Fakultät für Mathematik, Informatik und Statistik - Digitale Hochschulschriften der LMU - Teil 02/02

The combination of survey data with more objective information, such as administrative

records, is a promising innovation within social science research. The advantages of such

projects are manifold, but implementing them also bears challenges to be considered.

For example, the survey respondents typically have to consent to the linking of external

data sources and interviewers have to feel comfortable with this task.

This dissertation investigates whether and to what extent the interviewers have an influence

on the willingness of the respondents to participate in two new projects within

the Survey of Health, Ageing and Retirement in Europe (SHARE). Both projects had

the goal to reduce the burden for respondents and to increase the data quality by linking

the survey data with additional, more objective data. Both linkages required the

interviewers to collect respondents’ written consent during the interview.

The starting point of this dissertation is the question of what influences respondents’

decisions to consent to link their survey answers with administrative data. Three different

areas are considered: characteristics of the respondents, the interviewers, and the

interaction between respondents and interviewers. The results suggest that although

respondent and household characteristics are important, a large part of the variation

is explained by the interviewers. However, the information available about interviewers

in SHARE is limited to a few demographic characteristics. Therefore, it is difficult to

identify key interviewer characteristics that influence the consent process.

To close this research gap, a detailed interviewer survey was developed and implemented

in SHARE. This survey covers four different dimensions of interviewer characteristics:

interviewers’ attitudes, their own behavior, experiences in surveys and special measurements,

and their expectations regarding their success. These dimensions are applied to

several aspects of the survey process, such as unit or item nonresponse as well as the

specific projects of the corresponding SHARE questionnaire.

The information collected in the interviewer survey is then used to analyze interviewer

effects on respondents’ willingness to consent to the collection of blood samples. Those

samples are analyzed in a laboratory and the results linked with the survey data. Interviewers’

experience and their expectations are of special interest, because as these are

two characteristics that can be influenced during interviewer training and selection. The

results in this dissertation show that the interviewers have a considerable effect on respondents’

consent to the collection of biomarkers. Moreover, the information collected

in the interviewer survey can explain most of the variance on the interviewer level.

A motivation for linking survey data with more objective data is the assumption that

survey data suffer from recall error. In the last step, the overlap of information collected

in the survey and provided in the administrative records is used to analyze recall error in

the year of retirement. The comparison of the two datasets shows that most of respondents

remember the year they retired correctly. Nevertheless, a considerable proportion

of respondents make recall errors. Characteristics can be identified which increase the

likelihood of a misreport, However, the error seems to be unsystematic, meaning that

no pattern of reporting the event of retirement too late or too early is found.

IT infrastructures can be quantitatively described by attributes, like performance or energy efficiency. Ever-changing user demands and economic attempts require varying short-term and long-term decisions regarding the alignment of an IT infrastructure and particularly its attributes to this dynamic surrounding. Potentially conflicting attribute goals and the central role of IT infrastructures presuppose decision making based upon reasoning, the process of forming inferences from facts or premises. The focus on specific IT infrastructure parts or a fixed (small) attribute set disqualify existing reasoning approaches for this intent, as they neither cover the (complex) interplay of all IT infrastructure components simultaneously, nor do they address inter- and intra-attribute correlations sufficiently.

This thesis presents a process model for the integrated reasoning about quantitative IT infrastructure attributes. The process model’s main idea is to formalize the compilation of an individual reasoning function, a mathematical mapping of parametric influencing factors and modifications on an attribute vector. Compilation bases upon model integration to benefit from the multitude of existing specialized, elaborated, and well-established attribute models. The achieved reasoning function consumes an individual tuple of IT infrastructure components, attributes, and external influencing factors to expose a broad applicability. The process model formalizes a reasoning intent in three phases. First, reasoning goals and parameters are collected in a reasoning suite, and formalized in a reasoning function skeleton. Second, the skeleton is iteratively refined, guided by the reasoning suite. Third, the achieved reasoning function is employed for What-if analyses, optimization, or descriptive statistics to conduct the concrete reasoning. The process model provides five template classes that collectively formalize all phases in order to foster reproducibility and to reduce error-proneness.

Process model validation is threefold. A controlled experiment reasons about a Raspberry Pi cluster’s performance and energy efficiency to illustrate feasibility. Besides, a requirements analysis on a world-class supercomputer and on the European-wide execution of hydro meteorology simulations as well as a related work examination disclose the process model’s level of innovation. Potential future work employs prepared automation capabilities, integrates human factors, and uses reasoning results for the automatic generation of modification recommendations.

Network quality of service (QoS) and its management are concerned with providing, guaranteeing and reporting properties of data flows within computer networks. For the past two decades, virtualization has been becoming a very popular tool in data centres, yet, without network QoS management capabilities.

With virtualization, the management focus shifts from physical components and topologies, towards virtual infrastructures (VI) and their purposes. VIs are designed and managed as independent isolated entities. Without network QoS management capabilities, VIs cannot offer the same services and service levels as physical infrastructures can, leaving VIs at a disadvantage with respect to applicability and efficiency.

This thesis closes this gap and develops a management architecture, enabling network QoS management in virtulization environments.

First, requirements are dervied, based on real world scenarios, yielding a validation reference for the proposed architecture. After that, a life cycle for VIs and a taxonomy for network links and virtual components are introduced, to arrange the network QoS management task with the general management of virtualization environments and enabling the creation of technology specific adaptors for integrating the technologies and sub-services used in virtualization environments.

The core aspect, shaping the proposed management architecture, is a management loop and its corresponding strategy for identifying and ordering sub-tasks.

Finally, a prototypical implementation showcases that the presented management approach is suited for network QoS management and enforcement in virtualization environments.

The architecture fulfils its purpose, fulfilling all identified requirements. Ultimately, network QoS management is one amongst many aspects to management in virtualization environments and the herin presented architecture shows interfaces to other management areas, where integration is left as future work.

Music has been the subject of formal approaches for a long time, ranging from Pythagoras’ elementary research on tonal systems to J. S. Bach’s elaborate formal composition techniques. Especially in the 20th century, much music was composed based on formal techniques: Algorithmic approaches for composing music were developed by composers like A. Schoenberg as well as in the scientific area. So far, a variety of mathematical techniques have been employed for composing music, e.g. probability models, artificial neural networks or constraint-based reasoning. In the recent time, interactive music systems have become popular: existing songs can be replayed with musical video games and original music can be interactively composed with easy-to-use applications running e.g. on mobile devices. However, applications which algorithmically generate music in real-time based on user interaction are mostly experimental and limited in either interactivity or musicality. There are many enjoyable applications but there are also many opportunities for improvements and novel approaches.

The goal of this work is to provide a general and systematic approach for specifying and implementing interactive music systems. We introduce an algebraic framework for interactively composing music in real-time with a reasoning-technique called ‘soft constraints’: this technique allows modeling and solving a large range of problems and is suited particularly well for problems with soft and concurrent optimization goals. Our framework is based on well-known theories for music and soft constraints and allows specifying interactive music systems by declaratively defining ‘how the music should sound’ with respect to both user interaction and musical rules. Based on this core framework, we introduce an approach for interactively generating music similar to existing melodic material. With this approach, musical rules can be defined by playing notes (instead of writing code) in order to make interactively generated melodies comply with a certain musical style. We introduce an implementation of the algebraic framework in .NET and present several concrete applications: ‘The Planets’ is an application controlled by a table-based tangible interface where music can be interactively composed by arranging planet constellations. ‘Fluxus’ is an application geared towards musicians which allows training melodic material that can be used to define musical styles for applications geared towards non-musicians. Based on musical styles trained by the Fluxus sequencer, we introduce a general approach for transforming spatial movements to music and present two concrete applications: the first one is controlled by a touch display, the second one by a motion tracking system. At last, we investigate how interactive music systems can be used in the area of pervasive advertising in general and how our approach can be used to realize ‘interactive advertising jingles’.

This thesis contains results on singularity of nearcritical percolation scaling limits, on a rigidity estimate and on spontaneous rotational symmetry breaking.

First it is shown that - on the triangular lattice - the laws of scaling limits of nearcritical percolation exploration paths with different parameters are singular with respect to each other. This generalises a result of Nolin and Werner, using a similar technique. As a corollary, the singularity can even be detected from an infinitesimal initial segment. Moreover, nearcritical scaling limits of exploration paths are mutually singular under scaling maps.

Second full scaling limits of planar nearcritical percolation are investigated in the Quad-Crossing-Topology introduced by Schramm and Smirnov. It is shown that two nearcritical scaling limits with different parameters are singular with respect to each other. This result holds for percolation models on rather general lattices, including bond percolation on the square lattice and site percolation on the triangular lattice.

Third a rigidity estimate for 1-forms with non-vanishing exterior derivative is proven. It generalises a theorem on geometric rigidity of Friesecke, James and Müller.

Finally this estimate is used to prove a kind of spontaneous breaking of rotational symmetry for some models of crystals, which allow almost all kinds of defects, including unbounded defects as well as edge, screw and mixed dislocations, i.e. defects with Burgers vectors.

Due to the increasing power of data acquisition and data storage technologies, a large amount of data sets with complex structure are collected in the era of data explosion. Instead of simple representations by low-dimensional numerical features, such data sources range from high-dimensional feature spaces to graph data describing relationships among objects. Many techniques exist in the literature for mining simple numerical data but only a few approaches touch the increasing challenge of mining complex data, such as high-dimensional vectors of non-numerical data type, time series data, graphs, and multi-instance data where each object is represented by a finite set of feature vectors. Besides, there are many important data mining tasks for high-dimensional data, such as clustering, outlier detection, dimensionality reduction, similarity search, classification, prediction and result interpretation. Many algorithms have been proposed to solve these tasks separately, although in some cases they are closely related. Detecting and exploiting the relationships among them is another important challenge. This thesis aims to solve these challenges in order to gain new knowledge from complex high-dimensional data.

We propose several new algorithms combining different data mining tasks to acquire novel knowledge from complex high-dimensional data: ROCAT (Relevant Overlapping Subspace Clusters on Categorical Data) automatically detects the most relevant overlapping subspace clusters on categorical data. It integrates clustering, feature selection and pattern mining without any input parameters in an information theoretic way. The next algorithm MSS (Multiple Subspace Selection) finds multiple low-dimensional subspaces for moderately high-dimensional data, each exhibiting an interesting cluster structure. For better interpretation of the results, MSS visualizes the clusters in multiple low-dimensional subspaces in a hierarchical way. SCMiner (Summarization-Compression Miner) focuses on bipartite graph data, which integrates co-clustering, graph summarization, link prediction, and the discovery of the hidden structure of a bipartite graph data on the basis of data compression. Finally, we propose a novel similarity measure for multi-instance data. The Probabilistic Integral Metric (PIM) is based on a probabilistic generative model requiring few assumptions. Experiments demonstrate the effectiveness and efficiency of PIM for similarity search (multi-instance data indexing with M-tree), explorative data analysis and data mining (multi-instance classification).

To sum up, we propose algorithms combining different data mining tasks for complex data with various data types and data structures to discover the novel knowledge hidden behind the complex data.

This thesis presents several non-parametric and parametric models for estimating dynamic dependence between financial time series and evaluates their ability to precisely estimate risk

measures. Furthermore, the different dependence models are used to analyze the integration

of emerging markets into the world economy. In order to analyze numerous dependence structures and to discover possible asymmetries, two distinct model classes are investigated: the

multivariate GARCH and Copula models. On the theoretical side a new dynamic dependence

structure for multivariate Archimedean Copulas is introduced which lifts the prevailing restriction to two dimensions and extends the multivariate dynamic Archimedean Copulas to more than two dimensions. On this basis a new mixture copula is presented using the newly invented multivariate dynamic dependence structure for the Archimedean Copulas and mixing it with multivariate elliptical copulas. Simultaneously a new process for modeling the time-varying weights of the mixture copula is introduced: this specification makes it possible to estimate various dependence structures within a single model.

The empirical analysis of different portfolios shows that all equity portfolios and the bond portfolios of the emerging markets exhibit negative asymmetries, i.e. increasing dependence during market downturns. However, the portfolio consisting of the developed market bonds does

not show any negative asymmetries. Overall, the analysis of the risk measures reveals that parametric models display portfolio risk more precisely than non-parametric models. However, no

single parametric model dominates all other models for all portfolios and risk measures. The

investigation of dependence between equity and bond portfolios of developed countries, proprietary, and secondary emerging markets reveals that secondary emerging markets are less

integrated into the world economy than proprietary. Thus, secondary emerging markets are

more suitable to diversify a portfolio consisting of developed equity or bond indices than proprietary

Data intensive applications like biology, medicine, and neuroscience require effective and efficient data mining technologies. Advanced data acquisition methods produce a constantly increasing volume and complexity. As a consequence, the need of new data mining technologies to deal with complex data has emerged during the last decades. In this thesis, we focus on the data mining task of clustering in which objects are separated in different groups (clusters) such that objects inside a cluster are more similar than objects in different clusters. Particularly, we consider density-based clustering algorithms and their applications in biomedicine.

The core idea of the density-based clustering algorithm DBSCAN is that each object within a cluster must have a certain number of other objects inside its neighborhood. Compared with other clustering algorithms, DBSCAN has many attractive benefits, e.g., it can detect clusters with arbitrary shape and is robust to outliers, etc. Thus, DBSCAN has attracted a lot of research interest during the last decades with many extensions and applications. In the first part of this thesis, we aim at developing new algorithms based on the DBSCAN paradigm to deal with the new challenges of complex data, particularly expensive distance measures and incomplete availability of the distance matrix.

Like many other clustering algorithms, DBSCAN suffers from poor performance when facing expensive distance measures for complex data. To tackle this problem, we propose a new algorithm based on the DBSCAN paradigm, called Anytime Density-based Clustering (A-DBSCAN), that works in an anytime scheme: in contrast to the original batch scheme of DBSCAN, the algorithm A-DBSCAN first produces a quick approximation of the clustering result and then continuously refines the result during the further run. Experts can interrupt the algorithm, examine the results, and choose between (1) stopping the algorithm at any time whenever they are satisfied with the result to save runtime and (2) continuing the algorithm to achieve better results. Such kind of anytime scheme has been proven in the literature as a very useful technique when dealing with time consuming problems. We also introduced an extended version of A-DBSCAN called A-DBSCAN-XS which is more efficient and effective than A-DBSCAN when dealing with expensive distance measures.

Since DBSCAN relies on the cardinality of the neighborhood of objects, it requires the full distance matrix to perform. For complex data, these distances are usually expensive, time consuming or even impossible to acquire due to high cost, high time complexity, noisy and missing data, etc. Motivated by these potential difficulties of acquiring the distances among objects, we propose another approach for DBSCAN, called Active Density-based Clustering (Act-DBSCAN). Given a budget limitation B, Act-DBSCAN is only allowed to use up to B pairwise distances ideally to produce the same result as if it has the entire distance matrix at hand. The general idea of Act-DBSCAN is that it actively selects the most promising pairs of objects to calculate the distances between them and tries to approximate as much as possible the desired clustering result with each distance calculation. This scheme provides an efficient way to reduce the total cost needed to perform the clustering. Thus it limits the potential weakness of DBSCAN when dealing with the distance sparseness problem of complex data.

As a fundamental data clustering algorithm, density-based clustering has many applications in diverse fields. In the second part of this thesis, we focus on an application of density-based clustering in neuroscience: the segmentation of the white matter fiber tracts in human brain acquired from Diffusion Tensor Imaging (DTI). We propose a model to evaluate the similarity between two fibers as a combination of structural similarity and connectivity-related similarity of fiber tracts. Various distance measu