Robotic Process Automation - A Systematic Mapping Study and Classification Framework

Robotic Process Automation (RPA) deals with the automation of rule-based process tasks to increase process efficiency and to reduce process costs. Due to the utmost importance of business process automation in industry, RPA attracts increasing attention in the scientific field as well. This paper presents the state-of-the-art in the RPA field by means of a Systematic Mapping Study (SMS). In this SMS, 63 publications are identified, categorised, and analysed along well-defined research questions. From the SMS findings, additionally, a framework for systematically analysing, assessing, and comparing existing as well as upcoming RPA works is derived. The discovered thematic clusters suggest further investigations in order to develop a more elaborated structural research approach for RPA

Analyse von Mustern der Aufmerksamkeit beim Betrachten von Petri-Netzen

Petri-Netze sind bedeutend für die Geschäftsprozessmodellierung. Die Betrachtung dieser Netze ruft eine Menge an kognitiven Prozessen hervor. Dabei wird das vorgegebene Petri-Netz zuerst visuell wahrgenommen und somit wird die visuelle Aufmerksamkeit darauf gesteuert für die Informationsverarbeitung. Um diese Prozesse festhalten zu können, werden die Blickbewegungen mithilfe von Eye-Trackern aufgezeichnet. Eine Analyse dieser Daten ermöglicht das Auffinden von Mustern in den Rohdaten, die uns Einblicke in die kognitiven Prozesse und zu der Aufmerksamkeit eines Menschen verschaffen. Die erfassten Daten werden meistens als Scanpaths oder Heatmaps visualisiert. Durch Eye-Tracking und den damit erfassten Daten, kann auch die Analyse der Prozessmodelle verbessert werden. In dieser Bachelorarbeit werden die erfassen Eye-Tracking-Daten analysiert, um Aufmerksamkeitsmuster beim Betrachten von Petri-Netzen herausarbeiten zu können. Dazu werden die Daten in das Visualisierungsframework (Blickshift) importiert und zusammen mit den Stimuli visualisiert. Dadurch können verschiedene Aufmerksamkeitsmodelle erfasst werden und somit Urteile über die Effizienz der verschiedenen Petri-Netze (Stimuli) geschlossen werden

IoTDM4BPMN: An IoT-Enhanced Decision Making Framework for BPMN 2.0

The relevance of the Internet of Things (IoT) for Business Process Management (BPM) support is increasing. IoT devices enable the collection and exchange of data over the Internet, whereby each physical device is uniquely identifiable through its embedded computing system. BPM, in turn, is concerned with analyzing, discovering, modeling, executing, and monitoring (digitized) business processes. By enhancing BPM systems with IoT capabilities, real-world data can be gathered and considered during process execution to enhance process monitoring as well as IoT-driven decision making. In this context, the aggregation of low-level IoT data into high-level process-relevant data constitutes a fundamental step towards IoT-driven decisions in business processes. This paper presents IoT Decision Making for Business Process Model and Notation (IoTDM4BPMN) a webbased framework for modeling, executing, and monitoring IoTdriven decisions in real-time. We give insights into the design and implementation of IoTDM4BPMN and provide a case study as a first validation that applies IoTDM4BPMN to the modeling, executing, and monitoring of a real-world IoT-driven decision process

Enabling Conformance Checking for Object Lifecycle Processes

Abstract. In object-aware process management, processes are represented as multiple interacting objects rather than a sequence of activities, enabling data-driven and highly flexible processes. In such flexible scenarios, however, it is crucial to be able to check to what degree the process is executed according to the model (i.e., guided behavior). Conformance checking algorithms (e.g., Token Replay or Alignments) deal with this issue for activity-centric processes based on a process model (e.g., specified as a petri net) and a given event log that reflects how the process instances were actually executed. This paper applies conformance checking algorithms to the behavior of objects. In object-aware process management, object lifecycle processes specify the various states into which corresponding objects may transition as well as the object attribute values required to complete these states. The approach accounts for flexible lifecycle executions using multiple workflow nets and conformance categories, therefore facilitating process analysis for engineers

Towards a Comprehensive BPMN Extension for Modeling IoT-Aware Processes in Business Process Models

Internet of Thing (IoT) devices enable the collection and exchange of data over the Internet, whereas Business Process Management (BPM) is concerned with the analysis, discovery, implementation, execution, monitoring, and evolution of business processes. By enriching BPM systems with IoT capabilities, data from the real world can be captured and utilized during process execution in order to improve online process monitoring and data-driven decision making. Furthermore, this integration fosters prescriptive process monitoring, e.g., by enabling IoT-driven process adaptions when deviations between the digital process and the one actually happening in the real world occur. As a prerequisite for exploiting these benefits, IoT-related aspects of business processes need to be modeled. To enable the use of sensors, actuators, and other IoT objects in combination with process models, we introduce a BPMN 2.0 extension with IoT-related artifacts and events. We provide a first evaluation of this extension by applying it in two case studies for modeling of IoT-aware processes

Mobile Apps for the Management of Gastrointestinal Diseases: Systematic Search and Evaluation Within App Stores

Background: Gastrointestinal diseases are associated with substantial cost in health care. In times of the COVID-19 pandemic and further digitalization of gastrointestinal tract health care, mobile health apps could complement routine health care. Many gastrointestinal health care apps are already available in the app stores, but the quality, data protection, and reliability often remain unclear. Objective: This systematic review aimed to evaluate the quality characteristics as well as the privacy and security measures of mobile health apps for the management of gastrointestinal diseases. Methods: A web crawler systematically searched for mobile health apps with a focus on gastrointestinal diseases. The identified mobile health apps were evaluated using the Mobile Application Rating Scale (MARS). Furthermore, app characteristics, data protection, and security measures were collected. Classic user star rating was correlated with overall mobile health app quality. Results: The overall quality of the mobile health apps (N=109) was moderate (mean 2.90, SD 0.52; on a scale ranging from 1 to 5). The quality of the subscales ranged from low (mean 1.89, SD 0.66) to good (mean 4.08, SD 0.57). The security of data transfer was ensured only by 11 (10.1%) mobile health apps. None of the mobile health apps had an evidence base. The user star rating did not correlate with the MARS overall score or with the individual subdimensions of the MARS (all P\textgreater.05). Conclusions: Mobile health apps might have a positive impact on diagnosis, therapy, and patient guidance in gastroenterology in the future. We conclude that, to date, data security and proof of efficacy are not yet given in currently available mobile health apps

Evaluation and Comparison of Existing Applications and Scientific Publications to Acquire Guidelines for mHealth Applications

Die Bedeutung von mHealth Applikationen, als auch deren Anzahl und Ausführungen nimmt in mobilen Anwendungen zu. Diese erfüllen oft unterschiedliche Qualitätsstandards im Bereich Datenschutz, als auch im Zweck und Nutzen. Aber gerade in einem Bereich, welcher mit teils empfindlichen Gesundheitsdaten umgeht und es um die Gesundheit von Menschen geht, muss unbedingt der Schutz der Nutzer garantiert werden. Daher untersucht diese Arbeit Richtlinien für mHealth Applikationen, welche eingehalten werden sollten. Und gibt einen Überblick über aktuelle Anwendungen. Es werden zuerst wichtige Grundlagen erklärt und auf unterschiedliche medizinische Standards eingegangen, die für das Verständnis der Arbeit wichtig sind. Dann werden ähnliche Arbeiten analysiert und schließlich mit ausgewählten Kriterien Apps aus dem iOS App Store untersucht, um letztendlich sinnvolle Richtlinien für mHealth Applikationen zu finden. Die Ergebnisse zeigen, warum und wie wichtig es ist, dass es vereinheitlichte Standards gibt, um die Daten der Nutzer zu schützen und ihnen sinnvolle mHealth Applikationen angeboten werden, welche mehr Nutzen als Schaden anrichten. Die Arbeit schlägt fünf herausgearbeiteten Kategorien mit den dreizehn Unterkriterien vor. Diese können als Leitlinie verwendet werden, um mHealth Apps besser beurteilen zu können

Dealing With Inaccurate Sensor Data in the Context of Mobile Crowdsensing and mHealth

The technological capabilities and ubiquity of smart mobile devices favor the combined utilization of Ecological Momentary Assessments (EMA) and Mobile Crowdsensing (MCS). In the healthcare domain, this combination particularly enables the collection of ecologically valid and longitudinal data. Furthermore, the context in which these data are collected can be captured through the use of smartphone sensors as well as externally connected sensors. The TrackYourTinnitus (TYT) mobile platform uses these concepts to collect the user's individual subjective perception of tinnitus as well as an objective environmental sound level. However, the sound level data in the TYT database are subject to several possible sensor errors and therefore do not allow a meaningful interpretation in terms of correlation with tinnitus symptoms. To this end, a data-centric approach based on Principal Component Analysis (PCA) is proposed in this paper to cleanse MCS mHealth data sets from erroneous sensor data. To further improve the approach, additional information (i.e., responses to the EMA questionnaire) is considered in the PCA and a prior check for constant values is performed. To demonstrate the practical feasibility of the approach, in addition to TYT data, where it is generally unknown which sensor measurements are actually erroneous, a simulation with generated data was designed and performed to evaluate the performance of the approach with different parameters based on different quality metrics. The results obtained show that the approach is able to detect an average of 29.02% of the errors, with an average false-positive rate of 14.11%, yielding an overall error reduction of 22.74%

Backend Concept of the eSano eHealth Platform for Internet- and Mobile-based Interventions

Mental disorders represent an ongoing challenge to global health and can affect anyone at any age from any region in the world. The response of healthcare providers to mental health disorders still lags behind that of other diseases and a significant number of people who are affected by mental health disorders do not receive adequate treatment. The widespread usage of Internet-connected devices provides new opportunities to deliver treatment to more people using innovative approaches. The groundwork is being laid for the adoption of Internet- and mobile-based interventions, providing mental and behavioral health support to more people and narrowing the treatment gap. This paper discusses the main technical details of the backend API of the eSano eHealth platform as an example for a complex and comprehensive IT-framework for large-scale and flexible Internet- and mobile-based interventions. An overview of eSano is provided and the platform is compared with other technical solutions in the field. In addition, the components of eSano are described and further technical insights are elaborated in more detail. To this end, the work at hand demonstrates the main requirements of the backend API powering eSano, its concepts and the overall developed solution. It will as such inform researchers and practitioners about state-of-the-art backend API development in the eHealth context

Impact of the GDPR on the Development of eHealth Software

The new EU General Data Protection Regulation (GDPR) became effective on May 25, 2018 and regulates how personal data may be processed by companies, government agencies and other organizations in the European Union (EU). Since prior research focused mostly on the GDPR in general, its implications and impact on the development of health software are not as intuitive as one may think. Even though our main goal was to analyze the impact of the GDPR on health software, we have simultaneously covered several other important aspects of complying with the GDPR by researching relevant literature. We have outlined the history and content of the GDPR as well as other regulations like the Federal Data Protection Act (FDPA) and put them into the context of health. As a result, we were able to identify best practices for health-app providers and possibilities on how to comply with specific key aspects of the GDPR. Several other regulations and norms have been considered and illustrated concisely in this thesis. We have subsequently applied or analysis on eSano, the health platform of the University of Ulm. Our results show that eSano is GDPR-compliant with minor room for improvement