The thesis gives an overview of the techniques used up to now for responder identification and it proposes a new method for systematic search for responders. The responder identification method consists of the following three steps:

1. Identification of prognostic factors (e.g. via Cox-PH model on the standard treatment arm)

2. Identification of patients in the new treatment arm, who's survival is badly estimated by the prognostic model (e.g. via search for outliers in the deviance or martingale residuals)

3. Identification of predictive factors, which describe common features of the patients with residual outliers, namely the positive and negative responders (e.g. via regression tree or bump hunting analysis, or via the suggested stabilized bump hunting procedure)

The method is evaluated with a simulation study and applied on the EMIAT data se

In constraint programming, combinatorial problems are specified

declaratively in terms of constraints. Constraints are relations over

problem variables that define the space of solutions by specifying

restrictions on the values that variables may take simultaneously. To

solve problems stated in terms of constraints, the constraint

programmer typically combines chronological backtracking with

constraint propagation that identifies infeasible value combinations

and prunes the search space.

In recent years, constraint programming has emerged as a key

technology for combinatorial optimization in industrial

applications. In this success, global constraints have been playing a

vital role. Global constraints are carefully designed abstractions

that, in a concise and natural way, allow to model problems that arise

in different fields of application. For example, the alldiff

constraint allows to state that variables must take pairwise distinct

values; it has numerous applications in timetabling and scheduling.

In school timetabling, we are required to schedule a given set of

meetings between students and teachers s.t. the resulting timetables

are feasible and acceptable to all people involved. Since schools

differ in their educational policies, the school-timetabling problem

occurs in several variations. Nevertheless, a set of entities and

constraints among them exist that are common to these variations. This

common core still gives rise to NP-complete combinatorial problems.

In the first place, this thesis proposes to model the common core of

school-timetabling problems by means of global constraints. The

presentation continues with a series of operational enhancements to

the resulting problem solver which are grounded on the "track

parallelization problem" (TPP). A TPP is specified by a set of task

sets which are called "tracks". The problem of solving a TPP

consists in scheduling the tasks s.t. the tracks are processed in

parallel. We show how to infer TPPs in school timetabling and we

investigate two ways of TPP propagation: On the one hand, we utilize

TPPs to down-size our models. On the other hand, we propagate TPPs to

prune the search space. To this end, we introduce the TPP

constraint along with a suitable constraint solver for modeling and

solving TPPs in a finite-domain constraint programming framework.

To investigate our problem solvers' behavior, we performed a

large-scale empirical study. When designing the experiment, the top

priority was to obtain results that are both reliable from a

statistical point of view and practically relevant. To this end, the

sample sizes have been chosen accordingly - for each school, our

problem set contains 1000 problems - and the problems have been

generated from detailed models of ten representative schools. Our

timetabling engine essentially embeds network-flow techniques and

value sweep pruning into chronological backtracking.

Moräste sind eine Axiomatisierung von Jensens Feinstrukturtheorie. Die vorliegende Arbeit stellt eine mögliche Präzisierung dieser Aussage dar.

Die vorliegende Arbeit präsentiert einen Ansatz zur Spezifikation und Implementierung von kontextadaptiven Anwendungen in einer Ubiquitous Computing Umgebung. Grundlegend ist dabei das Konzept der kontextadaptiven Dienstnutzung, die sowohl die kontextadaptive Selektion als auch Ausführung von Diensten umfasst.

Die kontextadaptive Selektion erweitert grundlegende Techniken der Dienstvermittlung insofern, dass ein Matching nicht ausschließlich durch die Spezifikation von gewünschten Dienstattributen erfolgt, sondern auch Kontextinformationen Berücksichtigung finden. Die Ausführung eines Dienstes kann ebenfalls an kontextuelle Bedingungen geknüpft werden. Eine realisierte Kombination von kontextadaptiver Selektion und Ausführung ermöglicht eine sowohl personalisierte als auch situationsbezogene Bereitstellung von Diensten.

Kern der kontextadaptiven Dienstnutzung ist dabei ein Datenzentrisches Protokoll, welches die Weiterleitung (Routing) von Anwendungsdaten anhand kontextueller Einschränkungen erlaubt. Dieser Ansatz gestattet neben der kontextadaptiven Nutzung individueller Dienste auch die spontane Komposition von Diensten in einer Ubiquitous Computing Umgebung.

Ferner wird ein Konzept zur dynamischen Rollenverwaltung für Endgeräte in einer Ubiquitous Computing Umgebung entwickelt und ein Verfahren zur Konstruktion von Kontextinformationen innerhalb eines Ad-hoc-Sensornetzwerks vorgestellt.