Predicting the ability to lip-read in children who have a hearing loss

This study aims to discover if a variety of factors related to a child\u27s education and audiologic history predict a child\u27s ability to lip-read

Applying Process Mining Algorithms in the Context of Data Collection Scenarios

Despite the technological progress, paper-based questionnaires are still widely used to collect data in many application domains like education, healthcare or psychology. To facilitate the enormous amount of work involved in collecting, evaluating and analyzing this data, a system enabling process-driven data collection was developed. Based on generic tools, a process-driven approach for creating, processing and analyzing questionnaires was realized, in which a questionnaire is defined in terms of a process model. Due to this characteristic, process mining algorithms may be applied to event logs created during the execution of questionnaires. Moreover, new data that might not have been used in the context of questionnaires before may be collected and analyzed to provide new insights in regard to both the participant and the questionnaire. This thesis shows that process mining algorithms may be applied successfully to process-oriented questionnaires. Algorithms from the three process mining forms of process discovery, conformance checking and enhancement are applied and used for various analysis. The analysis of certain properties of discovered process models leads to new ways of generating information from questionnaires. Different techniques for conformance checking and their applicability in the context of questionnaires are evaluated. Furthermore, new data that cannot be collected from paper-based questionnaires is used to enhance questionnaires to reveal new and meaningful relationships

Towards the Discovery of Object-Aware Processes

There has been a huge body of research in order to reduce manual efforts in creating executable process models through the automated discovery of process models from the event logs created by information systems. Regarding activity-centric processes, such event logs comprise case ids and events related to the execution of process activities. However, there exist alternative process management paradigms, such as object-aware processes, for which existing algorithms fail to discover a sound model. These algorithms do not treat data as first-class citizens, but solely rely on the information from event logs. In consequence, existing discovery algorithms are insufficient for discovering object-aware processes. To address this issue, discovery algorithms need to consider additional data sources (e.g., existing forms). This paper discusses the need for dedicated discovery techniques in object-aware processes

A Dashboard-based Approach for Monitoring Object-Aware Processes

Data (e.g., event logs) gathered during the execution of business processes enable valuable insights into actual process performance. To leverage this knowledge, these data should be analyzed and interpreted in the context of the respective processes. Corresponding analyses allow for a comprehensive process monitoring as well as the discovery of weaknesses and potential process improvements. This also applies to object-aware processes, where data drives process execution and, thus, is treated as first-class citizen. This paper introduces a dashboard with advanced monitoring functions for object-aware processes

Towards Real-Time Progress Determination of Object-Aware Business Processes

To stay competitive, companies need to continuously improve and evolve their business processes. In this endeavour, business process optimisations and improvements are key elements. In particular, the monitoring of business processes enables the early discovery of problems and errors already during process enactment. Two approaches can be pursued to achieve this: real-time, also called online monitoring, and offline monitoring. A subtask of real-time monitoring is to determine the current progress of a business process, which is particularly challenging if the latter is composed of loosely coupled, smaller processes that interact with each other, like object lifecycle processes in data-centric approaches to BPM, which result in large process structures. This position paper discusses the challenges of determining the progress of such object-aware processes in real-time and defines research questions that need to be investigated in further work

Culture and the disposition effect

We study the relationship between national culture and the disposition effect by investigating international differences in the degree of investors’ disposition effect. We utilize brokerage data of 387,993 traders from 83 countries and find great variation in the degree of the disposition effect across the world. We find that the cultural dimensions of long-term orientation and indulgence help to explain why certain nationalities are more prone to the disposition effect. We also find support on an international level for the role of age and gender in explaining the disposition effect

Who trades cryptocurrencies, how do they trade it, and how do they perform? Evidence from brokerage accounts

We investigate the demographic characteristics, trading patterns, and performance of 465.926 brokerage accounts with respect to cryptocurrency trading. We find that cryptocurrency trading became increasingly popular across individuals of all different groups of age, gender, and trading patterns. Yet, men are more likely to engage in cryptocurrency trading, trade more frequently, and more speculative, respectively. As a result, men realize lower returns. Furthermore, we find that investors vary their trading patterns across different asset classes

Are cryptocurrency traders pioneers or just risk-seekers? Evidence from brokerage accounts

Are cryptocurrency traders driven by a desire to invest in a new asset class to diversify their portfolio or are they merely seeking to increase their levels of risk? To answer this question, we use individual-level brokerage data and study their behavior in stock trading around the time they engage in their first cryptocurrency trade. We find that when engaging in cryptocurrency trading investors simultaneously increase their risk-seeking behavior in stock trading as they increase their trading intensity and use of leverage. The increase in risk-seeking in stocks is particularly pronounced when volatility in cryptocurrency returns is low, suggesting that their overall behavior is driven by excitement-seeking

A One-Dimensional Kalman Filter for Real-Time Progress Prediction in Object Lifecycle Processes

Real-time monitoring of business processes offers promising perspectives to discover problems and optimisation potentials. Early detection is a key part in this endeavour. One crucial aspect of real-time monitoring is to determine the current progress of a running business process. This is particularly challenging for business processes that consist of a multitude of loosely coupled, smaller processes that interact with each other, like object lifecycle processes in data-centric approaches to business process management. In this paper, an approach to predict the remaining portion of the process path to be still executed in relation to the overall process is proposed. This prediction is based on a one-dimensional Kalman Filter. As a major benefit of this approach, real-time progress determination can start directly with the first run of the process, i.e., without need for comprehensive event log data. This becomes possible due to the procedure applied by the Kalman Filter, which requires no log data. A quantitative study with 250 progress estimations for large object lifecycle processes results in a deviation of the average estimated progress from the real progress, calculated after the completion of the process, of about 5%. This emphasises that reasonable progress predictions are possible even in the absence of an event log, as it is the case when deploying new or changed processes to the run-time system

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