1752 research outputs found
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Robotic Process Automation - A Systematic Mapping Study and Classification Framework
Full text linkRobotic 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
Towards Retrograde Process Analysis in Running Legacy Applications
Full text linkProcess mining algorithms are highly dependent on the existence and quality of event logs. In many cases, however, software systems (e.g., legacy systems) do not leverage workflow engines capable of producing high-quality event logs for process mining algorithms. As a result, the application of process mining algorithms is drastically hampered for such legacy systems. The generation of suitable event data from running legacy software systems, therefore, would foster approaches such as process mining, data-based process documentation, and process-oriented software migration of legacy systems. This paper discusses the need for dedicated event log generation approaches in this context
Integrität und Konsistenz: Daten-Validierung von MARS-G-Fragebögen mit CUE
No full textDie Integrität und Konsistenz von Daten ist essentiell die maschinelle Verarbeitung und Informationsextraktion. Wissenschaftler und Ingenieure investieren viel Zeit und Energie in die Bereiningung von Datensätzen. In dieser Arbeit wird validiert, ob die Programmiersprache CUE (Configure Unify Execute) in der Lage ist die Integrität und Korrektheit von Daten zu gewährleisten. CUE wurde entwickelt Daten, Schema- und Konfiguration-Dateien zu validieren. In dieser Arbeit wird CUE verwendet, Reviews der MARS-G-Fragebögen zu validieren. In verschiedenen Phasen der MHAD-Datenerhebung wird überprüft, ob CUE in der Lage ist die Daten Integrität und Konsistenz zu verbessern. Hierbei wurden fünf verschiedene Testfälle erstellt, um verschiedene Aspekte von CUE zu testen. Die Ergebnisse der Tests zeigen, das CUE in der Lage ist die Integrität und Korrektheit in verschiedenen Phasen der MHAD-Datenerhebung zu verbessern. Jedoch wird die Anwendung von CUE durch eine fehlende Dokumentation und nicht einheitliche Funktionalität erschwert
Towards a Comprehensive BPMN Extension for Modeling IoT-Aware Processes in Business Process Models
Full text linkInternet 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
Analyse von Mustern der Aufmerksamkeit beim Betrachten von Petri-Netzen
Full text linkPetri-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
Modeling, Executing and Monitoring IoT-Driven Business Rules in BPMN and DMN: Current Support and Challenges
Full text linkThe involvement of the Internet of Things (IoT) in Business Process Management (BPM) solutions is continuously increasing. While BPM enables the modeling, implementation, execution, monitoring, and analysis of business processes, IoT fosters the collection and exchange of data over the Internet. By enriching BPM solutions with real-world IoT data both process automation and process monitoring can be improved. Furthermore, IoT data can be utilized during process execution to realize IoT-driven business rules that consider the state of the physical environment. The aggregation of low-level IoT data into processrelevant, high-level IoT data is a paramount step towards IoT-driven business processes and business rules respectively. In this context, Business Process Modeling and Notation (BPMN) and Decision Model and Notation (DMN) provide support to model, execute, and monitor IoTdriven business rules, but some challenges remain. This paper derives the challenges that emerge when modeling, executing, and monitoring IoT-driven business rules using BPMN 2.0 and DMN standards
Backend Concept of the eSano eHealth Platform for Internet- and Mobile-based Interventions
Full text linkMental 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
Konzipierung und Implementierung einer Anwendung zur Verwaltung, Versionierung und Verteilung von App-Metadaten
No full textMobile Anwendungen, sogenannte Apps, werden Endnutzern über App Stores zur Verfügung gestellt. Hierbei werden immer nur die aktuellsten Versionen der Apps angeboten. Es ist nicht möglich, eine spezifische Version einer Anwendungsdatei herunterzuladen. Für Anwendungen, die auf eine Versionshistorie mobiler Anwendungen angewiesen sind, ist dieser Sachverhalt problematisch. Es existieren beispielsweise Plattformen zur Bewertung von Apps nach wissenschaftlichen Methoden. Diese benötigen eine Fixierung der App-Versionen in einer Historie, um die gewonnenen Erkenntnisse auch zukünftig nachvollziehbar zu halten. Die im Rahmen dieser Arbeit konzipierte und prototypisch implementierte Metadata Management Application ermöglicht es, eine solche Versionshistorie zur Verwaltung und Verteilung von App-Metadaten zu erstellen. Es wird beschrieben, wie Metadaten aus dem, im Kontext dieser Arbeit fokussierten, Google Play Store mit Scraper Programmen extrahiert werden können. Weiter wird beschrieben wie Android Packages, die auf Android-basierten Geräten ausführbaren Anwendungsdateien, heruntergeladen, gespeichert und analysiert werden können. Durch eine Betrachtung der Referenzimplementierung werden ausgewählte Anwendungsbereiche anhand von Quelltextausschnitten vorgestellt
Ecological Momentary Assessment (EMA), Mobile Crowdsensing (MCS), and their Combination: A Systematic Review and Analysis
No full textWith mobile devices having become a central part of our daily life, the question arises how we can use smartphones and wearable devices to resolve issues we face. Therefore, researchers used smartphones to assess their subjects’ state remotely in form of an ecological momentary assessment (EMA). To collect a larger amount of data or understand their subjects more deeply the researchers can not only use their subjects’ inputs but also a sensor adjacent to them as part of Mobile Crowd Sensing (MCS) approach. In the context of this master thesis we therefore explore how researches adopted the topic of MCS in the context of EMA through a systematic literature review. We found that most studies do not use the additional information provided by sensors in their study design. Additionally, studies showed similar characteristics regarding the assessment strategy with many of them being focused on self-assessments. As a result we identified potential opportunities to diversify our knowledge regarding the adoption of EMA and MCS
Dealing With Inaccurate Sensor Data in the Context of Mobile Crowdsensing and mHealth
Full text linkThe 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%