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%

Ecological Momentary Assessment (EMA), Mobile Crowdsensing (MCS), and their Combination: A Systematic Review and Analysis

With 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

Integrität und Konsistenz: Daten-Validierung von MARS-G-Fragebögen mit CUE

Die 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

Enhancing ProMoEE and DyVProMo with Additional Features to Foster Empirical Studies in the Context of Process Models Comprehension

Business Process Management (BPM) has become an important factor on management level for enterprises, as it offers the opportunity to increase productivity and lower cost. This has led to a wide use of BPM techniques in the industry, offering the ability to describe processes, improve and automate them or respond to changes quickly. In order to visually represent processes, notations are used. One of the most common is Business Process Model and Notation (BPMN), which is capable of displaying interconnected activities along with resources and other information. A process model that does not accurately represent the real world may lead to a reduction in above benefits. Therefore, enterprises have an interest in skilled experts creating high quality process models. In reality a lot of untrained personnel is involved in the modeling process. Hence, there is interest in efficient ways of helping novices to understand modeling languages. That is why research on the comprehension of process models is being conducted. One area of research is the addition of constructs to the existing notation. In particular, the coloring of modeling elements can help to distinguish and recognize them more easily. In this thesis two pre-existing applications dealing with the assistance of conducting research on modeling comprehension are fostered. One is an application to dynamically change the displayed model elements to reduce complexity or providing help with model element names through the addition of annotations. It is fostered by expanding its functionality to dynamically add predefined colors to the model elements, providing another way of supporting understanding. The other is a survey platform with the ability to create questionnaires including the functionality to view and edit process models. Hence, it aims at the conduction of empirical research on model comprehension. It is improved in its Maintainability, usability and Applicability. Moreover, the first application is fully integrated into the second one, providing the ability to use it for surveys in questionnaires

Data-Driven Evolution of Activity Forms in Object- and Process-Aware Information Systems

Abstract. Object-aware processes enable the data-driven generation of forms based on the object behavior, which is pre-specified by the respective object lifecycle process. Each state of a lifecycle process comprises a number of object attributes that need to be set (e.g., via forms) before transitioning to the next state. When initially modeling a lifecycle process, the optimal ordering of the form fields is often unknown and only a guess of the lifecycle process modeler. As a consequence, certain form fields might be obsolete, missing, or ordered in a non-intuitive manner. Though this does not affect process executability, it decreases the usability of the automatically generated forms. Discovering respective problems, therefore, provides valuable insights into how object- and process-aware information systems can be evolved to improve their usability. This paper presents an approach for deriving improvements of object lifecycle processes by comparing the respective positions of the fields of the generated forms with the ones according to which the fields were actually filled by users during runtime. Our approach enables us to discover missing or obsolete form fields, and additionally considers the order of the fields within the generated forms. Finally, we can derive the modeling operations required to automatically restructure the internal logic of the lifecycle process states and, thus, to automatically evolve lifecycle processes and corresponding forms

Modeling, Executing and Monitoring IoT-Driven Business Rules in BPMN and DMN: Current Support and Challenges

The 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

Progress Determination of a BPM Tool with Ad-Hoc Changes: An Empirical Study

One aspect of monitoring business processes in real-time is to determine their current progress. For any real-time progress determination it is of utmost importance to accurately predict the remaining share still to be executed in relation to the total process. At run-time, however, this constitutes a particular challenge, as unexpected ad-hocchanges of the ongoing business processes may occur at any time. To properly consider such changes in the context of progress determination, different progress variants may be suitable. In this paper, an empirical study with 194 participants is presented that investigates user acceptance of different progress variants in various scenarios. The study aims to identify which progress variant, each visualised by a progress bar, isaccepted best by users in case of dynamic process changes, which usually effect the current progress of the respective progress instance. Theresults of this study allow for an implementation of the most suitablevariant in business process monitoring systems. In addition, the study provides deeper insights into the general acceptance of different progress measurements. As a key observation for most scenarios, the majority of the participants give similar answers, e.g., progress jumps within a progress bar are rejected by most participants. Consequently, it can be assumed that a general understanding of progress exists. This underlines the importance of comprehending the users’ intuitive understanding of progress to implement the latter in the most suitable fashion

Development of a Rule-based Smart Notification Service

With more and more parts of our live getting digitized, the number of notifications rises, that arrive at an inopportune moment interrupting the work of the user. This thesis investigates a possibility to handle notifications in a multi device environment. We implemented a prototype of a multi device smart notification system. The system uses rules to allow, block, or postpone notifications to an opportune moment for the user. As part of the system, we developed an Android application as a client. Android is currently the only operating system that allows to capture and delete notifications from the notification drawer. It suggests the rules based on the past behaviour of the user, making the system adaptable to changes. Furthermore, the user can create their own rules too. The system also synchronizes all notifications across all logged in devices of the user. With the developed system, the user can read their notifications on the device of their choosing, block annoying notifications, or postpone notifications to an opportune moment

Towards Retrograde Process Analysis in Running Legacy Applications

Process 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

LAMP: a monitoring framework for mHealth application research

The usage of mobile applications in healthcare has gained popularity in recent years. In 2018, at least, 10,000 apps related to mental health could be downloaded in the app stores. The popularity of healthcare apps, especially in the field of mental health, is based on in their simplicity in large-scale data collection scenarios used for the improvement of health-related services or research. For these apps, instruments to quantify the quality of an app and repositories for app quality ratings have emerged in recent years. What is rarely considered, however, is the degree of functional correctness of an app, which can have a serious impact on the data collection process and thus on data quality. The increasing restrictions of background services are a challenge for app developers, who need to implement recurring tasks reliably in the background, like the collection of longitudinal data based on questionnaires or sensor measurements. In this paper, we present a monitoring framework to investigate the degree of functional correctness regarding the background service implementation of apps based on notification events. With this framework, we want to enable the large-scale collection of app execution data in the wild to gain more insights into the execution of apps in different execution environments and configurations. The gained knowledge shall help to improve existing applications in the field of mental health and eventually to improve the degree of functional correctness of those apps