The touchscreen, as an alternative user interface for applications that normally require mice and keyboards, has become more and more commonplace, showing up on mobile devices, on vending machines, on ATMs and in the control panels of machines in industry, where conventional input devices cannot provide intuitive, rapid and accurate user interaction with the content of the display. The exponential growth in processing power on the PC, together with advances in understanding human communication channels, has had a significant effect on the design of usable, human-factored interfaces on touchscreens, and on the number and complexity of applications available on touchscreens. Although computer-driven touchscreen interfaces provide programmable and dynamic displays, the absence of the expected tactile cues on the hard and static surfaces of conventional touchscreens is challenging interface design and touchscreen usability, in particular for distracting, low-visibility environments. Current technology allows the human tactile modality to be used in touchscreens. While the visual channel converts graphics and text unidirectionally from the computer to the end user, tactile communication features a bidirectional information flow to and from the user as the user perceives and acts on the environment and the system responds to changing contextual information. Tactile sensations such as detents and pulses provide users with cues that make selecting and controlling a more intuitive process. Tactile features can compensate for deficiencies in some of the human senses, especially in tasks which carry a heavy visual or auditory burden.

In this study, an interaction concept for tactile touchscreens is developed with a view to employing the key characteristics of the human sense of touch effectively and efficiently, especially in distracting environments where vision is impaired and hearing is overloaded. As a first step toward improving the usability of touchscreens through the integration of tactile effects, different mechanical solutions for producing motion in tactile touchscreens are investigated, to provide a basis for selecting suitable vibration directions when designing tactile displays. Building on these results, design know-how regarding tactile feedback patterns is further developed to enable dynamic simulation of UI controls, in order to give users a sense of perceiving real controls on a highly natural touch interface. To study the value of adding tactile properties to touchscreens, haptically enhanced UI controls are then further investigated with the aim of mapping haptic signals to different usage scenarios to perform primary and secondary tasks with touchscreens. The findings of the study are intended for consideration and discussion as a guide to further development of tactile stimuli, haptically enhanced user interfaces and touchscreen applications.

Physiological computing is an interesting and promising concept to widen the communication channel between the (human) users and computers, thus allowing an increase of software systems' contextual awareness and rendering software systems smarter than they are today. Using physiological inputs in pervasive computing systems allows re-balancing the information asymmetry between the human user and the computer system: while pervasive computing systems are well able to flood the user with information and sensory input (such as sounds, lights, and visual animations), users only have a very narrow input channel to computing systems; most of the time, restricted to keyboards, mouse, touchscreens, accelerometers and GPS receivers (through smartphone usage, e.g.). Interestingly, this information asymmetry often forces the user to subdue to the quirks of the computing system to achieve his goals -- for example, users may have to provide information the software system demands through a narrow, time-consuming input mode that the system could sense implicitly from the human body. Physiological computing is a way to circumvent these limitations; however, systematic means for developing and moulding physiological computing applications into software are still unknown.

This thesis proposes a methodological approach to the creation of physiological computing applications that makes use of component-based software engineering. Components help imposing a clear structure on software systems in general, and can thus be used for physiological computing systems as well. As an additional bonus, using components allow physiological computing systems to leverage reconfigurations as a means to control and adapt their own behaviours. This

adaptation can be used to adjust the behaviour both to the human and to the available computing environment in terms of resources and available devices - an activity that is crucial for complex physiological computing systems. With the help of components and reconfigurations, it is possible to structure the functionality of physiological computing applications in a way that makes them manageable and extensible, thus allowing a stepwise and systematic extension of a system's intelligence.

Using reconfigurations entails a larger issue, however. Understanding and fully capturing the behaviour of a system under reconfiguration is challenging, as the system may change its structure in ways that are difficult to fully predict. Therefore, this thesis also introduces a means for formal verification of reconfigurations based on assume-guarantee contracts. With the proposed assume-guarantee contract framework, it is possible to prove that a given system design (including component behaviours and reconfiguration specifications) is satisfying real-time properties expressed as assume-guarantee contracts using a variant of real-time linear temporal logic introduced in this thesis - metric interval temporal logic for reconfigurable systems.

Finally, this thesis embeds both the practical approach to the realisation of physiological computing systems and formal verification of reconfigurations into Scrum, a modern and agile software development methodology. The surrounding methodological approach is intended to provide a frame for the systematic development of physiological computing systems from first psychological findings to a working software system with both satisfactory functionality and software quality aspects.

By integrating practical and theoretical aspects of software engineering into a self-contained development methodology, this thesis proposes a roadmap and guidelines for the creation of new physiological computing applications.

Ortsbezogene Dienste in Gebäuden (Indoor Location-based Services, I-LBS) sammeln, verarbeiten und stellen Informationen bereit, die in Abhängigkeit der Aufenthaltsorte ihrer Zielobjekte sowie auf der Grundlage eines Umgebungsmodells des jeweiligen Gebäudes berechnet werden. Die Anwendungsbereiche von I-LBS reichen dabei von personenbezogenen Diensten wie z.B. Navigation, ortsbezogenen Benachrichtigungen und Ressourcen-Suche über organisationsbezogene Dienste zur Steuerung und Optimierung von Arbeitsprozessen und Ressourcen bis hin zu Anwendungen, die personen- und organisationsübergreifend Informationen über Zielobjekte und deren jeweilige Umgebungen austauschen. Für die Erzeugung und Nutzung von Umgebungsmodellen ergeben sich mehrere Herausforderungen: Bei der automatischen Erzeugung eines Modells müssen die Eigenschaften eines Gebäudes so erfasst werden, dass dadurch nicht nur Orte gesucht oder kürzeste Wege ermittelt werden können, sondern auch Navigationsdaten für die Berechnung von Navigationsanweisungen zur Verfügung stehen. Dabei stellt sich die Frage, wie ein solches Umgebungsmodell aufgebaut sein muss und wie es sich berechnen lässt. Zur Bereitstellung für verschiedene Dienste wird ein geeignetes Format benötigt, in dem alle relevanten geometrischen, symbolischen und topologischen Informationen über ein Gebäude abgebildet sind. Eine wichtige Rolle spielen dabei auch unterschiedliche Positionierungsverfahren, die miteinander kombiniert werden können und deren heterogene Positionsdaten integriert werden müssen. Für die Realisierung von Indoor-Navigationssystemen besteht sowohl bei stationären als auch bei mobilen Lösungen eine Herausforderung in der Berechnung von dedizierten Navigationsanweisungen. Diese leiten einen Benutzer anhand von textbasierten oder graphischen Hinweisen zu seinem Ziel, dabei können auch Landmarken in die Navigationsanweisungen mit einbezogen werden. Im Rahmen dieser Dissertation werden neue Lösungen für die vorgenannten Probleme aufgezeigt, und die entwickelten Konzepte werden anhand von Simulationen sowie praktischen Umsetzungen diskutiert.

Advancements in technology have resulted in unique changes in the way people interact with music today: Small, portable devices allow listening to it everywhere and provide access to thousands or, via streaming, even millions of songs. In addition, all played tracks can be logged with an accuracy down to the second. So far, these music listening histories are mostly used for music recommendation and hidden from their actual creators. But people may also benefit from this data more directly: as memory extensions that allow retrieving the name of a title, for rediscovering old favorites and reflecting about their lives. Additionally, listening histories can be representations of the implicit relationships between musical items. In this thesis, I discuss the contents of

these listening histories and present software tools that give their owners the chance to work with them.

As a first approach to understanding the patterns contained in listening histories I give an overview of the relevant literature from musicology, human-computer-interaction and music information retrieval. This literature review identifies the context as a main influence for listening: from the musical and temporal to the demographical and social.

I then discuss music listening histories as digital memory extensions and a part of lifelogging data. Based on this notion, I present what an ideal listening history would look like and how close the real-world implementations come. I also derive a design space, centered around time, items and listeners, for this specific type of data and shortcomings of the real-world data regarding the previously identified contextual factors.

The main part of this dissertation describes the design, implementation and evaluation of visualizations for listening histories. The first set of visualizations presents listening histories in the context of lifelogging, to allow analysing one’s behavior and reminiscing. These casual information visualizations vary in complexity and purpose. The second set is more concerned with the musical context and the idea that listening histories also represent relationships between musical items. I present approaches for improving music

recommendation through interaction and integrating listening histories in regular media players.

The main contributions of this thesis to HCI and information visualization are: First, a deeper understanding of relevant aspects and important patterns that make a person’s listening special and unique. Second, visualization prototypes and a design space of listening history visualizations that show approaches how to work with temporal personal data in a lifelogging context. Third, ways to improve recommender systems and existing software through the notion of seeing relationships between musical items in listening histories. Finally, as a meta-contribution, the casual approach of all visualizations also helps in providing non-experts with access to their own data, a future challenge for

researchers and practitioners alike.

Knowledge extraction from structured data aims for identifying valid, novel, potentially useful, and ultimately understandable patterns in the data. The core step of this process is the application of a data mining algorithm in order to produce an enumeration of particular patterns and relationships in large databases. Clustering is one of the major data mining tasks and aims at grouping the data objects into meaningful classes (clusters) such that the similarity of objects within clusters is maximized, and the similarity of objects from different clusters is minimized.

In this thesis, we advance the state-of-the-art data mining algorithms for analyzing structured data types. We describe the development of innovative solutions for hierarchical data mining. The EM-based hierarchical clustering method ITCH (Information-Theoretic Cluster Hierarchies) is designed to propose solid solutions for four different challenges. (1) to guide the hierarchical clustering algorithm to identify only meaningful and valid clusters. (2) to represent each cluster content in the hierarchy by an intuitive description with e.g. a probability density function. (3) to consistently handle outliers. (4) to avoid difficult parameter settings. ITCH is built on a hierarchical variant of the information-theoretic principle of Minimum Description Length (MDL). Interpreting the hierarchical cluster structure as a statistical model of the dataset, it can be used for effective data compression by Huffman coding. Thus, the achievable compression rate induces a natural objective function for clustering, which automatically satisfies all four above mentioned goals. The genetic-based hierarchical clustering algorithm GACH (Genetic Algorithm for finding Cluster Hierarchies) overcomes the problem of getting stuck in a local optimum by a beneficial combination of genetic algorithms, information theory and model-based clustering. Besides hierarchical data mining, we also made contributions to more complex data structures, namely objects that consist of

mixed type attributes and skyline objects. The algorithm INTEGRATE performs integrative mining of heterogeneous data, which is one of the major challenges in the next decade, by a unified view on numerical and categorical information in clustering. Once more, supported by the MDL principle, INTEGRATE guarantees the usability on real world data. For skyline objects we developed SkyDist, a similarity measure for comparing different skyline objects, which is therefore a first step towards performing data mining on this kind of data structure. Applied in a recommender system, for example SkyDist can be used for pointing the user to alternative car types, exhibiting a similar price/mileage behavior like in his original query. For mining graph-structured data, we developed different approaches that have the ability to detect patterns in static as well as in dynamic networks. We confirmed the practical feasibility of our novel approaches on large real-world case studies ranging from medical brain data to biological yeast networks.

In the second part of this thesis, we focused on boosting the knowledge extraction process. We achieved this objective by an intelligent adoption of Graphics Processing Units (GPUs). The GPUs have evolved from simple devices for

the display signal preparation into powerful coprocessors that do not only support typical computer graphics tasks but can also be used for general numeric and symbolic computations. As major advantage, GPUs provide extreme parallelism combined with a high bandwidth in memory transfer at low cost. In this thesis, we propose algorithms for computationally expensive data mining tasks like similarity search and different clustering paradigms which are designed for the highly parallel environment of a GPU, called CUDA-DClust and CUDA-k-means. We define a multi-dimensional index structure which is particularly suited to support similarity queries under the restricted programming mod

Variable selection and model choice are of major concern in many statistical

applications, especially in regression models for high-dimensional data.

Boosting is a convenient statistical method that combines model fitting with

intrinsic model selection. We investigate the impact of base-learner

specification on the performance of boosting as a model selection procedure. We

show that variable selection may be biased if the base-learners have different

degrees of flexibility, both for categorical covariates and for smooth effects

of continuous covariates. We investigate these problems from a theoretical

perspective and suggest a framework for unbiased model selection based on a

general class of penalized least squares base-learners. Making all base-learners

comparable in terms of their degrees of freedom strongly reduces the selection

bias observed with naive boosting specifications. Furthermore, the definition of

degrees of freedom that is used in the smoothing literature is questionable in

the context of boosting, and an alternative definition is theoretically derived.

The importance of unbiased model selection is demonstrated in simulations and in

an application to forest health models.

A second aspect of this thesis is the expansion of the boosting algorithm to new

estimation problems: by using constraint base-learners, monotonicity constrained

effect estimates can be seamlessly incorporated in the existing boosting

framework. This holds for both, smooth effects and ordinal variables.

Furthermore, cyclic restrictions can be integrated in the model for smooth

effects of continuous covariates. In particular in time-series models, cyclic

constraints play an important role. Monotonic and cyclic constraints of smooth

effects can, in addition, be extended to smooth, bivariate function estimates.

If the true effects are monotonic or cyclic, simulation studies show that

constrained estimates are superior to unconstrained estimates. In three case

studies (the modeling the presence of Red Kite in Bavaria, the modeling of

activity profiles for Roe Deer, and the modeling of deaths caused by air

pollution in Sao Paulo) it is shown that both constraints can be integrated

in the boosting framework and that they are easy to use.

All described results were included in the R add-on package mboost.

Advances of modern technologies produce huge amounts of data in various fields, increasing the need for efficient and effective data mining tools to uncover the information contained implicitly in the data. This thesis mainly aims to propose innovative and solid algorithms for data mining from a novel perspective: synchronization.

Synchronization is a prevalent phenomenon in nature that a group of events spontaneously come into co-occurrence with a common rhythm through mutual interactions. The mechanism of synchronization allows controlling of complex processes by simple operations based on interactions between objects.

The first main part of this thesis focuses on developing the innovative algorithms for data mining. Inspired by the concept of synchronization, this thesis presents Sync (Clustering by Synchronization), a novel approach to clustering. In combination with the Minimum Description Length principle (MDL), it allows discovering the intrinsic clusters without any data distribution assumptions and parameters setting. In addition, relying on the dierent dynamic behaviors of objects during the process towards synchronization,the algorithm SOD (Synchronization-based Outlier Detection) is further proposed. The outlier objects can be naturally flagged by the denition of Local Synchronization Factor (LSF). To cure the curse of dimensionality in clustering,a subspace clustering algorithm ORSC is introduced which automatically detects clusters in subspaces of the original feature space. This approach proposes a weighted local interaction model to ensure all objects in a common cluster, which accommodate in arbitrarily oriented subspace, naturally move together. In order to reveal the underlying patterns in graphs, a graph partitioning approach RSGC (Robust Synchronization-based Graph Clustering) is presented. The key philosophy of RSGC is to consider graph clustering as a dynamic process towards synchronization. Inherited from the powerful concept of synchronization, RSGC shows several desirable properties that

don't exist in other competitive methods. For all presented algorithms, their efficiency and eectiveness are thoroughly analyzed. The benets over traditional approaches are further demonstrated by evaluating them on synthetic as well as real-world data sets.

Not only the theory research on novel data mining algorithms, the second main part of the thesis focuses on brain network analysis based on Diusion Tensor Images (DTI). A new framework for automated white matter tracts clustering is rst proposed to identify the meaningful ber bundles in the

Human Brain by combining ideas from time series mining with density-based clustering. Subsequently, the enhancement and variation of this approach is discussed allowing for a more robust, efficient, or eective way to find hierarchies of ber bundles. Based on the structural connectivity network, an automated prediction framework is proposed to analyze and understand the abnormal patterns in patients of Alzheimer's Disease.