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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: 30 April 2019

Special Issue Editors

Guest Editor
Dr. Rüdiger Pryss

Institute of Databases and Information Systems, Ulm University, Ulm 89081, Germany
Website | E-Mail
Interests: mobile process management; mobile services; computer-based medical systems; mobile crowdsensing; mHealth
Guest Editor
Prof. Massimo Mecella

Dipartimento di Ingegneria, Informatica, Universita degli Studi di Roma La Sapienza, Rome, Italy
Website | E-Mail
Interests: service oriented computing; business process management; ubiquitous systems; smart environments; user interfaces
Guest Editor
Prof. Samir Tata

Department of Computer Science, Telecom SudParis, 9 rue Charles Fourier, 91011 Evry Cedex, France
Website | E-Mail
Interests: cloud computing; Internet of Things; service computing; business process management
Guest Editor
Prof. Manfred Reichert

Universitat Ulm, Institute of Databases and Information Systems, Ulm, Germany
Website | E-Mail
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 1800 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 (1 paper)

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Research

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
Received: 19 December 2018 / Revised: 14 January 2019 / Accepted: 26 January 2019 / Published: 29 January 2019
PDF Full-text (11055 KB) | HTML Full-text | XML Full-text
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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