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Future Internet
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IoT-Based BPM for Smart Environments
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IoT-Based BPM for Smart Environments

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (5 March 2023) | Viewed by 9511

Special Issue Editors

Prof. Dr. Estefanía Serral

E-Mail Website
Guest Editor
Research Centre for Information Systems Engineering (LIRIS), KU Leuven, Warmoesberg 26, 1000 Brussels, Belgium
Interests: The Internet of Things, Business Process Management, Context-adaptive systems
Prof. Dr. Francesco Leotta

E-Mail Website
Guest Editor
Department of Computer, Control, and Management Engineering, Sapienza Università di Roma, 00185 Roma, Italy
Interests: smart spaces; dataset generation; indoor localization and tracking systems; human-computer interaction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Victoria Torres

E-Mail Website
Guest Editor
PROS Research Centre, Universitat Politècnica de València, València, Spain
Interests: business process management; software variability; the internet of things; web engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Massimo Mecella

E-Mail Website
Guest Editor
Department of Computer Science, Automation and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy
Interests: web services; business process management; user interfaces; ubiquitous systems; smart environments
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The arrival of the Internet of Things (IoT) has put into play a huge amount of interconnected and embedded computing devices with sensing and actuating capabilities.

The use of IoT within Business Process Management (BPM) has the potential to revolutionize this field greatly improving our way of living and doing business. Business Process Management (BPM) is the set of guidelines and techniques used to manage business processes, whereas a process is a set of coordinated activities that are performed to satisfy a specific goal. As such, IoT-based BPM can take business processes to higher levels of flexibility, efficiency, and responsiveness; and can as well take the design and development of IoT systems to a higher maturity level.

While IoT and BPM have mostly been studied independently, many benefits can be realized by the integration of these two disciplines. This special issue invites research papers that present novel ideas for the design, development and evaluation of IoT-based BPM for a widespread and significant improvement of well-being in smart environments.

Papers must provide full technical details and thorough evaluations of their scientific contributions, which should demonstrate a clear advancement towards the state of the art.

The topics of the special include, but are not limited to:

  • Modeling IoT-enhanced BPs for smart systems
  • Exploiting context to improve BP execution
  • Privacy and security in IoT-enhanced BPs
  • Improving resource monitoring and quality of task execution
  • Sensor-based task management in BPM
  • Case studies of IoT-enhanced BPs in smart systems
  • Bridging the gap between low-level IoT data and high level process events
  • Visual Analytics of behavior for IoT BPs
  • IoT-enhanced process model discovery, recognition, monitoring and prediction
  • Process anomaly detection from IoT data
  • Execution of IoT-enhanced BPs
  • Architectures for IoT BPs
  • Evaluation of IoT-enhanced BPs
  • Considerate communication between process and IoT actors for increasing well-being
  • Visualization of IoT BPs
  • Architectures for IoT BPs
  • Methodologies for the design and implementation of IoT BPs

The special issue will also invite the best papers of the 6th International Workshop on BP-Meet-IoT (affiliated with BPM 2022).

Prof. Dr. Estefanía Serral
Prof. Dr. Francesco Leotta
Prof. Dr. Victoria Torres
Prof. Dr. Massimo Mecella
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. Future Internet 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 1600 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

  • smart environments
  • the Internet of Things
  • Business Process Management
  • IoT
  • BPM
  • integration of IoT and BPM

Published Papers (4 papers)

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Research

22 pages, 1110 KiB  
Article
CvAMoS—Event Abstraction Using Contextual Information
by Gemma Di Federico and Andrea Burattin
Future Internet 2023, 15(3), 113; https://doi.org/10.3390/fi15030113 - 18 Mar 2023
Cited by 1 | Viewed by 1136
Abstract
Process mining analyzes events that are logged during the execution of a process, with the aim of gathering useful information and knowledge. Process discovery algorithms derive process models that represent these processes. The level of abstraction at which the process model is represented [...] Read more.
Process mining analyzes events that are logged during the execution of a process, with the aim of gathering useful information and knowledge. Process discovery algorithms derive process models that represent these processes. The level of abstraction at which the process model is represented is reflected in the granularity of the event log. When a process is captured by the usage of sensor systems, process activities are recorded at the sensor-level in the form of sensor readings, and are therefore too fine-grained and non-explanatory. To increase the understandability of the process model, events need to be abstracted into higher-level activities that provide a more meaningful representation of the process. The abstraction becomes more relevant and challenging when the process involves human behavior, as the flexible nature of human actions can make it harder to identify and abstract meaningful activities. This paper proposes CvAMoS, a trace-based approach for event abstraction, which focuses on identifying motifs while taking context into account. A motif is a recurring sequence of events that represents an activity that took place under specific circumstances depicted by the context. Context information is logged in the event log in the form of environmental sensor readings (e.g., the temperature and light sensors). The presented algorithm uses a distance function to deal with the variability in the execution of activities. The result is a set of meaningful and interpretable motifs. The algorithm has been tested on both synthetic and real datasets, and compared to the state of the art. CvAMoS is implemented as a Java application and the code is freely available. Full article
(This article belongs to the Special Issue IoT-Based BPM for Smart Environments)
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21 pages, 816 KiB  
Article
DataStream XES Extension: Embedding IoT Sensor Data into Extensible Event Stream Logs
by Juergen Mangler, Joscha Grüger, Lukas Malburg, Matthias Ehrendorfer, Yannis Bertrand, Janik-Vasily Benzin, Stefanie Rinderle-Ma, Estefania Serral Asensio and Ralph Bergmann
Future Internet 2023, 15(3), 109; https://doi.org/10.3390/fi15030109 - 14 Mar 2023
Cited by 8 | Viewed by 2184
Abstract
The Internet of Things (IoT) has been shown to be very valuable for Business Process Management (BPM), for example, to better track and control process executions. While IoT actuators can automatically trigger actions, IoT sensors can monitor the changes in the environment and [...] Read more.
The Internet of Things (IoT) has been shown to be very valuable for Business Process Management (BPM), for example, to better track and control process executions. While IoT actuators can automatically trigger actions, IoT sensors can monitor the changes in the environment and the humans involved in the processes. These sensors produce large amounts of discrete and continuous data streams, which hold the key to understanding the quality of the executed processes. However, to enable this understanding, it is needed to have a joint representation of the data generated by the process engine executing the process, and the data generated by the IoT sensors. In this paper, we present an extension of the event log standard format XES called DataStream. DataStream enables the connection of IoT data to process events, preserving the full context required for data analysis, even when scenarios or hardware artifacts are rapidly changing. The DataStream extension is designed based on a set of goals and evaluated by creating two datasets for real-world scenarios from the transportation/logistics and manufacturing domains. Full article
(This article belongs to the Special Issue IoT-Based BPM for Smart Environments)
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34 pages, 2792 KiB  
Article
BPMNE4IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes
by Yusuf Kirikkayis, Florian Gallik, Michael Winter and Manfred Reichert
Future Internet 2023, 15(3), 90; https://doi.org/10.3390/fi15030090 - 22 Feb 2023
Cited by 9 | Viewed by 3229
Abstract
The Internet of Things (IoT) enables a variety of smart applications, including smart home, smart manufacturing, and smart city. By enhancing Business Process Management Systems with IoT capabilities, the execution and monitoring of business processes can be significantly improved. Providing a holistic support [...] Read more.
The Internet of Things (IoT) enables a variety of smart applications, including smart home, smart manufacturing, and smart city. By enhancing Business Process Management Systems with IoT capabilities, the execution and monitoring of business processes can be significantly improved. Providing a holistic support for modeling, executing and monitoring IoT-driven processes, however, constitutes a challenge. Existing process modeling and process execution languages, such as BPMN 2.0, are unable to fully meet the IoT characteristics (e.g., asynchronicity and parallelism) of IoT-driven processes. In this article, we present BPMNE4IoT—A holistic framework for modeling, executing and monitoring IoT-driven processes. We introduce various artifacts and events based on the BPMN 2.0 metamodel that allow realizing the desired IoT awareness of business processes. The framework is evaluated along two real-world scenarios from two different domains. Moreover, we present a user study for comparing BPMNE4IoT and BPMN 2.0. In particular, this study has confirmed that the BPMNE4IoT framework facilitates the support of IoT-driven processes. Full article
(This article belongs to the Special Issue IoT-Based BPM for Smart Environments)
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31 pages, 3031 KiB  
Article
An Interactive Method for Detection of Process Activity Executions from IoT Data
by Ronny Seiger, Marco Franceschetti and Barbara Weber
Future Internet 2023, 15(2), 77; https://doi.org/10.3390/fi15020077 - 16 Feb 2023
Cited by 10 | Viewed by 1965
Abstract
The increasing number of IoT devices equipped with sensors and actuators pervading every domain of everyday life allows for improved automated monitoring and analysis of processes executed in IoT-enabled environments. While sophisticated analysis methods exist to detect specific types of activities from low-level [...] Read more.
The increasing number of IoT devices equipped with sensors and actuators pervading every domain of everyday life allows for improved automated monitoring and analysis of processes executed in IoT-enabled environments. While sophisticated analysis methods exist to detect specific types of activities from low-level IoT data, a general approach for detecting activity executions that are part of more complex business processes does not exist. Moreover, dedicated information systems to orchestrate or monitor process executions are not available in typical IoT environments. As a consequence, the large corpus of existing process analysis and mining techniques to check and improve process executions cannot be applied. In this work, we develop an interactive method guiding the analysis of low-level IoT data with the goal of detecting higher-level process activity executions. The method is derived following the exploratory data analysis of an IoT data set from a smart factory. We propose analysis steps, sensor-actuator-activity patterns, and the novel concept of activity signatures that are applicable in many IoT domains. The method shows to be valuable for the early stages of IoT data analyses to build a ground truth based on domain knowledge and decisions of the process analyst, which can be used for automated activity detection in later stages. Full article
(This article belongs to the Special Issue IoT-Based BPM for Smart Environments)
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