Reuse in the development process of TOSCANA systems

TOSCANA is a graphical tool that supports the human-centered interactive processes of conceptual knowledge processing. The generality of the approach makes TOSCANA a universal tool applicable to a variety of domains. Only the so-called conceptual scales have to be designed for new applications. The presentation shows how the use of abstract scales allows the reuse of formerly defined conceptual scales. Furthermore it describes how thesauri and conceptual taxonomies can be integrated in the generation of conceptual scales.

Numerical aspects in the data model of conceptual information systems

While most data analysis and decision support tools use numerical aspects of the data, Conceptual Information Systems focus on their conceptual structure. This paper discusses how both approaches can be combined.

Acquiring expert knowledge for the design of conceptual information systems

Conceptual Information Systems unfold the conceptual structure of data stored in relational databases. In the design phase of the system, conceptual hierarchies have to be created which describe different aspects of the data. In this paper, we describe two principal ways of designing such conceptual hierarchies, data driven design and theory driven design and discuss advantages and drawbacks. The central part of the paper shows how Attribute Exploration, a knowledge acquisition tool developped by B. Ganter can be applied for narrowing the gap between both approaches.

Dual retrieval in conceptual information systems

Conceptual Information Systems provide a multi-dimensional conceptually structured view on data stored in relational databases. On restricting the expressiveness of the retrieval language, they allow the visualization of sets of realted queries in conceptual hierarchies, hence supporting the search of something one does not have a precise description, but only a vague idea of. Information Retrieval is considered as the process of finding specific objects (documents etc.) out of a large set of objects which fit to some description. In some data analysis and knowledge discovery applications, the dual task is of interest: The analyst needs to determine, for a subset of objects, a description for this subset. In this paper we discuss how Conceptual Information Systems can be extended to support also the second task.

Conceptual information systems discussed through an IT-security tool

Conceptual Information Systems are based on a formalization of the concept of "concept" as it is discussed in traditional philosophical logic. This formalization supports a human-centered approach to the development of Information Systems. We discuss this approach by means of an implemented Conceptual Information System for supporting IT security management in companies and organizations.

Reverse pivoting in conceptual information systems

In database marketing, the behavior of customers is analyzed by studying the transactions they have performed. In order to get a global picture of the behavior of a customer, his single transactions have to be composed together. In On-Line Analytical Processing, this operation is known as reverse pivoting. With the ongoing data analysis process, reverse pivoting has to be repeated several times, usually requiring an implementation in SQL. In this paper, we present a construction for conceptual scales for reverse pivoting in Conceptual Information Systems, and also discuss the visualization. The construction allows the reuse of previously created queries without reprogramming and offers a visualization of the results by line diagrams.

Evolving Distributed Algorithms with Genetic Programming

Distributed systems are one of the most vital components of the economy. The most prominent example is probably the internet, a constituent element of our knowledge society. During the recent years, the number of novel network types has steadily increased. Amongst others, sensor networks, distributed systems composed of tiny computational devices with scarce resources, have emerged. The further development and heterogeneous connection of such systems imposes new requirements on the software development process. Mobile and wireless networks, for instance, have to organize themselves autonomously and must be able to react to changes in the environment and to failing nodes alike. Researching new approaches for the design of distributed algorithms may lead to methods with which these requirements can be met efficiently. In this thesis, one such method is developed, tested, and discussed in respect of its practical utility. Our new design approach for distributed algorithms is based on Genetic Programming, a member of the family of evolutionary algorithms. Evolutionary algorithms are metaheuristic optimization methods which copy principles from natural evolution. They use a population of solution candidates which they try to refine step by step in order to attain optimal values for predefined objective functions. The synthesis of an algorithm with our approach starts with an analysis step in which the wanted global behavior of the distributed system is specified. From this specification, objective functions are derived which steer a Genetic Programming process where the solution candidates are distributed programs. The objective functions rate how close these programs approximate the goal behavior in multiple randomized network simulations. The evolutionary process step by step selects the most promising solution candidates and modifies and combines them with mutation and crossover operators. This way, a description of the global behavior of a distributed system is translated automatically to programs which, if executed locally on the nodes of the system, exhibit this behavior. In our work, we test six different ways for representing distributed programs, comprising adaptations and extensions of well-known Genetic Programming methods (SGP, eSGP, and LGP), one bio-inspired approach (Fraglets), and two new program representations called Rule-based Genetic Programming (RBGP, eRBGP) designed by us. We breed programs in these representations for three well-known example problems in distributed systems: election algorithms, the distributed mutual exclusion at a critical section, and the distributed computation of the greatest common divisor of a set of numbers. Synthesizing distributed programs the evolutionary way does not necessarily lead to the envisaged results. In a detailed analysis, we discuss the problematic features which make this form of Genetic Programming particularly hard. The two Rule-based Genetic Programming approaches have been developed especially in order to mitigate these difficulties. In our experiments, at least one of them (eRBGP) turned out to be a very efficient approach and in most cases, was superior to the other representations.

CD-Systems of Restarting Automata Governed by Explicit Enable and Disable Conditions

We introduce a new mode of operation for CD-systems of restarting automata by providing explicit enable and disable conditions in the form of regular constraints. We show that, for each CD-system M of restarting automata and each mode m of operation considered by Messerschmidt and Otto, there exists a CD-system M' of restarting automata of the same type as M that, working in the new mode ed, accepts the language that M accepts in mode m. Further, we prove that in mode ed, a locally deterministic CD-system of restarting automata of type RR(W)(W) can be simulated by a locally deterministic CD-system of restarting automata of the more restricted type R(W)(W). This is the first time that a non-monotone type of R-automaton without auxiliary symbols is shown to be as expressive as the corresponding type of RR-automaton.

On CD-systems of stateless deterministic R-automata with window size one

We study cooperating distributed systems (CD-systems) of restarting automata that are very restricted: they are deterministic, they cannot rewrite, but only delete symbols, they restart immediately after performing a delete operation, they are stateless, and they have a read/write window of size 1 only, that is, these are stateless deterministic R(1)-automata. We study the expressive power of these systems by relating the class of languages that they accept by mode =1 computations to other well-studied language classes, showing in particular that this class only contains semi-linear languages, and that it includes all rational trace languages. In addition, we investigate the closure and non-closure properties of this class of languages and some of its algorithmic properties.

CD-Systems of Stateless Deterministic R(1)-Automata Governed by an External Pushdown Store

We study cooperating distributed systems (CD-systems) of stateless deterministic restarting automata with window size 1 that are governed by an external pushdown store. In this way we obtain an automata-theoretical characterization for the class of context-free trace languages.

On Globally Deterministic CD-Systems of Stateless R-Automata with Window Size One

It is known that cooperating distributed systems (CD-systems) of stateless deterministic restarting automata with window size 1 accept a class of semi-linear languages that properly includes all rational trace languages. Although the component automata of such a CD-system are all deterministic, in general the CD-system itself is not, as in each of its computations, the initial component and the successor components are still chosen nondeterministically. Here we study CD-systems of stateless deterministic restarting automata with window size 1 that are themselves completely deterministic. In fact, we consider two such types of CD-systems, the strictly deterministic systems and the globally deterministic systems.

Kontextbasiertes System zur Vermeidung von Kollisionen zwischen Autos und Fußgängern

Laut dem Statistischen Bundesamts ist die Zahl der im Straßenverkehr getöteten Personen zwar rückläufig, jedoch wurden in 2010 in Deutschland noch immer 3648 Personen bei Unfällen im Straßenverkehr getötet, 476 davon waren Fußgänger. In den letzten Dekaden lag der Schwerpunkt der Forschungsarbeiten zur Reduzierung der Verkehrstoten besonders im Bereich des Insassenschutzes. Erst in den letzten Jahren rückte die Thematik des Fußgängerschutzes mehr in den Fokus des öffentlichen Interesses und der Automobilhersteller. Forschungsarbeiten beschäftigen sich mit unterschiedlichen Ansätzen die Folgen einer Kollision zwischen einem Auto und einem Fußgänger zu reduzieren. Hierzu zählen z.B. weiche Aufprallzonen im Frontbereich eines Autos, aufstellende Motorhaube oder auch Fußgängerairbags im Bereich der Frontscheibe. Da passive Ansätze aber nur die Folgen eines Aufpralls am Fahrzeug, nicht aber die Folgen eines Sekundäraufpralls auf dem Boden verringern können, werden parallel Ansätze zur aktiven Kollisionsvermeidung untersucht. Die bisher verfolgten, ebenso wertvollen Ansätze, zeigen jedoch jeweils Schwachpunkte in Ihrer Lösung. So ist der Einsatz der bisherigen bordautonomen Ansätze auf Grund der Anforderungen der verschiedenen Systeme, wie der Notwendigkeit einer direkten, ungestörten Sichtverbindung zwischen Auto und Fußgänger, leider nur eingeschränkt möglich. Kooperative Systeme, die ein zusätzliches, vom Fußgänger mitzuführendes Sende-Empfänger Gerät zur Ermittlung der Fußgängerposition benötigen sind hingegen mit zusätzlichem Aufwand für den Fußgänger verbunden. Auch fehlen den bisher verfolgten Ansätzen Informationen über den Fußgänger, wodurch es schwierig ist, wenn nicht gar manchmal unmöglich, eine Situation korrekt bewerten zu können. Auch sehen diese Systeme keine Warnung des Fußgängers vor. In dieser Arbeit wird ein Verfahren zum Fußgängerschutz betrachtet, welches per Funk ausgetauschte Informationen zur Risikobewertung eines Szenarios nutzt. Hierbei werden neben den vom Auto bekannten Informationen und Parameter, die vom Smartphone des Fußgängers zur Verfügung gestellten Kontextinformationen verwendet. Es werden zum einen die Parameter, Bedingungen und Anforderungen analysiert und die Architektur des Systems betrachtet. Ferner wird das Ergbnis einer Untersuchung zur generellen Umsetzbarkeit mit bereits heute in Smartphone verfügbaren Funktechnolgien vorgestellt. Final werden die bereits vielversprechenden Ergebnisse eines ersten Experiments zur Nutzbarkeit von Sensorinformationen des Smartphones im Bereich der Kollisionsvermeidung vorgestellt und diskutiert.

Capturing Emergent Semantics from Social Annotation Systems

The ongoing growth of the World Wide Web, catalyzed by the increasing possibility of ubiquitous access via a variety of devices, continues to strengthen its role as our prevalent information and commmunication medium. However, although tools like search engines facilitate retrieval, the task of finally making sense of Web content is still often left to human interpretation. The vision of supporting both humans and machines in such knowledge-based activities led to the development of different systems which allow to structure Web resources by metadata annotations. Interestingly, two major approaches which gained a considerable amount of attention are addressing the problem from nearly opposite directions: On the one hand, the idea of the Semantic Web suggests to formalize the knowledge within a particular domain by means of the "top-down" approach of defining ontologies. On the other hand, Social Annotation Systems as part of the so-called Web 2.0 movement implement a "bottom-up" style of categorization using arbitrary keywords. Experience as well as research in the characteristics of both systems has shown that their strengths and weaknesses seem to be inverse: While Social Annotation suffers from problems like, e. g., ambiguity or lack or precision, ontologies were especially designed to eliminate those. On the contrary, the latter suffer from a knowledge acquisition bottleneck, which is successfully overcome by the large user populations of Social Annotation Systems. Instead of being regarded as competing paradigms, the obvious potential synergies from a combination of both motivated approaches to "bridge the gap" between them. These were fostered by the evidence of emergent semantics, i. e., the self-organized evolution of implicit conceptual structures, within Social Annotation data. While several techniques to exploit the emergent patterns were proposed, a systematic analysis - especially regarding paradigms from the field of ontology learning - is still largely missing. This also includes a deeper understanding of the circumstances which affect the evolution processes. This work aims to address this gap by providing an in-depth study of methods and influencing factors to capture emergent semantics from Social Annotation Systems. We focus hereby on the acquisition of lexical semantics from the underlying networks of keywords, users and resources. Structured along different ontology learning tasks, we use a methodology of semantic grounding to characterize and evaluate the semantic relations captured by different methods. In all cases, our studies are based on datasets from several Social Annotation Systems. Specifically, we first analyze semantic relatedness among keywords, and identify measures which detect different notions of relatedness. These constitute the input of concept learning algorithms, which focus then on the discovery of synonymous and ambiguous keywords. Hereby, we assess the usefulness of various clustering techniques. As a prerequisite to induce hierarchical relationships, our next step is to study measures which quantify the level of generality of a particular keyword. We find that comparatively simple measures can approximate the generality information encoded in reference taxonomies. These insights are used to inform the final task, namely the creation of concept hierarchies. For this purpose, generality-based algorithms exhibit advantages compared to clustering approaches. In order to complement the identification of suitable methods to capture semantic structures, we analyze as a next step several factors which influence their emergence. Empirical evidence is provided that the amount of available data plays a crucial role for determining keyword meanings. From a different perspective, we examine pragmatic aspects by considering different annotation patterns among users. Based on a broad distinction between "categorizers" and "describers", we find that the latter produce more accurate results. This suggests a causal link between pragmatic and semantic aspects of keyword annotation. As a special kind of usage pattern, we then have a look at system abuse and spam. While observing a mixed picture, we suggest that an individual decision should be taken instead of disregarding spammers as a matter of principle. Finally, we discuss a set of applications which operationalize the results of our studies for enhancing both Social Annotation and semantic systems. These comprise on the one hand tools which foster the emergence of semantics, and on the one hand applications which exploit the socially induced relations to improve, e. g., searching, browsing, or user profiling facilities. In summary, the contributions of this work highlight viable methods and crucial aspects for designing enhanced knowledge-based services of a Social Semantic Web.