Contributions in Merlin

Advanced 3D microstructural analysis in natural sciences and engineering depends ever more on modern data acquisition and imaging technologies such as micro-computed or synchrotron tomography and interactive visualization. The acquired volume data sets are not only of high-resolution but in particular exhibit complex spatial structures at different levels of scale (e.g. variable spatial expression of multiscale periodic growth structures in tooth enamel). Such highly structured volume data sets represent a tough challenge to be analyzed and explored by means of interactive visualization due to the amount of raw volume data to be processed and filtered for the desired features. As an approach to address this bottleneck by multiscale feature preserving data reduction, we propose higher-order tensor approximations (TAs). We demonstrate the power of TA to represent, and highlight the structural features in volume data. We visually and quantitatively show that TA yields high data reduction and that TA preserves volume features at multiple scales.

Traditionally Semantic Web applications either included a web crawler or relied on external services to gain access to the Web of Data. Recent efforts have enabled applications to query the entire Semantic Web for up-to-date results. Such approaches are based on either centralized indexing of semantically annotated metadata or link traversal and URI dereferencing as in the case of Linked Open Data. By making limiting assumptions about the information space, they violate the openness principle of the Web - a key factor for its ongoing success. In this article we propose a technique called Avalanche, designed to allow a data surfer to query the Semantic Web transparently without making any prior assumptions about the distribution of the data - thus adhering to the openness criteria. Specifically, Avalanche can perform "live" (SPARQL) queries over the Web of Data. First, it gets on-line statistical information about the data distribution, as well as bandwidth availability. Then, it plans and executes the query in a distributed manner trying to quickly provide first answers. The main contribution of this paper is the presentation of this open and distributed SPARQL querying approach. Furthermore, we propose to extend the query planning algorithm with qualitative statistical information. We empirically evaluate Avalanche using a realistic dataset, show its strengths but also point out the challenges that still exist.

Whilst SAML-based federations are most often used by academic and semi-commercial institutions that focus only on attribute-based authentication, we foresee a growing interest for service providers providing charged services. Since more and more academic and semi-commercial federation participants offer Voice-over-IP (VoIP) services, this type of service provides an entry point into identity federation based payment. Therefore, this paper describes an approach on how to harmonize the SAML-based federation technology with the needs of a payment infrastructure for enabling charging of VoIP services within a federation. However, since the different aspects of our approach (SAML Payment, SIP Discovery and Tariff Function) are not bound to VoIP applications, each one of them could be used separately or combined for several service types.

In the beginning of the Semantic Web, ontologies were usually constructed once by a single knowledge engineer and then used as a static conceptualization of some domain. Nowadays, knowledge bases are increasingly dynamically evolving and incorporate new knowledge from different heterogeneous domains -- some of which is even contributed by casual users (i.e., non-knowledge engineers) or even software agents. Given that ontologies are based on the rather strict formalism of Description Logics and their inference procedures, conflicts are likely to occur during ontology evolution. Conflicts, in turn, may cause an ontological knowledge base to become inconsistent and making reasoning impossible. Hence, every formalism for ontology evolution should provide a mechanism for resolving conflicts. In this paper we provide a general framework for conflict-free ontology evolution without changing the knowledge representation. Using a variant of Lehmann's Default Logics and Probabilistic Description Logics, we can invalidate unwanted implicit inferences without removing explicitly stated axioms. We show that this method outperforms classical ontology repair w.r.t. the amount of information lost while allowing for automatic conflict-solving when evolving ontologies.

One of the problems of Knowledge Discovery in Databases (KDD) is the lack of user support for solving KDD problems. Current Data Mining (DM) systems enable the user to manually design workflows but this becomes difficult when there are too many operators to choose from or the workflow's size is too large. Therefore we propose to use auto-experimentation based on ontological planning to provide the users with automatic generated workflows as well as rankings for workflows based on several criteria (execution time, accuracy, etc.). Moreover auto-experimentation will help to validate the generated workflows and to prune and reduce their number. Furthermore we will use mixed-initiative planning to allow the users to set parameters and criteria to limit the planning search space as well as to guide the planner towards better workflows.

While many uses of mined software engineering data are automatic in nature, some techniques and studies either require, or can be improved, by manual methods. Unfortunately, manually inspecting, analyzing, and annotating mined data can be difficult and tedious, especially when information from multiple sources must be integrated. Oddly, while there are numerous tools and frameworks for automatically mining and analyzing data, there is a dearth of tools which facilitate manual methods. To fill this void, we have developed LINKSTER, a tool which integrates data from bug databases, source code repositories, and mailing list archives to allow manual inspection and annotation. LINKSTER has already been used successfully by an OSS project lead to obtain data for one empirical study.

Keeping requirements specification up-to-date during the evolution of a software system is an expensive task. Consequently, specifications are usually not updated and rapidly become obsolete and unreliable. The goal of our research is to preserve the alignment between requirements and the implementation by supporting the maintenance of the specification. In this proposal, we explore the idea of using tests to automatically generate hints about the evolution of requirements. We discuss the main research questions that need to be addressed, and propose ideas to approach them.

Knowledge representation is a long-standing research area of computer science that aims at representing human knowledge in a form that computers can interpret. Most knowledge representation approaches, however, have suffered from poor user interfaces. It turns out to be difficult for users to learn and use the logic-based languages in which the knowledge has to be encoded. A new approach to design more intuitive but still reliable user interfaces for knowledge representation systems is the use of controlled natural language (CNL). CNLs are subsets of natural languages that are restricted in a way that allows their automatic translation into formal logic. A number of CNLs have been developed but the resulting tools are mostly just prototypes so far. Furthermore, nobody has yet been able to provide strong evidence that CNLs are indeed easier to understand than other logic-based languages. The goal of this thesis is to give the research area of CNLs for knowledge representation a shift in perspective: from the present explorative and proof-of-concept-based approaches to a more engineering-focussed point of view. For this reason, I introduce theoretical and practical building blocks for the design and application of controlled English for the purpose of knowledge representation. I first show how CNLs can be defined in an adequate and simple way by the introduction of a novel grammar notation and I describe efficient algorithms to process such grammars. I then demonstrate how these theoretical concepts can be implemented and how CNLs can be embedded in knowledge representation tools so that they provide intuitive and powerful user interfaces that are accessible even to untrained users. Finally, I discuss how the understandability of CNLs can be evaluated. I argue that the understandability of CNLs cannot be assessed reliably with existing approaches, and for this reason I introduce a novel testing framework. Experiments based on this framework show that CNLs are not only easier to understand than comparable languages but also need less time to be learned and are preferred by users.

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