT poses challenges that will require enhancements and extensions of the current state of the art in the BPM field. In this article, we question the extent to which these two paradigms can be combined, and we discuss emerging challenges and intersections from a research and practitioner's point of view in terms of complex software systems development.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Although IoT data quality is acknowledged as a problem in IoT-enhanced BPM, there is little literature proposing methods to address data quality in this context.
“Although IoT data quality is acknowledged as a problem in IoT-enhanced BPM [11,31,36], there is little literature proposing methods to address data quality in this context.”
From Case-Based Data Quality Management for IoT Logs: A Case Study Focusing on Detection of Data Quality Issues (2025) · cites this paper as [31]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Part of this paper’s reference list could not be split into single entries.
Internet of Things (IoT): A vision, architectural elements, and future directions
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013) · Future Generation Computer Systems · doi:10.1016/j.future.2013.01.010 · Cited by 12,293 · see citing works
A model for types and levels of human interaction with automation
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/3468.844354 · Cited by 4,195 · cited 1× · see citing works
“Currently, there is no universal way to represent these levels of autonomy or to resolve conflicts originating from this distinction [17], [18].”
BackgroundFundamentals of Business Process Management
Dumas, M., Rosa, M. L., Mendling, J., & Reijers, H. A. (2018) · Springer eBooks · doi:10.1007/978-3-662-56509-4 · Cited by 1,832 · see citing works
Smart objects as building blocks for the Internet of things
Kortuem, G., Kawsar, F., Sundramoorthy, V., & Fitton, D. (2009) · IEEE Internet Computing · doi:10.1109/mic.2009.143 · Cited by 1,477 · see citing works
Discrimination-aware data mining
Pedreshi, D., Ruggieri, S., & Turini, F. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1401959 · Cited by 676 · see citing works
Privacy by Design — Principles of Privacy-Aware Ubiquitous Systems
Langheinrich, M. (2001) · Lecture notes in computer science · doi:10.1007/3-540-45427-6_23 · Cited by 544 · see citing works
Schaar, P. (2010) · Identity in the Information Society · doi:10.1007/s12394-010-0055-x · Cited by 390 · see citing works
Batini, C., & Scannapieco, M. (2016) · Data-centric systems and applications · doi:10.1007/978-3-319-24106-7 · Cited by 390 · see citing works
An IoT-Oriented Data Storage Framework in Cloud Computing Platform
Jiang, L., Xu, L. D., Cai, H., Jiang, Z., Bu, F., & Xu, B. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2306384 · Cited by 352 · see citing works
Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches
Di Ciccio, C., Marrella, A., & Russo, A. (2015) · Journal on Data Semantics · doi:10.1007/s13740-014-0038-4 · Cited by 312 · see citing works
Self-citationReference Modeling for Business Systems Analysis
Fettke, P., & Loos, P. (2007) · IGI Global eBooks · doi:10.4018/978-1-59904-054-7 · Cited by 231 · see citing works
Elastic Business Process Management: State of the art and open challenges for BPM in the cloud
Schulte, S., Janiesch, C., Venugopal, S., Weber, I. M., & Hoenisch, P. (2014) · Future Generation Computer Systems · doi:10.1016/j.future.2014.09.005 · Cited by 124 · see citing works
Design Blueprint for Stress-Sensitive Adaptive Enterprise Systems
Adam, M. T. P., Gimpel, H., Maedche, A., & Riedl, R. (2017) · Business & Information Systems Engineering · doi:10.1007/s12599-016-0451-3 · Cited by 78 · see citing works
Beyond process monitoring: a proof‐of‐concept of event‐driven business activity management
Janiesch, C., Matzner, M., & Müller, O. (2012) · Business Process Management Journal · doi:10.1108/14637151211253765 · Cited by 73 · see citing works
Socially Responsible Decision Making by Autonomous Agents
Kalenka, S., & Jennings, N. R. (1999) · Springer eBooks · doi:10.1007/978-94-017-1070-1_9 · Cited by 72 · see citing works
Steinau, S., Marrella, A., Andrews, K., Leotta, F., Mecella, M., & Reichert, M. (2018) · Software & Systems Modeling · doi:10.1007/s10270-018-0695-0 · Cited by 67 · see citing works
The Internet-of-Things Meets Business Process Management: Mutual Benefits and ChallengesPreprint
Janiesch, C., Koschmider, A., Mecella, M., Weber, B., Burattin, A., Ciccio, C. D., Gal, A., Kannengiesser, U., Mannhardt, F., Mendling, J., et al. (2017) · arXiv (Cornell University) · Cited by 61 · see citing works
The ROAD from Sensor Data to Process Instances via Interaction Mining
Senderovich, A., Solti, A., Gal, A., Mendling, J., & Mandelbaum, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39696-5_16 · Cited by 59 · see citing works
Supporting adaptiveness of cyber-physical processes through action-based formalisms
Marrella, A., Mecella, M., & Sardiña, S. (2017) · AI Communications · doi:10.3233/aic-170748 · Cited by 32 · see citing works
Visual process maps: a visualization tool for discovering habits in smart homes
Leotta, F., Mecella, M., & Sora, D. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01211-7 · Cited by 27 · see citing works
Optimizing the Performance of Automated Business Processes Executed on Virtualized Infrastructure
Janiesch, C., Weber, I. M., Kuhlenkamp, J., & Menzel, M. (2014) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2014.474 · Cited by 15 · see citing works
A Taxonomy of Autonomy in Multiagent Organisation
Schillo, M., & Fischer, K. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-25928-2_6 · Cited by 14 · see citing works
NFC-Based Task Enactment for Automatic Documentation of Treatment Processes
Stertz, F., Mangler, J., & Rinderle‐Ma, S. (2017) · Lecture notes in business information processing · doi:10.1007/978-3-319-59466-8_3 · Cited by 5 · see citing works
A Classification Framework for IoT Scenarios
Mandal, S., Hewelt, M., Oestreich, M., & Weske, M. (2019) · Lecture notes in business information processing · doi:10.1007/978-3-030-11641-5_36 · Cited by 5 · see citing works
Robust Execution of Mobile Activities in Process-Aware Information Systems
Pryss, R. C., & Reichert, M. (2016) · International Journal of Information System Modeling and Design · doi:10.4018/ijismd.2016100104 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- [5][5] K. Ashton et al. , 'That 'internet of things' thing,' RFID journal , vol. 22, no. 7, pp. 97-114, 2009. -
- [7][7] J. Holler, V. Tsiatsis, C. Mulligan, S. Avesand, S. Karnouskos, and D. Boyle, 'From Machine-to-Machine to the Internet of Things: Introduction to a New Age of Intelligence,' Academic Press, Inc., USA, 2014. -
- [9][9] A. Nordrum et al. , 'Popular Internet of Things forecast of 50 billion devices by 2020 is outdated,' IEEE spectrum , vol. 18, no. 3, 2016. 3 Cf. Article 8 of 'European Convention on Human Rights', http://www. echr.coe.int/Documents/Convention ENG.pdf and Article 12 of the 'Universal Declaration of Human Rights', http://www.ohchr.org/EN/UDHR/Documents/ UDHR Translations/eng.pdf. -
- [11][11] W. Van Der Aalst, Process mining: discovery, conformance and enhancement of business processes . Springer, 2011, vol. 2. -
Cited by
Sorted by citations
Modelling and executing IoT-enhanced business processes through BPMN and microservices
Valderas, P., Torres, V., & Serral, E. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.111139 · Cited by 71 · see citing works
Process Prediction with Digital Twins
Brockhoff, T., Heithoff, M., Koren, I., Michael, J., Pfeiffer, J., Rumpe, B., Uysal, M. S., van der Aalst, W. M. P., & Wortmann, A. (2021) · 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) · doi:10.1109/models-c53483.2021.00032 · Cited by 59 · see citing works
Seiger, R., Malburg, L., Weber, B., & Bergmann, R. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.05.012 · Cited by 59 · see citing works
HoloFlows: modelling of processes for the Internet of Things in mixed reality
Seiger, R., Kühn, R., Korzetz, M., & Aßmann, U. (2021) · Software & Systems Modeling · doi:10.1007/s10270-020-00859-6 · Cited by 50 · see citing works
“Despite showing high potential for improved comfort and efficiency, more complex automated routines and processes are only rarely implemented in smart environments [3].”
BackgroundMethodInfluentialTowards IoT-driven Process Event Log Generation for Conformance Checking in Smart Factories
Seiger, R., Zerbato, F., Burattin, A., García‐Bañuelos, L., & Weber, B. (2020) · IEEE International Enterprise Distributed Object Computing Conference Workshops (EDOCW) · doi:10.1109/edocw49879.2020.00016 · Cited by 49 · see citing works
“Indeed, such IoT applications are often built upon proprietary control software relying on non-standardized interfaces that thus hinder the use of process-enabled enactment systems [1], [5].”
BackgroundMethodInfluentialAmani, A. M., Tayebi, L., Abbasi, M., Vaez, A., Kamyab, H., Chelliapan, S., & Vafa, E. (2023) · ACS Omega · doi:10.1021/acsomega.3c06590 · Cited by 41 · see citing works
Compagnucci, I., Corradini, F., Fornari, F., Polini, A., Re, B., & Tiezzi, F. (2022) · Software & Systems Modeling · doi:10.1007/s10270-022-01049-2 · Cited by 40 · see citing works
“However, as suggested by the Business Processes Meet the Internet of Things group and its manifest, integrating IoT and business processes poses challenges to the research community that, among various things, ask for appropriate modeling notations [61].”
BackgroundMethodFoundations of Process Event Data
Weerdt, J. D., & Wynn, M. T. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-031-08848-3_6 · Cited by 37 · see citing works
An Interactive Method for Detection of Process Activity Executions from IoT Data
Seiger, R., Franceschetti, M., & Weber, B. (2023) · Future Internet · doi:10.3390/fi15020077 · Cited by 36 · see citing works
Process-aware IIoT Knowledge Graph: A semantic model for Industrial IoT integration and analytics
Diamantini, C., Mircoli, A., Potena, D., & Storti, E. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.10.003 · Cited by 36 · see citing works
DataStream XES Extension: Embedding IoT Sensor Data into Extensible Event Stream Logs
Mangler, J., Grüger, J., Malburg, L., Ehrendorfer, M., Bertrand, Y., Benzin, J.-V., Rinderle-Ma, S., Serral Asensio, E., & Bergmann, R. (2023) · Future Internet · doi:10.3390/fi15030109 · Cited by 33 · see citing works
“The foundations of this extension are based in the challenge C3 of the BPM&IoT Manifesto [4], which formulates the requirement of a “connection of analytical processes with IoT”.”
MethodInfluentialSelf-citationOn the adoption of blockchain for business process monitoring
Ciccio, C. D., Meroni, G., & Plebani, P. (2022) · Software & Systems Modeling · doi:10.1007/s10270-021-00959-x · Cited by 33 · see citing works
Digital Twins of Business Processes: A Research Manifesto
Fornari, F., Compagnucci, I., Donato, M. C. D., Bertrand, Y., Beyel, H. H., Carrión, E., Franceschetti, M., Groher, W., Grüger, J., Kilic, E., et al. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101477 · Cited by 37 · see citing works
“The more a system incorporates human factors, the more complex it becomes to define DTs with a high level of fidelity due to the difficulty of representing the variability of humans in terms of experience, behavior, fatigue, and reactions to the situations, which can impact the process [4, 52, 102].”
InfluentialOn the Interplay Between Business Process Management and Internet-of-Things
Luzi, F. D., Leotta, F., Marrella, A., & Mecella, M. (2024) · Business & Information Systems Engineering · doi:10.1007/s12599-024-00859-6 · Cited by 30 · see citing works
“…to systematically evaluate the literature on IoT-aware BPM concerning the phases of the BP life cycle covered by the different approaches, the specific topics addressed, the application domains involved, and the possibility to tackle the research challenges identified by Janiesch et al. (2020).”
InfluentialUsing Physical Factory Simulation Models for Business Process Management Research
Malburg, L., Seiger, R., Bergmann, R., & Weber, B. (2020) · Lecture notes in business information processing · doi:10.1007/978-3-030-66498-5_8 · Cited by 29 · see citing works
“The FT factory is a perfect example of a complex IoT system, which is both event-driven due to large amount of sensors and process-based (C13 : Bridging the Gap between Event-based and Process-based Systems [8]).”
BackgroundMethodInfluentialInteractive Process Automation based on lightweight object detection in manufacturing processes
Mangat, A. S., Mangler, J., & Rinderle‐Ma, S. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103482 · Cited by 29 · see citing works
Towards an Interdisciplinary Development of IoT-Enhanced Business Processes
Valderas, P., Torres, V., & Serral, E. (2022) · Business & Information Systems Engineering · doi:10.1007/s12599-022-00770-y · Cited by 30 · see citing works
“They provide Business Processes (BPs) with real-time data to take more informed decisions based on context (Janiesch et al. 2020).”
BackgroundBPMNE4IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes
Kirikkayis, Y., Gallik, F., Winter, M. R., & Reichert, M. (2023) · Future Internet · doi:10.3390/fi15030090 · Cited by 28 · see citing works
Relevance of Green Manufacturing and IoT in Industrial Transformation and Marketing Management
Naim, A., Muniasamy, A., Clementking, A., & Rajkumar, R. (2022) · Green energy and technology · doi:10.1007/978-3-030-96429-0_19 · Cited by 25 · see citing works
Process mining on sensor data: a review of related works
Brzychczy, E., Aleknonytė‐Resch, M., Janssen, D., & Koschmider, A. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-024-02297-y · Cited by 21 · see citing works
Corradini, F., Fedeli, A., Fornari, F., Polini, A., & Re, B. (2022) · Software & Systems Modeling · doi:10.1007/s10270-022-01026-9 · Cited by 20 · see citing works
“As mentioned in [19], proposing a business process oriented solution, it is clearly necessary to enrich the adopted modelling language with IoT-aware constructs to include in the business processes model aspects related to the IoT domain.”
BackgroundInfluentialA Characterisation of Ambiguity in BPM
Franceschetti, M., Seiger, R., López, H. A., Burattin, A., García‐Bañuelos, L., & Weber, B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47262-6_15 · Cited by 18 · see citing works
“To this end, the recent idea of integrating Internet of Things (IoT) technologies with BPM to collect rich datasets (cf. [26]) shows great potential for disambiguation and is worth investigating.”
BackgroundProcess Mining of Mining Processes: Analyzing Longwall Coal Excavation Using Event Data
Brzychczy, E., Żuber, A., & van der Aalst, W. M. P. (2024) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3348496 · Cited by 18 · see citing works
“It is worth emphasizing that event abstraction based on sensor data is recognized as one of the main challenges in BPM and PM domains [6].”
BackgroundFloBP: a model-driven approach for developing and executing IoT-enhanced business processes
Fedeli, A., Fornari, F., Polini, A., Re, B., Torres, V., & Valderas, P. (2024) · Software & Systems Modeling · doi:10.1007/s10270-024-01150-8 · Cited by 19 · see citing works
de Leoni, M., & Pellattiero, L. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-030-94343-1_31 · Cited by 17 · see citing works
AI-Empowered Process Mining for Complex Application Scenarios: Survey and Discussion
Folino, F., & Pontieri, L. (2021) · Journal on Data Semantics · doi:10.1007/s13740-021-00121-2 · Cited by 17 · see citing works
Towards a Comprehensive BPMN Extension for Modeling IoT-Aware Processes in Business Process Models
Kirikkayis, Y., Gallik, F., & Reichert, M. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-031-05760-1_47 · Cited by 17 · see citing works
“Many embedded components are equipped with sensors and actuators that enable collection of environmental data (sensors) as well as physical responses to specific events (actuators) [2].”
BackgroundUnsupervised Segmentation of Smart Home Logs for Human Habit Discovery
Esposito, L., Leotta, F., Mecella, M., & Veneruso, S. V. (2022) · International Conference on Intelligent Environments (IE) · doi:10.1109/ie54923.2022.9826776 · Cited by 17 · see citing works
Modeling, Executing and Monitoring IoT-Aware Processes with BPM Technology
Gallik, F., Kirikkayis, Y., & Reichert, M. (2022) · International Conference on Service Science (ICSS) · doi:10.1109/icss55994.2022.00023 · Cited by 16 · see citing works
“Janiesch et al. [4] have identified fundamental research challenges for such an architecture including the improvement of resource monitoring, which shall also be addressed in this paper.”
BackgroundHakim, I. M., Singgih, M. L., & Gunarta, I. K. (2023) · Sustainability · doi:10.3390/su15042909 · Cited by 16 · see citing works
“Companies that have modules in implementing IoT to support the company’s business activities require employees who are experts in this field of carrying out the process of setting up, evaluating, and periodically monitoring business activities in the company [40].”
BackgroundOn the application of process management and process mining to Industry 4.0
Monti, F., Mathew, J. G., Leotta, F., Koschmider, A., & Mecella, M. (2024) · Software & Systems Modeling · doi:10.1007/s10270-024-01175-z · Cited by 14 · see citing works
“The IoT-Meets-BPM Manifesto [8] proposes how business process management (BPM) and the IoT can benefit from each other and be effectively combined (see Fig.1).”
InfluentialEnabling security risk assessment and management for business process models
Rosado, D. G., Sanchez, L. E., Varela‐Vaca, Á. J., Santos-Olmo, A., Gómez-López, M. T., Gasca, R. M., & Fernández‐Medina, E. (2024) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2024.103829 · Cited by 14 · see citing works
Shelf-life assessment techniques for fruit and vegetables: Recent trends and future prospects
ElManawy, A. I., Ali, M., Ghazal, A. F., Li, F., Li, X., & Zhang, X. (2025) · Postharvest Biology and Technology · doi:10.1016/j.postharvbio.2025.113521 · Cited by 14 · see citing works
Business process redesign towards IoT-enabled context-awareness: the case of a Chinese bulk port
Song, R., Cui, W., Vanthienen, J., Huang, L., & Wang, Y. (2022) · Business Process Management Journal · doi:10.1108/bpmj-09-2021-0569 · Cited by 14 · see citing works
BRIBOT: Towards a Service-Based Methodology for Bridging Business Processes and IoT Big Data
Gruhn, V., Han, Y., Hesenius, M., Reichert, M., Wang, G. G., Yu, J., & Zhang, L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91431-8_37 · Cited by 13 · see citing works
Generating Synthetic Sensor Event Logs for Process Mining
Zisgen, Y., Janssen, D., & Koschmider, A. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-031-07481-3_15 · Cited by 12 · see citing works
A Practitioner’s View on Process Mining Adoption, Event Log Engineering and Data Challenges
Accorsi, R., & Lebherz, J. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-031-08848-3_7 · Cited by 12 · see citing works
NICE: The Native IoT-Centric Event Log Model for Process Mining
Bertrand, Y., Veneruso, S. V., Leotta, F., Mecella, M., & Serral, E. (2024) · Lecture notes in business information processing · doi:10.1007/978-3-031-56107-8_3 · Cited by 12 · see citing works
RBPMN: the value of roles for business process modeling
Skouti, T., Seiger, R., Furrer, F. J., & Strahringer, S. (2024) · Software & Systems Modeling · doi:10.1007/s10270-024-01202-z · Cited by 14 · see citing works
“…extension that [3] explicitly represents context-sensitive and relational roles and their interactions in BPs (Role-based BPMN, RBPMN), we propose an approach that tackles many of the new process modeling challenges that arise with the increasing complexity of CPS and in other complex settings [2].”
InfluentialMalburg, L., Brand, F., & Bergmann, R. (2023) · Lecture notes in business information processing · doi:10.1007/978-3-031-26886-1_5 · Cited by 12 · see citing works
Bertrand, Y., Weerdt, J. D., & Serral, E. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-41620-0_23 · Cited by 11 · see citing works
Assessing the Suitability of Traditional Event Log Standards for IoT-Enhanced Event Logs
Bertrand, Y., Weerdt, J. D., & Serral, E. (2023) · Lecture notes in business information processing · doi:10.1007/978-3-031-25383-6_6 · Cited by 11 · see citing works
Application of Internet of Things in Marketing Management
Naim, A., Muniasamy, A., & Alqahtani, H. (2022) · doi:10.1002/9781119821847.ch9 · Cited by 11 · see citing works
Semi-Automated Approach for Building Event Logs for Process Mining from Relational Database
Hernandez-Resendiz, J. D., Tello-Leal, E., Ramirez-Alcocer, U. M., & Macías‐Hernández, B. A. (2022) · Applied Sciences · doi:10.3390/app122110832 · Cited by 10 · see citing works
SKTR: Trace Recovery from Stochastically Known Logs
Bogdanov, E., Cohen, I., & Gal, A. (2023) · International Conference on Process Mining (ICPM) · doi:10.1109/icpm60904.2023.10271985 · Cited by 9 · see citing works
LABPMN: Location-Aware Business Process Modeling and Notation
Poss, L., Dietz, L., & Schönig, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46846-9_11 · Cited by 9 · see citing works
Siwach, P., Gulia, D., Yadav, D. K., Malik, M., & Gahlawat, V. K. (2025) · Digital Business · doi:10.1016/j.digbus.2025.100125 · Cited by 9 · see citing works
Malburg, L., Klein, P., & Bergmann, R. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106727 · Cited by 9 · see citing works
Data-Driven Generation of Services for IoT-Based Online Activity Detection
Seiger, R., Franceschetti, M., & Weber, B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48424-7_14 · Cited by 9 · see citing works
Hoffmann, M., Malburg, L., & Bergmann, R. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-030-94343-1_4 · Cited by 9 · see citing works
“huge amounts of real-time data can benefit from the advantages of BPM solutions [7].”
BackgroundTowards a Digital Twin Modelling Notation
Corradini, F., Fedeli, A., Polini, A., & Re, B. (2022) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc/picom/cbdcom/cy55231.2022.9927827 · Cited by 9 · see citing works
Integrating IoT-Driven Events into Business Processes
Kirikkayis, Y., Gallik, F., Seiger, R., & Reichert, M. (2023) · Lecture notes in business information processing · doi:10.1007/978-3-031-34674-3_11 · Cited by 9 · see citing works
Stoiber, C., & Schönig, S. (2024) · Information Systems and e-Business Management · doi:10.1007/s10257-024-00676-0 · Cited by 10 · see citing works
“Hence, integrating IIoT technology into existing business processes can result in valuable business process improvement (BPI), which is particularly relevant for process-oriented organizations (Janiesch et al. 2020).”
Analyzing Data Streams from Cyber-Physical-Systems: A Case Study
Beyel, H. H., Makke, O., Pourbafrani, M., Gusikhin, O., & van der Aalst, W. M. P. (2024) · SN Computer Science · doi:10.1007/s42979-024-03008-8 · Cited by 8 · see citing works
“As denoted in [4] and in our previous example, using IoT data for process mining is a challenging task.”
A model-based simulator for smart homes: Enabling reproducibility and standardization
Veneruso, S. V., Bertrand, Y., Leotta, F., Serral, E., & Mecella, M. (2023) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-220016 · Cited by 8 · see citing works
“In the recent years, researchers investigated the possibility of applying BPM and Process Mining to the IoT field [27,36] and underlying processes.”
Pietroni, G., & Marconi, M. (2023) · Proceedings of the Design Society · doi:10.1017/pds.2023.113 · Cited by 8 · see citing works
“This potentially provides benefits both in the domain of Digital Innovation and in production process management (Janiesch, et al., 2020).”
BackgroundConformance Checking of a Longwall Shearer Operation Based on Low-Level Events
Szpyrka, M., Brzychczy, E., Napieraj, A., Korski, J., & Nalepa, G. J. (2020) · Energies · doi:10.3390/en13246630 · Cited by 9 · see citing works
“In this context, IoT data, such as events, delivered via in-memory databases or complex event processing can be useful [13].”
BackgroundLinac: A Smart Environment Simulator of Human Activities
Federico, G. D., Nikolajsen, E. R., Azam, M., & Burattin, A. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-030-98581-3_5 · Cited by 8 · see citing works
Arellanes, D. (2021) · IEEE Systems Man and Cybernetics Magazine · doi:10.1109/msmc.2021.3062822 · Cited by 8 · see citing works
“Unlike traditional enterprise systems, IoT systems are moving towards environments full of complex interactions, as a consequence of the overwhelming number of objects available worldwide [1]–[3].”
BackgroundLocation-aware business process modeling and execution
Poss, L., & Schönig, S. (2024) · Software & Systems Modeling · doi:10.1007/s10270-024-01224-7 · Cited by 7 · see citing works
“The data collected through IoT can be leveraged to establish intelligent environments that respond to both user and organizational requirements, thereby enhancing process efficiency and effectiveness [23, 24].”
Approaches for IoT-enhanced predictive process monitoring
Bertrand, Y., Stevens, A., Deforce, B., Smedt, J. D., Weerdt, J. D., & Serral, E. (2025) · Process Science · doi:10.1007/s44311-025-00011-x · Cited by 8 · see citing works
“The challenge of the scope of relevance of the IoT data refers to the fact that it is not always known which process and activity instance(s) each IoT data measurement relates to (Koschmider et al. 2020; van Eck et al. 2016).”
Seiger, R., Kurz, A. F., & Franceschetti, M. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.107987 · Cited by 8 · see citing works
Role of IoT in Industrial Transformation and Marketing Management
Naim, A., & Alqahtani, H. (2023) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/978-1-6684-8166-0.ch005 · Cited by 7 · see citing works
Human-Sensing Platforms and Ethical Considerations Throughout Their Data Life-Cycles
Lee, S. U., Paik, H.-Y., & Kanhere, S. S. (2024) · IEEE Internet of Things Magazine · doi:10.1109/iotm.001.2300148 · Cited by 7 · see citing works
“This data reveals how “things” and people interact, better integration [8].”
BackgroundAn Expert-Validated Bridging Model for IoT Process Mining
Bertrand, Y., Weerdt, J. D., & Serral, E. (2024) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00849-0 · Cited by 7 · see citing works
“A distinction between the two accepta-tions of the term is recognized in IoT PM research (Koschmider et al. 2020; Tax et al. 2018).”
Kirikkayis, Y., Gallik, F., & Reichert, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17604-3_7 · Cited by 7 · see citing works
“Data from traditional repositories such as databases and data warehouses are not sufficient for IoT-aware decision making [1].”
BackgroundMethodInfluentialSupporting the Implementation of Digital Twins for IoT-Enhanced BPs
Valderas, P. (2023) · Lecture notes in business information processing · doi:10.1007/978-3-031-33080-3_14 · Cited by 7 · see citing works
SIREN: Designing Business Processes for Comprehensive Industrial IoT Security Management
Hornsteiner, M., & Schönig, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-32808-4_24 · Cited by 6 · see citing works
A Process Oriented Integration Model for Smart Health Services
Gondal, F. K., Shahzad, S. K., Jaffar, M. A., & Iqbal, M. W. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.028407 · Cited by 6 · see citing works
An Event-Centric Metamodel for IoT-Driven Process Monitoring and Conformance Checking
Franceschetti, M., Seiger, R., & Weber, B. (2024) · Lecture notes in business information processing · doi:10.1007/978-3-031-50974-2_11 · Cited by 6 · see citing works
A User Study on Modeling IoT-Aware Processes with BPMN 2.0
Kirikkayis, Y., Winter, M. R., & Reichert, M. (2024) · Information · doi:10.3390/info15040229 · Cited by 6 · see citing works
Analyzing Behavior in Cyber-Physical Systems in Connected Vehicles: A Case Study
Beyel, H. H., Makke, O., Gusikhin, O., & van der Aalst, W. M. P. (2024) · Lecture notes in business information processing · doi:10.1007/978-3-031-50974-2_8 · Cited by 5 · see citing works
Simulation for Decision Support in Process Reengineering in the Automotive Industry
Zêdo, Y. A., & Ramos, A. L. (2022) · International Journal of Mathematical Engineering and Management Sciences · doi:10.33889/ijmems.2022.7.2.012 · Cited by 5 · see citing works
Bridging Signals and Human Intelligence
Gráf, D., Retschitzegger, W., Schwinger, W., Kapsammer, E., & Baumgartner, N. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-10986-7_46 · Cited by 5 · see citing works
A Data-Centric Approach to Design Resilient-Aware Process Models in BPMN
Agostinelli, S., Luzi, F. D., di Canito, U., Ferraro, J., Marrella, A., & Mecella, M. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-031-16171-1_3 · Cited by 5 · see citing works
vAMoS: eVent Abstraction via Motifs Search
Federico, G. D., & Burattin, A. (2023) · Lecture notes in business information processing · doi:10.1007/978-3-031-25383-6_9 · Cited by 5 · see citing works
“When Business Process Management is used with data coming from Internet of Things [6], assuming a one-to-one mapping between an event in the log and a sensor reading, typically leads to too-fine grained event logs.”
MethodManaging next generation BP-x initiatives
Schaschek, M., Gwinner, F., Neis, N., Tomitza, C., Zeiß, C., & Winkelmann, A. (2024) · Information Systems and e-Business Management · doi:10.1007/s10257-024-00681-3 · Cited by 5 · see citing works
“…high-level data covers complex event data from event-driven systems (Krumeich et al. 2015; Mayr et al. 2022b), IoT and Blockchain application data (Janiesch et al. 2017; Beverungen et al. 2021), user interaction data (e.g., click-streams) (Linn et al. 2018; Mayr et al. 2022a), (I1,I3), or social…”
Schultheis, A., Jilg, D., Malburg, L., Bergweiler, S., & Bergmann, R. (2024) · Processes · doi:10.3390/pr12122714 · Cited by 6 · see citing works
Challenges in Data Quality Management for IoT-Enhanced Event Logs
Bertrand, Y., Schultheis, A., Malburg, L., Grüger, J., Asensio, E. S., & Bergmann, R. (2025) · Lecture Notes in Business Information Processing · doi:10.1007/978-3-031-92474-3_2 · Cited by 4 · see citing works
Formalizing the operational perspective in BPM: an ontology-based approach for tool integration
Poss, L., & Schönig, S. (2025) · Business Process Management Journal · doi:10.1108/bpmj-10-2024-0930 · Cited by 4 · see citing works
Seiger, R., Locher, D., Kaufmann, M., & Kurz, A. F. (2026) · Internet of Things · doi:10.1016/j.iot.2025.101870 · Cited by 4 · see citing works
“Two significant works discuss challenges and opportunities of using IoT data together with BPM systems, highlighting the issue of event abstraction from low-level data to BPM-related data [3, 19], and more specific challenges associated with using data from sensors in this context [5].”
InfluentialFrom Internet of Things Data to Business Processes: Challenges and a Framework
Mangler, J., Seiger, R., Benzin, J.-V., Grüger, J., Kirikkayis, Y., Gallik, F., Malburg, L., Ehrendorfer, M., Bertrand, Y., Franceschetti, M., et al. (2026) · Internet of things · doi:10.1007/978-3-031-90746-3_2 · Cited by 4 · see citing works
Zhao, X., Su, Q., & Li, Q. (2025) · International Journal of Logistics Research and Applications · doi:10.1080/13675567.2025.2543830 · Cited by 5 · see citing works
“…systems where automation remains embedded in existing architectures and often depends on human intervention or platform-based authority (Janiesch et al. 2020; Meyer, Ruppen, and Hilty 2015), blockchain enables machine-based, autonomous execution across organ-isational boundaries,…”
A Holistic Framework for IoT-Aware Business Processes
Kirikkayis, Y., Gallik, F., & Reichert, M. (2023) · Lecture notes in business information processing · doi:10.1007/978-3-031-25383-6_8 · Cited by 4 · see citing works
A Process to Non-invasively Augment Legacy IoT Systems Using Business Processes and Microservices
Seiger, R., Franceschetti, M., & Andaloussi, A. A. (2024) · International Conference on the Internet of Things (IoT) · doi:10.1145/3703790.3703791 · Cited by 4 · see citing works
“In our experience, the smart factory and many other IoT settings produce incomplete and inappropriate process data, as these systems are typically not developed with a process-oriented view [4, 15].”
InfluentialRobotic Datasets for Process Mining
Corradini, F., Pettinari, S., Re, B., Rossi, L., & Sampaolo, M. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-94590-8_7 · Cited by 4 · see citing works
Generating Reliable Process Event Streams and Time Series Data Based on Neural Networks
Herbert, T., Mangler, J., & Rinderle-Ma, S. (2021) · Lecture notes in business information processing · doi:10.1007/978-3-030-79186-5_6 · Cited by 4 · see citing works
“The continuous monitoring and analysis of their processes is crucial for many application domains such as manufacturing [29] and medicine [13].”
BackgroundLifecycle Support for IoT-Driven Business Rules
Kirikkayis, Y., Gallik, F., & Reichert, M. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.281 · Cited by 4 · see citing works
A framework for AI-based self-adaptive cyber-physical process systems
Guldner, A., Hoffmann, M., Lohr, C., Machhamer, R., Malburg, L., Morgen, M., Rodermund, S. C., Schäfer, F., Schaupeter, L., Schneider, J., et al. (2023) · it - Information Technology · doi:10.1515/itit-2023-0001 · Cited by 4 · see citing works
Multi-Task Prediction Method Based on GGCN for Object Centric Event Logs
Ke, L., Fang, H., Xu, Y., & Chifeng, S. (2025) · IEEE Access · doi:10.1109/access.2025.3553618 · Cited by 3 · see citing works
“Owing to the widespread adoption of information systems, large amounts of event log data are stored in complex relational databases such as Enterprise Resource Planning (ERP) and Manufacturing Execution System (MES) [1].”
Toward IoT-Based Process Analytics: Extending Event Knowledge Graphs with Ambiguity
Franceschetti, M., Buchegger, D. M., Seiger, R., & Weber, B. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-95397-2_11 · Cited by 3 · see citing works
IoT-enriched event log generation and quality analytics: a case study
Grüger, J., Malburg, L., & Bergmann, R. (2023) · it - Information Technology · doi:10.1515/itit-2022-0077 · Cited by 3 · see citing works
Clustering Raw Sensor Data in Process Logs to Detect Data Streams
Ehrendorfer, M., Mangler, J., & Rinderle‐Ma, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46846-9_25 · Cited by 3 · see citing works
Enabling Process Mining on Multimodal Robotic Data
Corradini, F., Pettinari, S., Re, B., Rossi, L., & Sampaolo, M. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-94931-9_21 · Cited by 3 · see citing works
Ehrendorfer, M., Mangler, J., & Rinderle‐Ma, S. (2021) · Lecture notes in business information processing · doi:10.1007/978-3-030-79108-7_4 · Cited by 3 · see citing works
Hoffmann, M., Malburg, L., Bach, N., & Bergmann, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-14923-8_16 · Cited by 3 · see citing works
“robotic process automation [1], or IoT environments [4] such as manufacturing [13, 24, 28, 29].”
BackgroundMethod to Identify Process Activities by Visualizing Sensor Events
Weyers, F., Seiger, R., & Weber, B. (2023) · Lecture notes in business information processing · doi:10.1007/978-3-031-25383-6_7 · Cited by 3 · see citing works
“The BPM-IoT Manifesto discusses various challenges and benefits of bringing both domains together [6].”
BackgroundMethodInfluentialCheck My Flow: Distributed Conformance Checking at the Source
Andersen, J., Rathje, P., & Landsiedel, O. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-78666-2_8 · Cited by 2 · see citing works
Methodology for Evaluating Process Mining Tools in IoT Contexts
Tratnjek, T., & Polančič, G. (2026) · Sensors · doi:10.3390/s26031020 · Cited by 2 · see citing works
“This gap motivates the use of process mining not as a business analytics tool, but as a method for diagnosis and validation in IoT-based systems [1].”
InfluentialPoss, L., & Schönig, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-15538-2_31 · Cited by 2 · see citing works
- Authors
- Janiesch, C., Koschmider, A., Mecella, M., Weber, B., Burattin, A., Di Ciccio, C., Fortino, G., Gal, A., Kannengiesser, U., Leotta, F., Mannhardt, F., Marrella, A., Mendling, J., Oberweis, A., Reichert, M., Rinderle-Ma, S., Serral, E., Song, W., Su, J., Torres, V., Weidlich, M., Weske, M., & Zhang, L.
- Affiliations
- University of Würzburg (DE)
- Christian-Albrechts-Universität zu Kiel (DE)
- Sapienza University of Rome (IT)
- University of St.Gallen (CH)
- Technical University of Denmark (DK)
- University of Calabria (IT)
- Technion – Israel Institute of Technology (IL)
- Johannes Kepler University of Linz (AT)
- Norwegian University of Science and Technology (NO)
- Vienna University of Economics and Business (AT)
- Karlsruhe Institute of Technology (DE)
- Universität Ulm (DE)
- University of Georgia (US)
- University of California, Santa Barbara (US)
- Universitat Politècnica de València (ES)
- Humboldt-Universität zu Berlin (DE)
- University of Potsdam (DE)
- Fudan University (CN)
- Venue
- IEEE Systems Man and Cybernetics Magazine
- Published
- 2020-10
- Volume
- 6
- Issue
- 4
- Pages
- 34-44
- ISSN
- 2333-942X (ISSN-L)
- 2380-1298
- OpenAlex
- W3041128643
- Cited by
- 177
- References
- 25
- Citations by year
- 2026: 31 · 2025: 37 · 2024: 31 · 2023: 31 · 2022: 34 · 2021: 10 · 2020: 3
- FWCI
- 22.15
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Systems Man and Cybernetics Magazine (publishedVersion)
- Alexandria (UniSG) (University of St.Gallen) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- WU Research (submittedVersion)
- Alexandria (UniSG) (University of St.Gallen) (submittedVersion, other-oa)
- Archive of Doctoral Theses and Digital Collections (University of Calabria) (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- publish.UP (University of Potsdam) (submittedVersion)
- Lirias (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Business Process Modeling and Analysis
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Robotic Process Automation Applications
- Keywords
- Internet of Things, business process management, process discovery, process monitoring, process evolution, complex software systems, autonomous agents, collaborative business processes
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
