Fakultät für Mathematik, Informatik und Statistik - Digitale Hochschulschriften der LMU - Teil 01/02

Despite advances in software and hardware technologies, faults are still inevitable in a highly-dependent, human-engineered and administrated IT environment. Given the critical role of IT services today, it is imperative

that faults, having once occurred, have to be dealt with eciently and eeffectively to avoid or reduce the actual losses. Nevertheless, the complexities of current IT services, e.g., with regard to their scales, heterogeneity and highly dynamic infrastructures, make the recovery operation a challenging task for operators. Such complexities will eventually outgrow the human capability to manage them. Such diculty is augmented by the fact that there are few well-devised methods available to support fault recovery.

To tackle this issue, this thesis aims at providing a computer-aided approach to assist operators with fault recovery planning and, consequently, to increase the eciency of recovery activities.We propose a generic framework

based on the automated planning theory to generate plans for recoveries of IT services. At the heart of the framework is a planning component. Assisted by the other participants in the framework, the planning component aggregates the relevant information and computes recovery steps accordingly. The main idea behind the planning component is to sustain the planning operations with automated planning techniques, which is one of the research fields of articial intelligence. Provided with a general planning model, we

show theoretically that the service fault recovery problem can be indeed solved by automated planning techniques. The relationship between a planning problem and a fault recovery problem is shown by means of reduction between these problems. After an extensive investigation, we choose a planning paradigm that based on Hierarchical Task Networks (HTN) as the guideline for the design of our main planning algorithm called H2MAP.

To sustain the operation of the planner, a set of components revolving around the planning component is provided. These components are responsible for tasks such as translation between dierent knowledge formats, persistent storage of planning knowledge and communication with external systems.

To ensure extendibility in our design, we apply dierent design patterns for the components. We sketch and discuss the technical aspects of implementations of the core components. Finally, as proof of the concept, the framework is instantiated to two distinguishing application scenarios.

Usable and secure authentication is a research field that approaches different challenges related to authentication, including security, from a human-computer interaction perspective. That is, work in this field tries to overcome security, memorability and performance problems that are related to the interaction with an authentication mechanism. More and more services that require authentication, like ticket vending machines or automated teller machines (ATMs), take place in a public setting, in which security threats are more inherent than in other settings. In this work, we approach the problem of usable and secure authentication for public spaces.

The key result of the work reported here is a set of well-founded criteria for the systematic evaluation of authentication mechanisms. These criteria are justified by two different types of investigation, which are on the one hand prototypical examples of authentication mechanisms with improved usability and security, and on the other hand empirical studies of security-related behavior in public spaces. So this work can be structured in three steps:

Firstly, we present five authentication mechanisms that were designed to overcome the main weaknesses of related work which we identified using a newly created categorization of authentication mechanisms for public spaces. The systems were evaluated in detail and showed encouraging results for future use. This and the negative sides and problems that we encountered with these systems helped us to gain diverse insights on the design and evaluation process of such systems in general. It showed that the development process of authentication mechanisms for public spaces needs to be improved to create better results. Along with this, it provided insights on why related work is difficult to compare to each other. Keeping this in mind, first criteria were identified that can fill these holes and improve design and evaluation of authentication mechanisms, with a focus on the public setting.

Furthermore, a series of work was performed to gain insights on factors influencing the quality of authentication mechanisms and to define a catalog of criteria that can be used to support creating such systems. It includes a long-term study of different PIN-entry systems as well as two field studies and field interviews on real world ATM-use. With this, we could refine the previous criteria and define additional criteria, many of them related to human factors. For instance, we showed that social issues, like trust, can highly affect the security of an authentication mechanism.

We used these results to define a catalog of seven criteria. Besides their definition, we provide information on how applying them influences the design, implementation and evaluation of a the development process, and more specifically, how adherence improves authentication in general. A comparison of two authentication mechanisms for public spaces shows that a system that fulfills the criteria outperforms a system with less compliance. We could also show that compliance not only improves the authentication mechanisms themselves, it also allows for detailed comparisons between different systems.

Over the past 10 years, biclustering has become popular not only in the field of biological data analysis but also in other applications with high-dimensional two way datasets. This technique clusters both rows and columns simultaneously, as opposed to clustering only rows or only columns. Biclustering retrieves subgroups of objects that are similar in one subgroup of variables and different in the remaining variables. This dissertation focuses on improving and advancing biclustering methods. Since most existing methods are extremely sensitive to variations in parameters and data, we developed an ensemble method to overcome these limitations. It is possible to retrieve more stable and reliable bicluster in two ways: either by running algorithms with different parameter settings or by running them on sub- or bootstrap samples of the data and combining the results. To this end, we designed a software package containing a collection of bicluster algorithms for different clustering tasks and data scales, developed several new ways of visualizing bicluster solutions, and adapted traditional cluster validation indices (e.g. Jaccard index) for validating the bicluster framework. Finally, we applied biclustering to marketing data. Well-established algorithms were adjusted to slightly different data situations, and a new method specially adapted to ordinal data was developed. In order to test this method on artificial data, we generated correlated original random values. This dissertation introduces two methods for generating such values given a probability vector and a correlation structure.

All the methods outlined in this dissertation are freely available in the R packages biclust and orddata. Numerous examples in this work illustrate how to use the methods and software.

Benchmark experiments nowadays are the method of choice to evaluate learning algorithms in most research fields with applications related to statistical learning. Benchmark experiments are an empirical tool to analyze statistical learning algorithms on one or more data sets: to compare a set of algorithms, to find the best hyperparameters for an algorithm, or to make a sensitivity analysis of an algorithm. In the main part, this dissertation focus on the comparison of candidate algorithms and introduces a comprehensive toolbox for analyzing such benchmark experiments. A systematic approach is introduced -- from exploratory analyses with specialized visualizations static and interactive) via formal investigations and their interpretation as preference relations through to a consensus order of the algorithms, based on one or more performance measures and data sets. The performance of learning algorithms is determined by data set characteristics, this is common knowledge. Not exactly known is the concrete relationship between characteristics and algorithms. A formal framework on top of benchmark experiments is presented for investigation on this relationship. Furthermore, benchmark experiments are commonly treated as fixed-sample experiments, but their nature is sequential. First thoughts on a sequential framework are presented and its advantages are discussed. Finally, this main part of the dissertation is concluded with a discussion on future research topics in the field of benchmark experiments.

The second part of the dissertation is concerned with archetypal analysis. Archetypal analysis has the aim to represent observations in a data set as convex combinations of a few extremal points. This is used as an analysis approach for benchmark experiments -- the identification and interpretation of the extreme performances of candidate algorithms. In turn, benchmark experiments are used to analyze the general framework for archetypal analyses worked out in this second part of the dissertation. Using its generalizability, the weighted and robust archetypal problems are introduced and solved; and in the outlook a generalization towards prototypes is discussed.

The two freely available R packages -- benchmark and archetypes -- make the introduced methods generally

applicable.

Enabling non-experts to publish data on the web is an important

achievement of the social web and one of the primary goals of the social

semantic web. Making the data easily accessible in turn has received only

little attention, which is problematic from the point of view of

incentives: users are likely to be less motivated to participate in the

creation of content if the use of this content is mostly reserved to

experts.

Querying in semantic wikis, for example, is typically realized in terms of

full text search over the textual content and a web query language such as

SPARQL for the annotations. This approach has two shortcomings that limit

the extent to which data can be leveraged by users: combined queries over

content and annotations are not possible, and users either are restricted

to expressing their query intent using simple but vague keyword queries or

have to learn a complex web query language.

The work presented in this dissertation investigates a more suitable form

of querying for semantic wikis that consolidates two seemingly conflicting

characteristics of query languages, ease of use and expressiveness. This

work was carried out in the context of the semantic wiki KiWi, but the

underlying ideas apply more generally to the social semantic and social

web.

We begin by defining a simple modular conceptual model for the KiWi wiki

that enables rich and expressive knowledge representation. A component of

this model are structured tags, an annotation formalism that is simple yet

flexible and expressive, and aims at bridging the gap between atomic tags

and RDF. The viability of the approach is confirmed by a user study, which

finds that structured tags are suitable for quickly annotating evolving

knowledge and are perceived well by the users.

The main contribution of this dissertation is the design and

implementation of KWQL, a query language for semantic wikis. KWQL combines

keyword search and web querying to enable querying that scales with user

experience and information need: basic queries are easy to express; as the

search criteria become more complex, more expertise is needed to formulate

the corresponding query. A novel aspect of KWQL is that it combines both

paradigms in a bottom-up fashion. It treats neither of the two as an

extension to the other, but instead integrates both in one framework. The

language allows for rich combined queries of full text, metadata, document

structure, and informal to formal semantic annotations. KWilt, the KWQL

query engine, provides the full expressive power of first-order queries,

but at the same time can evaluate basic queries at almost the speed of the

underlying search engine. KWQL is accompanied by the visual query language

visKWQL, and an editor that displays both the textual and visual form of

the current query and reflects changes to either representation in the

other. A user study shows that participants quickly learn to construct

KWQL and visKWQL queries, even when given only a short introduction.

KWQL allows users to sift the wealth of structure and annotations in an

information system for relevant data. If relevant data constitutes a

substantial fraction of all data, ranking becomes important. To this end,

we propose PEST, a novel ranking method that propagates relevance among

structurally related or similarly annotated data. Extensive experiments,

including a user study on a real life wiki, show that pest improves the

quality of the ranking over a range of existing ranking approaches.

In this day and age, the mobile phone is becoming one of the most indispensable personal computing device. People no longer use it just for communication (i.e. calling, sending messages) but also for other aspects of their lives as well. Because of this rise in demand for different and innovative applications, mobile companies (i.e. mobile handset manufacturers and mobile network providers) and organizations have realized the power of collaborative software development and have changed their business strategy. Instead of hiring specific organizations to do programming, they are now opening up their APIs and tools to allow ordinary people create their own mobile applications either for personal use or for profit.

However, the problem with this approach is that there are people who might have nice ideas of their own but do not possess the technical expertise in order to create applications implementing these ideas.

The goal of this research is to find ways to simplify the creation of mobile applications for non-technical people by applying model-driven software development particularly domain-specific modeling combined with techniques from the field of human-computer interaction (HCI) particularly iterative, user-centered system design. As proof of concept, we concentrate on the development of applications in the domain of mHealth and use the Android Framework as the target platform for code generation.

The iterative user-centered design and development of the front-end tool which is called the Mobia Modeler, led us to eventually create a tool that features a configurable-component based design and integrated modeless environment to simplify the different development tasks of end-users. The Mobia models feature both constructs specialized for specific domains (e.g. sensor component, special component ), and also those that are applicable to any type of domain (e.g. structure component, basic component ). In order to accommodate different needs of end-users, a clear separation between the front-end tools (i.e. Mobia Modeler ) and the underlying code generator (i.e. Mobia Processor ) is recommended as long as there is a consistent model in between, that serves as a bridge between the different tools.