Mobile devices such as smartphones and smart watches are ubiquitous companions of humans’ daily life. Since 2014, there are more mobile devices on Earth than humans. Mobile applications utilize sensors and actuators of these devices to support individuals in their daily life. In particular, 24% of the Android applications leverage users’ mobility data. For instance, this data allows applications to understand which places an individual typically visits. This allows providing her with transportation information, location-based advertisements, or to enable smart home heating systems. These and similar scenarios require the possibility to access the Internet from everywhere and at any time. To realize these scenarios 83% of the applications available in the Android Play Store require the Internet to operate properly and therefore access it from everywhere and at any time. Mobile applications such as Google Now or Apple Siri utilize human mobility data to anticipate where a user will go next or which information she is likely to access en route to her destination. However, predicting human mobility is a challenging task. Existing mobility prediction solutions are typically optimized a priori for a particular application scenario and mobility prediction task. There is no approach that allows for automatically composing a mobility prediction solution depending on the underlying prediction task and other parameters. This approach is required to allow mobile devices to support a plethora of mobile applications running on them, while each of the applications support its users by leveraging mobility predictions in a distinct application scenario. Mobile applications rely strongly on the availability of the Internet to work properly. However, mobile cellular network providers are struggling to provide necessary cellular resources. Mobile applications generate a monthly average mobile traffic volume that ranged between 1 GB in Asia and 3.7 GB in North America in 2015. The Ericsson Mobility Report Q1 2016 predicts that by the end of 2021 this mobile traffic volume will experience a 12-fold increase. The consequences are higher costs for both providers and consumers and a reduced quality of service due to congested mobile cellular networks. Several countermeasures can be applied to cope with these problems. For instance, mobile applications apply caching strategies to prefetch application content by predicting which applications will be used next. However, existing solutions suffer from two major shortcomings. They either (1) do not incorporate traffic volume information into their prefetching decisions and thus generate a substantial amount of cellular traffic or (2) require a modification of mobile application code. In this thesis, we present novel human mobility and application usage prediction algorithms for mobile devices. These two major contributions address the aforementioned problems of (1) selecting a human mobility prediction model and (2) prefetching of mobile application content to reduce cellular traffic. First, we address the selection of human mobility prediction models. We report on an extensive analysis of the influence of temporal, spatial, and phone context data on the performance of mobility prediction algorithms. Building upon our analysis results, we present (1) SELECTOR – a novel algorithm for selecting individual human mobility prediction models and (2) MAJOR – an ensemble learning approach for human mobility prediction. Furthermore, we introduce population mobility models and demonstrate their practical applicability. In particular, we analyze techniques that focus on detection of wrong human mobility predictions. Among these techniques, an ensemble learning algorithm, called LOTUS, is designed and evaluated. Second, we present EBC – a novel algorithm for prefetching mobile application content. EBC’s goal is to reduce cellular traffic consumption to improve application content freshness. With respect to existing solutions, EBC presents novel techniques (1) to incorporate different strategies for prefetching mobile applications depending on the available network type and (2) to incorporate application traffic volume predictions into the prefetching decisions. EBC also achieves a reduction in application launch time to the cost of a negligible increase in energy consumption. Developing human mobility and application usage prediction algorithms requires access to human mobility and application usage data. To this end, we leverage in this thesis three publicly available data set. Furthermore, we address the shortcomings of these data sets, namely, (1) the lack of ground-truth mobility data and (2) the lack of human mobility data at short-term events like conferences. We contribute with JK2013 and UbiComp Data Collection Campaign (UbiDCC) two human mobility data sets that address these shortcomings. We also develop and make publicly available a mobile application called LOCATOR, which was used to collect our data sets. In summary, the contributions of this thesis provide a step further towards supporting mobile applications and their users. With SELECTOR, we contribute an algorithm that allows optimizing the quality of human mobility predictions by appropriately selecting parameters. To reduce the cellular traffic footprint of mobile applications, we contribute with EBC a novel approach for prefetching of mobile application content by leveraging application usage predictions. Furthermore, we provide insights about how and to what extent wrong and uncertain human mobility predictions can be detected. Lastly, with our mobile application LOCATOR and two human mobility data sets, we contribute practical tools for researchers in the human mobility prediction domain.

Mobile devices such as smartphones and smart watches are ubiquitous companions of humans’ daily life. Since 2014, there are more mobile devices on Earth than humans. Mobile applications utilize sensors and actuators of these devices to support individuals in their daily life. In particular, 24% of the Android applications leverage users’ mobility data. For instance, this data allows applications to understand which places an individual typically visits. This allows providing her with transportation information, location-based advertisements, or to enable smart home heating systems. These and similar scenarios require the possibility to access the Internet from everywhere and at any time. To realize these scenarios 83% of the applications available in the Android Play Store require the Internet to operate properly and therefore access it from everywhere and at any time. Mobile applications such as Google Now or Apple Siri utilize human mobility data to anticipate where a user will go next or which information she is likely to access en route to her destination. However, predicting human mobility is a challenging task. Existing mobility prediction solutions are typically optimized a priori for a particular application scenario and mobility prediction task. There is no approach that allows for automatically composing a mobility prediction solution depending on the underlying prediction task and other parameters. This approach is required to allow mobile devices to support a plethora of mobile applications running on them, while each of the applications support its users by leveraging mobility predictions in a distinct application scenario. Mobile applications rely strongly on the availability of the Internet to work properly. However, mobile cellular network providers are struggling to provide necessary cellular resources. Mobile applications generate a monthly average mobile traffic volume that ranged between 1 GB in Asia and 3.7 GB in North America in 2015. The Ericsson Mobility Report Q1 2016 predicts that by the end of 2021 this mobile traffic volume will experience a 12-fold increase. The consequences are higher costs for both providers and consumers and a reduced quality of service due to congested mobile cellular networks. Several countermeasures can be applied to cope with these problems. For instance, mobile applications apply caching strategies to prefetch application content by predicting which applications will be used next. However, existing solutions suffer from two major shortcomings. They either (1) do not incorporate traffic volume information into their prefetching decisions and thus generate a substantial amount of cellular traffic or (2) require a modification of mobile application code. In this thesis, we present novel human mobility and application usage prediction algorithms for mobile devices. These two major contributions address the aforementioned problems of (1) selecting a human mobility prediction model and (2) prefetching of mobile application content to reduce cellular traffic. First, we address the selection of human mobility prediction models. We report on an extensive analysis of the influence of temporal, spatial, and phone context data on the performance of mobility prediction algorithms. Building upon our analysis results, we present (1) SELECTOR – a novel algorithm for selecting individual human mobility prediction models and (2) MAJOR – an ensemble learning approach for human mobility prediction. Furthermore, we introduce population mobility models and demonstrate their practical applicability. In particular, we analyze techniques that focus on detection of wrong human mobility predictions. Among these techniques, an ensemble learning algorithm, called LOTUS, is designed and evaluated. Second, we present EBC – a novel algorithm for prefetching mobile application content. EBC’s goal is to reduce cellular traffic consumption to improve application content freshness. With respect to existing solutions, EBC presents novel techniques (1) to incorporate different strategies for prefetching mobile applications depending on the available network type and (2) to incorporate application traffic volume predictions into the prefetching decisions. EBC also achieves a reduction in application launch time to the cost of a negligible increase in energy consumption. Developing human mobility and application usage prediction algorithms requires access to human mobility and application usage data. To this end, we leverage in this thesis three publicly available data set. Furthermore, we address the shortcomings of these data sets, namely, (1) the lack of ground-truth mobility data and (2) the lack of human mobility data at short-term events like conferences. We contribute with JK2013 and UbiComp Data Collection Campaign (UbiDCC) two human mobility data sets that address these shortcomings. We also develop and make publicly available a mobile application called LOCATOR, which was used to collect our data sets. In summary, the contributions of this thesis provide a step further towards supporting mobile applications and their users. With SELECTOR, we contribute an algorithm that allows optimizing the quality of human mobility predictions by appropriately selecting parameters. To reduce the cellular traffic footprint of mobile applications, we contribute with EBC a novel approach for prefetching of mobile application content by leveraging application usage predictions. Furthermore, we provide insights about how and to what extent wrong and uncertain human mobility predictions can be detected. Lastly, with our mobile application LOCATOR and two human mobility data sets, we contribute practical tools for researchers in the human mobility prediction domain.

Im Rahmen des Leistungsansatzes von emotionaler Intelligenz sehen Mayer und Salovey (1997) Emotionsverstaendnis als Voraussetzung für Emotionsregulation. Es sollte nützlich sein zu wissen, wie man sich in bestimmten Situationen fühlen wird. Zur Messung werden unter anderem Vignetten eingesetzt, in denen Emotionen für hypothetische Situationen vorhergesagt werden. Im Gegensatz dazu postulieren Gilbert und Wilson (2003) charakteristische Fehler bei affektiven Vorhersagen, die motivational günstig sind. In der vorliegenden Arbeit wird die Akkuratheit emotionaler Vorhersagen im natürlichen Umfeld untersucht, um dessen adaptiven Wert zu beurteilen. Zunächst sollten Beamtenanwärter ihre Emotionen in einer bedeutenden Testsituation vorhersagen (N=143). Dann wurden studentische Arbeitsgruppen (180 Mitglieder in 43 Gruppen) gebeten, Gefühle zwischen den Mitgliedern zu prognostizieren (Zuneigung, Zufriedenheit mit der Zusammenarbeit, Freude und Ärger). Akkuratheit wurde als geringer Bias (euklidische Distanz) und hohe Korrespondenz (Profilkorrelation) definiert. Das Round Robin Design der zweiten Studie ermöglichte die Varianzzerlegung der Akkuratheit nach Cronbach (1955). In beiden Studien ist ein niedriger Bias adaptiv in Hinblick auf harte Kriterien, auch inkrementell über Intelligenz und Persönlichkeit hinaus. Bias hing teilweise mit Allgemeinwissen zusammen, aber nicht mit Intelligenz. Zusammenhänge zu emotionaler Intelligenz waren inkonsistent. Die Akkuratheit als Korrespondenz ist theoretisch interessant aber deutlich weniger reliabel. Auf Gruppenebene konnte die Korrespondenz Kriterien vorhersagen, aber es zeigte sich keine inkrementelle Validität. Zukünftige Forschung sollte sich auf spezifische Situationen und spezifische Emotionen konzentrieren sowie die Prozesse untersuchen, die emotionalen Vorhersagen zugrunde liegen. / In the ability model of emotional intelligence by Mayer and Salovey (1997), emotional understanding is a prerequisite for emotion regulation. Knowing which emotions occur in which situations should be beneficial and adaptive. One of the subtests for emotional understanding asks for likely emotional reactions in hypothetical situations. In contrast, Gilbert and Wilson (2003) argue that characteristic biases in affective forecasting are adaptive. The current thesis aims to measure accuracy of emotional predictions in a natural setting and examines its adaptive value. In the anxiety study, public officials were asked to predict future emotions in an important test (N=143). The second study focused on freshman student work-groups (N=180 in 43 groups). Group members predicted interpersonal feelings for each other (affection, satisfaction with the collaboration, fun, and anger). In both studies, accuracy of emotional predictions is defined as low bias (i.e. Euclidean distance) and high correspondence (i.e. profile correlation). The round robin design in the work-group study also allows to decompose accuracy following Cronbach (1955). In both studies, a low bias was adaptive in terms of strong criteria, also incrementally over and above intelligence and personality alone. Accuracy was partly related to general knowledge but not to intelligence. Associations to emotional intelligence were inconsistent. Accuracy as correspondence is theoretically interesting but much less reliable. There is some evidence for its adaptive value on a group level but no indication of incremental validity. Future research should focus on specific situations and specific emotions. Also, processes underlying affective forecasts should be evaluated in detail.

Diese Arbeit ist ein Überblick über die Ideen und Methoden der dynamischen stochastischen Modellierung von normalverteilten und nicht-normalverteilten Prozessen. Nach einer Einführung der allgemeinen Modellform werden Aussagemöglichkeiten wie Filtern, Glätten und Vorhersagen diskutiert und das Problem der Identifikation unbekannter Hyperparameter behandelt. Die allgemeinen Ausführungen werden an zwei Fallstudien, einer Zeitreihe des mittleren jährlichen Grundwasserspiegels und einer Zeitreihe von Tagesmittelwerten von SO2-Emissionen illustriert. (Autorenref.) / Series: Forschungsberichte / Institut für Statistik

Das übergeordnete Ziel der Arbeit war die Überprüfung der Vorhersagbarkeit unterschiedlicher Outcome-Kriterien bei Patienten in stationärer berufsdermatologischer Rehabilitation mithilfe der Rehamotivation und weiterer theoretisch relevanter Variablen. Zur Anwendung kamen der Rehamotivationsfragebogen PAREMO-20, Verfahren zur Erfassung von psychischer Belastung (Marburger Hautfragebogen, MHF), Krankheitsbewältigung, Selbstwirksamkeit und Lebensqualität sowie soziodemographische, krankheits- und behandlungsbezogene Daten. In einem ersten Schritt ging es um die teststatistische Überprüfung der Untersuchungsverfahren, insbesondere des PAREMO-20 bei der vorliegenden Patientengruppe (N=424). Alle Verfahren erwiesen sich als für die Anwendung bei dermatologischen Patienten geeignet. Für den PAREMO-20 ließen sich die aus der allgemeinen Rehabilitationsforschung bekannten teststatistischen Merkmale weitgehend replizieren. Als Nächstes erfolgte die Überprüfung der prädiktiven Validität dieser diagnostischen Informationen bezüglich verschiedener subjektiver und objektiver Kriterien des Behandlungsergebnisses. Im Zentrum standen hierbei deskriptiv ermittelte sowie mithilfe des Reliable Change Index ermittelte Gruppierungen von Patienten hinsichtlich ihrer Behandlungsfortschritte (verbessert, verschlechtert, gleich geblieben). Hierzu wurden Diskriminanzanalysen und logistische Regressionsanalysen durchgeführt. Als Ergebnis zeigte sich, dass Kriterien der objektiven und subjektiven Hautgesundheit mit den eingesetzten Verfahren nicht vorhersagbar sind. Der PAREMO-20 besitzt hier keine prädiktive Validität. Dagegen konnte die Veränderung der Lebensqualität als indirektes Erfolgskriterium durch die psychische Verfassung zu Beginn der 3-wöchigen Maßnahme mit dem Marburger Hautfragebogen vorhergesagt werden. Zudem wurden Möglichkeiten der Weiterentwicklung der Instrumente diskutiert und Implikationen für Forschung und Praxis abgeleitet.