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Special Issue "Integrating Process Management Technology with Sensor Data"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 June 2019).

Special Issue Editors

Dr. Rüdiger Pryss
Website
Guest Editor
Institute of Databases and Information Systems, Ulm University, Ulm 89081, Germany
Interests: mobile process management; mobile services; computer-based medical systems; mobile crowdsensing; mHealth
Special Issues and Collections in MDPI journals
Prof. Dr. Massimo Mecella
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Guest Editor
Dipartimento di Ingegneria informatica, automatica, e gestionale “Antonio Ruberti", Sapienza Università di Roma - Via Ariosto, 25, 00185 Roma RM, Italy
Interests: service oriented computing; process management; CPSs (cyber-physical systems) and IoT (Internet-of-Things); user-centered design for complex systems; CIS—cooperative information systems and WWW-based information systems; software architectures, distributed technologies and middleware
Special Issues and Collections in MDPI journals
Prof. Samir Tata
Website
Guest Editor
Department of Computer Science, Telecom SudParis, 9 rue Charles Fourier, 91011 Evry Cedex, France
Interests: cloud computing; Internet of Things; service computing; business process management
Prof. Dr. Manfred Reichert
Website
Guest Editor
Universitat Ulm, Institute of Databases and Information Systems, Ulm, Germany
Interests: business process management; process-aware information systems; adaptive processes; process and service science; e- and m-Health

Special Issue Information

Dear Colleagues,

Process management technology has contributed to the advancement of process-aware enterprise information systems (PAIS) for more than a decade. Modern companies have adopted research results from the business process management (BPM) field to keep pace with the challenges emerging due to increasing customer demands. Meanwhile, Millennials have fanned the use of mobile technology in everyday life, as well as for many businesses. Consequently, enterprises need to enhance their process-aware information systems to properly integrate smart mobile devices in a flexible and cost-effective way. Along this trend, the sensor capabilities of mobile devices have increased by magnitude of orders enabling an advanced support of business processes and their activities. In the era of the Internet of Things, in addition, numerous sensors emerged that are used ubiquitously in everyday business on a cheap and effective basis. Especially in the light of Industry 4.0 and cyber-physical systems, manufacturers crave for the integration of sensor technology and sensor data with their process-aware information systems. As the latter provide a well-defined context, the context-aware integration of sensors and sensor data becomes powerfully possible.

The aim of this Special Issue is to investigate upcoming challenges, research opportunities, and technologies emerging along the described trends. In particular, the Special Issue shall investigate:

  • The impact of considering sensor data on the various stages of the business process lifecycle, i.e., modeling, implementation and configuration, deployment, enactment, monitoring, dynamic adaptation, mining and evolution of business processes.
  • The impact of considering business processes on the way sensors systems and networks are built or reorganized.

This Special Issue aims to provide a comprehensive overview of state-of-the-art sensor technology in the light of business process management. We invite research articles that will consolidate the understanding of the state-of-the-art in this area. The Special Issue will publish full research, review, and highly rated manuscripts on the integration of process technology with sensor data, addressing—amongst others—one of the following topics:

  • Sensor-enriched process tasks
  • Sensor data processing and event management
  • System architectures
  • Wearables and mobile sensors
  • Sensor categories and their relevance for business process support
  • Resource tracking and management (e.g., humans vs. robots)
  • Sensor- and context-driven processes
  • Virtual and augmented reality support for business processes / tasks
  • Aligning digital processes with processes from the physical world
  • Predictive processes based on sensor data
  • Sensor-based detection of discrepancies between digital and real process
  • Business process support in the era of the Internet/Web of Things
  • Industry 4.0 and Industrial Internet of Things
  • Sensor-based exception discovery and handling
  • Equipping process resources (incl. humans) with sensors
  • Combining process mining with sensor data support

Dr. Rüdiger Pryss
Prof. Massimo Mecella
Prof. Samir Tata
Prof. Manfred Reichert
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 papers will be 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. Sensors is an international peer-reviewed open access semimonthly 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 2000 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

  • Sensor-enriched process tasks
  • Sensor data processing & event management
  • System architectures
  • Wearables & mobile sensors
  • Sensor categories and their relevance for business process support
  • Resource tracking and management (e.g., humans vs. robots)
  • Sensor- and context-driven processes
  • Virtual and augmented reality support for business processes / tasks
  • Aligning digital processes with processes from the physical world
  • Predictive processes based on sensor data
  • Sensor-based detection of discrepancies between digital and real process
  • Business process support in the era of the Internet / Web of Things
  • Industry 4.0 and Industrial Internet of Things
  • Sensor-based exception discovery & handling
  • Equipping process resources (incl. humans) with sensors
  • Combining process mining with sensor data support

Published Papers (2 papers)

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Research

Open AccessArticle
Dimensionality Reduction and Subspace Clustering in Mixed Reality for Condition Monitoring of High-Dimensional Production Data
Sensors 2019, 19(18), 3903; https://doi.org/10.3390/s19183903 - 10 Sep 2019
Abstract
Visual analytics are becoming increasingly important in the light of big data and related scenarios. Along this trend, the field of immersive analytics has been variously furthered as it is able to provide sophisticated visual data analytics on one hand, while preserving user-friendliness [...] Read more.
Visual analytics are becoming increasingly important in the light of big data and related scenarios. Along this trend, the field of immersive analytics has been variously furthered as it is able to provide sophisticated visual data analytics on one hand, while preserving user-friendliness on the other. Furthermore, recent hardware developments such as smart glasses, as well as achievements in virtual-reality applications, have fanned immersive analytic solutions. Notably, such solutions can be very effective when they are applied to high-dimensional datasets. Taking this advantage into account, the work at hand applies immersive analytics to a high-dimensional production dataset to improve the digital support of daily work tasks. More specifically, a mixed-reality implementation is presented that will support manufacturers as well as data scientists to comprehensively analyze machine data. As a particular goal, the prototype will simplify the analysis of manufacturing data through the usage of dimensionality reduction effects. Therefore, five aspects are mainly reported in this paper. First, it is shown how dimensionality reduction effects can be represented by clusters. Second, it is presented how the resulting information loss of the reduction is addressed. Third, the graphical interface of the developed prototype is illustrated as it provides (1) a correlation coefficient graph, (2) a plot for the information loss, and (3) a 3D particle system. In addition, an implemented voice recognition feature of the prototype is shown, which was considered to be being promising to select or deselect data variables users are interested in when analyzing the data. Fourth, based on a machine learning library, it is shown how the prototype reduces computational resources using smart glasses. The main idea is based on a recommendation approach as well as the use of subspace clustering. Fifth, results from a practical setting are presented, in which the prototype was shown to domain experts. The latter reported that such a tool is actually helpful to analyze machine data daily. Moreover, it was reported that such a system can be used to educate machine operators more properly. As a general outcome of this work, the presented approach may constitute a helpful solution for the industry as well as other domains such as medicine. Full article
(This article belongs to the Special Issue Integrating Process Management Technology with Sensor Data)
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Open AccessArticle
Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application
Sensors 2019, 19(3), 557; https://doi.org/10.3390/s19030557 - 29 Jan 2019
Cited by 9
Abstract
The study presents some results of customer paths’ analysis in a shopping mall. Bluetooth-based technology is used to collect data. The event log containing spatiotemporal information is analyzed with process mining. Process mining is a technique that enables one to see the whole [...] Read more.
The study presents some results of customer paths’ analysis in a shopping mall. Bluetooth-based technology is used to collect data. The event log containing spatiotemporal information is analyzed with process mining. Process mining is a technique that enables one to see the whole process contrary to data-centric methods. The use of process mining can provide a readily-understandable view of the customer paths. We installed iBeacon devices, a Bluetooth-based positioning system, in the shopping mall. During December 2017 and January and February 2018, close to 8000 customer data were captured. We aim to investigate customer behaviors regarding gender by using their paths. We can determine the gender of customers if they go to the men’s bathroom or women’s bathroom. Since the study has a comprehensive scope, we focused on male and female customers’ behaviors. This study shows that male and female customers have different behaviors. Their duration and paths, in general, are not similar. In addition, the study shows that the process mining technique is a viable way to analyze customer behavior using Bluetooth-based technology. Full article
(This article belongs to the Special Issue Integrating Process Management Technology with Sensor Data)
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