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Various best practices and principles guide an ontology engineer when modeling Linked Data. The choice of appropriate vocabularies is one essential aspect in the guidelines, as it leads to better interpretation, querying, and consumption of the data by Linked Data applications and users.
In this paper, we present the various types of support features for an ontology engineer to model a Linked Data dataset, discuss existing tools and services with respect to these support features, and propose LOVER: a novel approach to support the ontology engineer in modeling a Linked Data dataset. We demonstrate that none of the existing tools and services incorporate all types of supporting features and illustrate the concept of LOVER, which supports the engineer by recommending appropriate classes and properties from existing and actively used vocabularies. Hereby, the recommendations are made on the basis of an iterative multimodal search. LOVER uses different, orthogonal information sources for finding terms, e.g. based on a best string match or schema information on other datasets published in the Linked Open Data cloud. We describe LOVER's recommendation mechanism in general and illustrate it alongrna real-life example from the social sciences domain.
In this paper we describe a series of projects on location based and personalised information systems. We start wit a basic research project and we show how we came with the help of two other more application oriented project to a product. This is developed by a consortium of enterprises and it already is in use in the city of Koblenz.
This thesis describes the implementation of a Path-planning algorithm for multi-axle vehicles using machine learning algorithms. For that purpose, a general overview over Genetic Algorithms is given and alternative machine learning algorithms are briefly explained. The software developed for this purpose is based on the EZSystem Simulation Software developed by the AG Echtzeitysteme at the University Koblenz-Landau and a path correction algorithm developed by Christian Schwarz, which is also detailed in this paper. This also includes a description of the vehicle used in these simulations. Genetic Algorithms as a solution for path-planning in complex scenarios are then evaluated based on the results of the developed simulation software and compared to alternative, non-machine learning solutions, which are also shortly presented.
Knowledge compilation is a common technique for propositional logic knowledge bases. A given knowledge base is transformed into a normal form, for which queries can be answered efficiently. This precompilation step is expensive, but it only has to be performed once. We apply this technique to concepts defined in the Description Logic ALC. We introduce a normal form called linkless normal form for ALC concepts and discuss an efficient satisability test for concepts given in this normal form. Furthermore, we will show how to efficiently calculate uniform interpolants of precompiled concepts w.r.t. a given signature.
Knowledge compilation is a common technique for propositional logic knowledge bases. The idea is to transform a given knowledge base into a special normal form ([MR03],[DH05]), for which queries can be answered efficiently. This precompilation step is very expensive but it only has to be performed once. We propose to apply this technique to knowledge bases defined in Description Logics. For this, we introduce a normal form, called linkless concept descriptions, for ALC concepts. Further we present an algorithm, based on path dissolution, which can be used to transform a given concept description into an equivalent linkless concept description. Finally we discuss a linear satisfiability test as well as a subsumption test for linkless concept descriptions.
Iterative Signing of RDF(S) Graphs, Named Graphs, and OWL Graphs: Formalization and Application
(2013)
When publishing graph data on the web such as vocabulariesrnusing RDF(S) or OWL, one has only limited means to verify the authenticity and integrity of the graph data. Today's approaches require a high signature overhead and do not allow for an iterative signing of graph data. This paper presents a formally defined framework for signing arbitrary graph data provided in RDF(S), Named Graphs, or OWL. Our framework supports signing graph data at different levels of granularity: minimum self-contained graphs (MSG), sets of MSGs, and entire graphs. It supports for an iterative signing of graph data, e. g., when different parties provide different parts of a common graph, and allows for signing multiple graphs. Both can be done with a constant, low overhead for the signature graph, even when iteratively signing graph data.
The objective of this contribution is to conceptually analyze the potentials of entrepreneurial design thinking as being a rather new method for entrepreneurship education. Based on a literature review of different design thinking concepts we carve out a generic design thinking model upon we conceptually build a new model that considers entrepreneurial thinking as a valuable characteristic.
The results of our work show that the characteristics of entrepreneurial design thinking can enhance entrepreneurship education by supporting respective action fields of entrepreneurial learning. In addition we reveal that entrepreneurial design thinking offers beneficial guidelines for the design of entrepreneurship education programs.
With the Multimedia Metadata Ontology (M3O), we have developed a sophisticated model for representing among others the annotation, decomposition, and provenance of multimedia metadata. The goal of the M3O is to integrate the existing metadata standards and metadata formats rather than replacing them. To this end, the M3O provides a scaffold needed to represent multimedia metadata. Being an abstract model for multimedia metadata, it is not straightforward how to use and specialize the M3O for concrete application requirements and existing metadata formats and metadata standards. In this paper, we present a step-by-step alignment method describing how to integrate and leverage existing multimedia metadata standards and metadata formats in the M3O in order to use them in a concrete application. We demonstrate our approach by integrating three existing metadata models: the Core Ontology on Multimedia (COMM), which is a formalization of the multimedia metadata standard MPEG-7, the Ontology for Media Resource of the W3C, and the widely known industry standard EXIF for image metadata
The Multimedia Metadata Ontology (M3O) provides a generic modeling framework for representing multimedia metadata. It has been designed based on an analysis of existing metadata standards and metadata formats. The M3O abstracts from the existing metadata standards and formats and provides generic modeling solutions for annotations, decompositions, and provenance of metadata. Being a generic modeling framework, the M3O aims at integrating the existing metadata standards and metadata formats rather than replacing them. This is in particular useful as today's multimedia applications often need to combine and use more than one existing metadata standard or metadata format at the same time. However, applying and specializing the abstract and powerful M3O modeling framework in concrete application domains and integrating it with existing metadata formats and metadata standards is not always straightforward. Thus, we have developed a step-by-step alignment method that describes how to integrate existing multimedia metadata standards and metadata formats with the M3O in order to use them in a concrete application. We demonstrate our alignment method by integrating seven different existing metadata standards and metadata formats with the M3O and describe the experiences made during the integration process.
We propose a new approach for mobile visualization and interaction of temporal information by integrating support for time with today's most prevalent visualization of spatial information, the map. Our approach allows for an easy and precise selection of the time that is of interest and provides immediate feedback to the users when interacting with it. It has been developed in an evolutionary process gaining formative feedback from end users.
This paper consists of the observation of existing first aid applications for smartphones and comparing them to a first aid application developed by the University of Koblenz called "Defi Now!". The main focus lies on examining "Defi Now!" in respect to its usability based on the dialogue principles referring to the seven software ergonomic principles due to the ISO 9241-110 standard. These are known as suitability for learning, controllability, error tolerance, self-descriptiveness, conformity with user expectations, suitability for the task, and suitability for individualization.
Therefore a usability study was conducted with 74 participants. A questionnaire was developed, which was to be filled out by the test participants anonymously. The test results were used for an optimization of the app referring its' usability.
The goal of this Bachelor thesis is to implement and evaluate the "Simulating of Collective Misbelief"-model into the NetLogo programming language. Therefore, the model requirements have to be specified and implemented into the NetLogo environment. Further tool-related re-quirements have to be specified to enable the model to work in NetLogo. After implementation several simulations will be conducted to answer the research question stated above.
Identifying reusable legacy code able to implement SOA services is still an open research issue. This master thesis presents an approach to identify legacy code for service implementation based on dynamic analysis and the application of data mining techniques. rnrnAs part of the SOAMIG project, code execution traces were mapped to business processes. Due to the high amount of traces generated by dynamic analyses, the traces must be post-processed in order to provide useful information. rnrnFor this master thesis, two data mining techniques - cluster analysis and link analysis - were applied to the traces. First tests on a Java/Swing legacy system provided good results, compared to an expert- allocation of legacy code.
This paper shows how multiagent systems can be modeled by a combination of UML statecharts and hybrid automata. This allows formal system specification on different levels of abstraction on the one hand, and expressing real-time system behavior with continuous variables on the other hand. It is not only shown how multi-robot systems can be modeled by a combination of hybrid automata and hierarchical state machines, but also how model checking techniques for hybrid automata can be applied. An enhanced synchronization concept is introduced that allows synchronization taking time and avoids state explosion to a certain extent.
In this thesis we exercise a wide variety of libraries, frameworks and other technologies that are available for the Haskell programming language. We show various applications of Haskell in real-world scenarios and contribute implementations and taxonomy entities to the 101companies system. That is, we cover a broad range of the 101companies feature model and define related terms and technologies. The implementations illustrate how different language concepts of Haskell, such as a very strong typing system, polymorphism, higher-order functions and monads, can be effectively used in the development of information systems. In this context we demonstrate both advantages and limitations of different Haskell technologies.
In recent years, traceability has been more and more universally accepted as being a key factor for the success of software development projects. However, the multitude of different, not well-integrated taxonomies, approaches and technologies impedes the application of traceability techniques in practice. This paper presents a comprehensive view on traceability, pertaining to the whole software development process. Based on graph technology, it derives a seamless approach which combines all activities related to traceability information, namely definition, recording, identification, maintenance, retrieval, and utilization in one single conceptual framework. The presented approach is validated in the context of the ReDSeeDS-project aiming at requirements-based software reuse.
The purpose of this master thesis is to enable the Robot Lisa to process complex commands and extract the necessary information in order to perform a complex task as a sequence of smaller tasks. This is intended to be achieved by the improvement of the understanding that Lisa has of her environment by adding semantics to the maps that she builds. The complex command itself will be expected to be already parsed. Therefore the way the input is processed to become a parsed command is out of the scope of this work. Maps that Lisa builds will be improved by the addition of semantic annotations that can include any kind of information that might be useful for the performance of generic tasks. This can include (but not necessarily limited to) hierarchical classifications of locations, objects and surfaces. The processing of the command in addition to some information of the environment shall trigger the performance of a sequence of actions. These actions are expected to be included in Lisa- currently implemented tasks and will rely on the currently existing modules that perform them.
Nevertheless the aim of this work is not only to be able to use currently implemented tasks in a more complex sequence of actions but also make it easier to add new tasks to the complex commands that Lisa can perform.
Expert-driven business process management is an established means for improving efficiency of organizational knowledge work. Implicit procedural knowledge in the organization is made explicit by defining processes. This approach is not applicable to individual knowledge work due to its high complexity and variability. However, without explicitly described processes there is no analysis and efficient communication of best practices of individual knowledge work within the organization. In addition, the activities of the individual knowledge work cannot be synchronized with the activities in the organizational knowledge work.rnrnSolution to this problem is the semantic integration of individual knowledgernwork and organizational knowledge work by means of the patternbased core ontology strukt. The ontology allows for defining and managing the dynamic tasks of individual knowledge work in a formal way and to synchronize them with organizational business processes. Using the strukt ontology, we have implemented a prototype application for knowledge workers and have evaluated it at the use case of an architectural fifirm conducting construction projects.
We introduce linear expressions for unrestricted dags (directed acyclic graphs) and finite deterministic and nondeterministic automata operating on them. Those dag automata are a conservative extension of the Tu,u-automata of Courcelle on unranked, unordered trees and forests. Several examples of dag languages acceptable and not acceptable by dag automata and some closure properties are given.
On-screen interactive presentations have got immense popularity in the domain of attentive interfaces recently. These attentive screens adapt their behavior according to the user's visual attention. This thesis aims to introduce an application that would enable these attentive interfaces to change their behavior not just according to the gaze data but also facial features and expressions. The modern era requires new ways of communications and publications for advertisement. These ads need to be more specific according to people's interests, age, and gender. When advertising, it's important to get a reaction from the user but not every user is interested in providing feedback. In such a context more, advance techniques are required that would collect user's feedback effortlessly. The main problem this thesis intends to resolve is, to apply advanced techniques of gaze and face recognition to collect data about user's reactions towards different ads being played on interactive screens. We aim to create an application that enables attentive screens to detect a person's facial features, expressions, and eye gaze. With eye gaze data we can determine the interests and with facial features, age and gender can be specified. All this information will help in optimizing the advertisements.
In this thesis the feasibility of a GPGPU (general-purpose computing on graphics processing units) approach to natural feature description on mobile phone GPUs is assessed. To this end, the SURF descriptor [4] has been implemented with OpenGL ES 2.0/GLSL ES 1.0 and evaluated across different mobile devices. The implementation is multiple times faster than a comparable CPU variant on the same device. The results proof the feasibility of modern mobile graphics accelerators for GPGPU tasks especially for the detection phase in natural feature tracking used in augmented reality applications. Extensive analysis and benchmarking of this approach in comparison to state of the art methods have been undertaken. Insights into the modifications necessary to adapt and modify the SURF algorithm to the limitations of a mobile GPU are presented. Further, an outlook for a GPGPU-based tracking pipeline on a mobile device is provided.
Six and Gimmler have identified concrete capabilities that enable users to use the Internet in a competent way. Their media competence model can be used for the didactical design of media usage in secondary schools. However, the special challenge of security awareness is not addressed by the model. In this paper, the important dimension of risk and risk assessment will be introduced into the model. This is especially relevant for the risk of the protection of personal data and privacy. This paper will apply the method of IT risk analysis in order to select those dimensions of the Six/Gimmler media competence model that are appropriate to describe privacy aware Internet usage. Privacy risk aware decisions for or against the Internet usage is made visible by the trust model of Mayer et al.. The privacy extension of the competence model will lead to a measurement of the existing privacy awareness in secondary schools, which, in turn, can serve as a didactically well-reasoned design of Informatics modules in secondary schools. This paper will provide the privacy-extended competence model, while empirical measurement and module design is planned for further research activities.
In automated theorem proving, there are some problems that need information on the inequality of certain constants. In most cases this information is provided by adding facts which explicitly state that two constants are unequal. Depending on the number of constants, a huge amount of this facts can clutter the knowledge base and distract the author and readers of the problem from its actual proposition. For most cases it is save to assume that a larger knowledge base reduces the performance of a theorem prover, which is another drawback of explicit inequality facts. Using the unique name assumption in those reasoning tasks renders the introduction of inequality facts obsolete as the unique name assumptions states that two constants are identical iff their interpretation is identical. Implicit handling of non-identical constants makes the problems easier to comprehend and reduces the execution time of reasoning. In this thesis we will show how to integrate the unique name assumption into the E-hyper tableau calculus and that the modified calculus is sound and complete. The calculus will be implemented into the E-KRHyper theorem prover and we will show, by empiric evaluation, that the changed implementation, which is able to use the unique name assumption, is superior to the traditional version of E-KRHyper.
Modeling and publishing Linked Open Data (LOD) involves the choice of which vocabulary to use. This choice is far from trivial and poses a challenge to a Linked Data engineer. It covers the search for appropriate vocabulary terms, making decisions regarding the number of vocabularies to consider in the design process, as well as the way of selecting and combining vocabularies. Until today, there is no study that investigates the different strategies of reusing vocabularies for LOD modeling and publishing. In this paper, we present the results of a survey with 79 participants that examines the most preferred vocabulary reuse strategies of LOD modeling. Participants of our survey are LOD publishers and practitioners. Their task was to assess different vocabulary reuse strategies and explain their ranking decision. We found significant differences between the modeling strategies that range from reusing popular vocabularies, minimizing the number of vocabularies, and staying within one domain vocabulary. A very interesting insight is that the popularity in the meaning of how frequent a vocabulary is used in a data source is more important than how often individual classes and properties arernused in the LOD cloud. Overall, the results of this survey help in understanding the strategies how data engineers reuse vocabularies, and theyrnmay also be used to develop future vocabulary engineering tools.
Schema information about resources in the Linked Open Data (LOD) cloud can be provided in a twofold way: it can be explicitly defined by attaching RDF types to the resources. Or it is provided implicitly via the definition of the resources´ properties.
In this paper, we analyze the correlation between the two sources of schema information. To this end, we have extracted schema information regarding the types and properties defined in two datasets of different size. One dataset is a LOD crawl from TimBL- FOAF profile (11 Mio. triple) and the second is an extract from the Billion Triples Challenge 2011 dataset (500 Mio. triple). We have conducted an in depth analysis and have computed various entropy measures as well as the mutual information encoded in this two manifestations of schema information.
Our analysis provides insights into the information encoded in the different schema characteristics. It shows that a schema based on either types or properties alone will capture only about 75% of the information contained in the data. From these observations, we derive conclusions about the design of future schemas for LOD.
Regarding the rapidly growing amount of data produced every year and the increasing acceptance of Enterprise 2.0 enterprises have to care about the management of their data more and more. Content created and stored in an uncoordinated manner can lead to data-silos (Williams & Hardy 2011, p.57), which result in long search times, inaccessible data and in consequence monetary losses. The "expanding digital universe" forces enterprises to develop new archiving solutions and records management policies (Gantz et al. 2007, p.13). Enterprise Content Management (ECM) is the research field that deals with these challenges. It is placed in the scientific context of Enterprise Information Management. This thesis aims to find out to what extent current Enterprise Content Management Systems (ECMS) support these new requirements, especially concerning the archiving of Enterprise 2.0 data. For this purpose, three scenarios were created to evaluate two different kinds of ECMS (one Open Source - and one proprietary system) chosen on the basis of a short marketrnresearch. The application of the scenarios reveals that the system vendors actually face the industry- concerns: both tools provide functionality for the archiving of data arising from online collaboration and also business records management capabilities but the integration of those topics is not, or is only inconsistently solved. At this point new questions - such as, "Which datarngenerated in an Enterprise 2.0 is worth being a record?" - arise and should be examined in future research.
Social networks are ubiquitous structures that we generate and enrich every-day while connecting with people through social media platforms, emails, and any other type of interaction. While these structures are intangible to us, they carry important information. For instance, the political leaning of our friends can be a proxy to identify our own political preferences. Similarly, the credit score of our friends can be decisive in the approval or rejection of our own loans. This explanatory power is being leveraged in public policy, business decision-making and scientific research because it helps machine learning techniques to make accurate predictions. However, these generalizations often benefit the majority of people who shape the general structure of the network, and put in disadvantage under-represented groups by limiting their resources and opportunities. Therefore it is crucial to first understand how social networks form to then verify to what extent their mechanisms of edge formation contribute to reinforce social inequalities in machine learning algorithms.
To this end, in the first part of this thesis, I propose HopRank and Janus two methods to characterize the mechanisms of edge formation in real-world undirected social networks. HopRank is a model of information foraging on networks. Its key component is a biased random walker based on transition probabilities between k-hop neighborhoods. Janus is a Bayesian framework that allows to identify and rank plausible hypotheses of edge formation in cases where nodes possess additional information. In the second part of this thesis, I investigate the implications of these mechanisms - that explain edge formation in social networks - on machine learning. Specifically, I study the influence of homophily, preferential attachment, edge density, fraction of inorities, and the directionality of links on both performance and bias of collective classification, and on the visibility of minorities in top-k ranks. My findings demonstrate a strong correlation between network structure and machine learning outcomes. This suggests that systematic discrimination against certain people can be: (i) anticipated by the type of network, and (ii) mitigated by connecting strategically in the network.
E-KRHyper is a versatile theorem prover and model generator for firstorder logic that natively supports equality. Inequality of constants, however, has to be given by explicitly adding facts. As the amount of these facts grows quadratically in the number of these distinct constants, the knowledge base is blown up. This makes it harder for a human reader to focus on the actual problem, and impairs the reasoning process. We extend E-Hyper- underlying E-KRhyper tableau calculus to avoid this blow-up by implementing a native handling for inequality of constants. This is done by introducing the unique name assumption for a subset of the constants (the so called distinct object identifiers). The obtained calculus is shown to be sound and complete and is implemented into the E-KRHyper system. Synthetic benchmarks, situated in the theory of arrays, are used to back up the benefits of the new calculus.
Semantic desktop environments aim at improving the effectiveness and efficiency of users carrying out daily tasks within their personal information management infrastructure (PIM). They support the user by transferring and exploiting the explicit semantics of data items across different PIM applications. Whether such an approach does indeed reach its aim of facilitating users" life and—if so—to which extent, however, remains an open question that we address in this paper with the first summative evaluation of a semantic desktop approach. We approach the research question exploiting our own semantic desktop infrastructure, X-COSIM. As data corpus, we have used over 100 emails and 50 documents extracted from the organizers of a conference-like event at our university. The evaluation has been carried out with 18 subjects. We have developed a test environment to evaluate COSIMail and COSIFile, two semantic PIM applications based on X-COSIM. As result, we have found a significant improvement for typical PIM tasks compared to a standard desktop environment.
We present the conceptual and technological foundations of a distributed natural language interface employing a graph-based parsing approach. The parsing model developed in this thesis generates a semantic representation of a natural language query in a 3-staged, transition-based process using probabilistic patterns. The semantic representation of a natural language query is modeled in terms of a graph, which represents entities as nodes connected by edges representing relations between entities. The presented system architecture provides the concept of a natural language interface that is both independent in terms of the included vocabularies for parsing the syntax and semantics of the input query, as well as the knowledge sources that are consulted for retrieving search results. This functionality is achieved by modularizing the system's components, addressing external data sources by flexible modules which can be modified at runtime. We evaluate the system's performance by testing the accuracy of the syntactic parser, the precision of the retrieved search results as well as the speed of the prototype.
The estimation of various social objects is necessary in different fields of social life, science, education, etc. This estimation is usually used for forecasting, for evaluating of different properties and for other goals in complex man-machine systems. At present this estimation is possible by means of computer and mathematical simulation methods which is connected with significant difficulties, such as: - time-distributed process of receiving information about the object; - determination of a corresponding mathematical device and structure identification of the mathematical model; - approximation of the mathematical model to real data, generalization and parametric identification of the mathematical model; - identification of the structure of the links of the real social object. The solution of these problems is impossible without a special intellectual information system which combines different processes and allows predicting the behaviour of such an object. However, most existing information systems lead to the solution of only one special problem. From this point of view the development of a more general technology of designing such systems is very important. The technology of intellectual information system development for estimation and forecasting the professional ability of respondents in the sphere of education can be a concrete example of such a technology. Job orientation is necessary and topical in present economic conditions. It helps tornsolve the problem of expediency of investments to a certain sphere of education. Scientifically validated combined diagnostic methods of job orientation are necessary to carry out professional selection in higher education establishments. The requirements of a modern society are growing, with the earlier developed techniques being unable to correspond to them sufficiently. All these techniques lack an opportunity to account all necessary professional and personal characteristics. Therefore, it is necessary to use a system of various tests. Thus, the development of new methods of job orientation for entrants is necessary. The information model of the process of job orientation is necessary for this purpose. Therefore, it would be desirable to have an information system capable of giving recommendations concerning the choice of a trade on the basis of complex personal characteristics of entrants.
Procedural content generation, the generation of video game content using pseudo-random algorithms, is a field of increasing business and academic interest due to its suitability for reducing development time and cost as well as the possibility of creating interesting, unique game spaces. Although many contemporary games feature procedurally generated content, the author perceived a lack of games using this approach to create realistic outer-space game environments, and the feasibility of employing procedural content generations in such a game was examined. Using current scientific models, a real-time astronomical simulation was developed in Python which generates star and planets object in a fictional galaxy procedurally to serve as the game space of a simple 2D space exploration game where the player has to search for intelligent life.
Magnetic resonance (MR) tomography is an imaging method, that is used to expose the structure and function of tissues and organs in the human body for medical diagnosis. Diffusion weighted (DW) imaging is a specific MR imaging technique, which enables us to gain insight into the connectivity of white matter pathways noninvasively and in vivo. It allows for making predictions about the structure and integrity of those connections. In clinical routine this modality finds application in the planning phase of neurosurgical operations, such as in tumor resections. This is especially helpful if the lesion is deeply seated in a functionally important area, where the risk of damage is given. This work reviews the concepts of MR imaging and DW imaging. Generally, at the current resolution of diffusion weighted data, single white matter axons cannot be resolved. The captured signal rather describes whole fiber bundles. Beside this, it often appears that different complex fiber configurations occur in a single voxel, such as crossings, splittings and fannings. For this reason, the main goal is to assist tractography algorithms who are often confound in such complex regions. Tractography is a method which uses local information to reconstruct global connectivities, i.e. fiber tracts. In the course of this thesis, existing reconstruction methods such as diffusion tensor imaging (DTI) and q-ball imaging (QBI) are evaluated on synthetic generated data and real human brain data, whereas the amount of valuable information provided by the individual reconstruction mehods and their corresponding limitations are investigated. The output of QBI is the orientation distribution function (ODF), where the local maxima coincides with the underlying fiber architecture. We determine those local maxima. Furthermore, we propose a new voxel-based classification scheme conducted on diffusion tensor metrics. The main contribution of this work is the combination of voxel-based classification, local maxima from the ODF and global information from a voxel- neighborhood, which leads to the development of a global classifier. This classifier validates the detected ODF maxima and enhances them with neighborhood information. Hence, specific asymmetric fibrous architectures can be determined. The outcome of the global classifier are potential tracking directions. Subsequently, a fiber tractography algorithm is designed that integrates along the potential tracking directions and is able to reproduce splitting fiber tracts.
The World Wide Web (WWW) has become a very important communication channel. Its usage has steadily grown within the past. Interest by website owners in identifying user behaviour has been around since Tim Berners-Lee developed the first web browser in 1990. But as the influence of the online channel today eclipses all other media the interest in monitoring website usage and user activities has intensified as well. Gathering and analysing data about the usage of websites can help to understand customer behaviour, improve services and potentially increase profit.
It is further essential for ensuring effective website design and management, efficient mass customization and effective marketing. Web Analytics (WA) is the area addressing these considerations. However, changing technologies and evolving Web Analytic methods and processes present a challenge to organisations starting with Web Analytic programmes. Because of lacking resources in different areas and other types of websites especially small and medium-sized enterprises (SME) as well as non-profit organisations struggle to operate WA in an effective manner.
This research project aims to identify the existing gap between theory, tool possibilities and business needs for undertaking Web Analytic programmes. Therefore the topic was looked at from three different ways: the academic literature, Web Analytic tools and an interpretative case study. The researcher utilized an action research approach to investigate Web Analytics presenting an holistic overview and to identify the gaps that exists. The outcome of this research project is an overall framework, which provides guidance for SMEs who operate information websites on how to proceed in a Web Analytic programme.
Designing Core Ontologies
(2011)
One of the key factors that hinders integration of distributed, heterogeneous information systems is the lack of a formal basis for modeling the complex, structured knowledge that is to be exchanged. To alleviate this situation, we present an approach based on core ontologies. Core ontologies are characterized by a high degree of axiomatization and formal precision. This is achieved by basing on a foundational ontology. In addition, core ontologies should follow a pattern-oriented design approach. By this, they are modular and extensible. Core ontologies allow for reusing the structured knowledge they define as well as integrating existing domainrnknowledge. The structured knowledge of the core ontologies is clearly separated from the domain-specific knowledge. Such core ontologies allow for both formally conceptualize their particular fields and to be flexibly combined to cover the needsrnof concrete, complex application domains. Over the last years, we have developed three independent core ontologies for events and objects, multimedia annotations, and personal information management. In this paper, we present the simultaneousrnuse and integration of our core ontologies at the example of a complex, distributed socio-technical system of emergency response. We describe our design approach for core ontologies and discuss the lessons learned in designing them. Finally, we elaborate on the beauty aspects of our core ontologies.
Software projects typically rely on several, external libraries. The interface provided by such a library is called API (application programming interface). APIs often evolve over time, thereby implying the need to adapt applications that use them. There are also reasons which may call for the replacement of one library by another one, what also results in a need to adapt the applications where the library is replaced. The process of adapting applications to use a different API is called API migration. Doing API migration manually is a cumbersome task. Automated API migration is an active research field. A related field of research is API analysis which can also provide data for developing API migration tools.
The following thesis investigates techniques and technologies for API analysis and API migration frameworks. To this end, design patterns are leveraged. These patterns are based on experience with API analysis and migration within the Software Languages Team.
Information systems research has started to use crowdsourcing platforms such as Amazon Mechanical Turks (MTurk) for scientific research, recently. In particular, MTurk provides a scalable, cheap work-force that can also be used as a pool of potential respondents for online survey research. In light of the increasing use of crowdsourcing platforms for survey research, the authors aim to contribute to the understanding of its appropriate usage. Therefore, they assess if samples drawn from MTurk deviate from those drawn via conventional online surveys (COS) in terms of answers in relation to relevant e-commerce variables and test the data in a nomological network for assessing differences in effects.
The authors compare responses from 138 MTurk workers with those of 150 German shoppers recruited via COS. The findings indicate, inter alia, that MTurk workers tend to exhibit more positive word-of mouth, perceived risk, customer orientation and commitment to the focal company. The authors discuss the study- results, point to limitations, and provide avenues for further research.
Large amounts of qualitative data make the utilization of computer-assisted methods for their analysis inevitable. In this thesis Text Mining as an interdisciplinary approach, as well as the methods established in the empirical social sciences for analyzing written utterances are introduced. On this basis a process of extracting concept networks from texts is outlined and the possibilities of utilitzing natural language processing methods within are highlighted. The core of this process is text processing, to whose execution software solutions supporting manual as well as automated work are necessary. The requirements to be met by these solutions, against the background of the initiating project GLODERS, which is devoted to investigating extortion racket systems as part of the global fiσnancial system, are presented, and their fulσlment by the two most preeminent candidates reviewed. The gap between theory and pratical application is closed by a prototypical application of the method to a data set of the research project utilizing the two given software solutions.
Concept for a Knowledge Base on ICT for Governance and Policy Modelling regarding eGovPoliNet
(2013)
Abstract The EU project eGovPoliNet is engaged in research and development in the field of information and communication technologies (ICT) for governance and policy modelling. Numerous communities pursue similar goals in this field of IT-based, strategic decision making and simulation of social problem areas. Though, the existing research approaches and results so far are quite fragmented. The aim of eGovPoliNet is to overcome the fragmentation across disciplines and to establish an international, open dialogue by fostering the cooperation between research and practice. This dialogue will advance the discussion and development of various problem areas with the help of researchers from different disciplines, who share knowledge, expertise and best practice supporting policy analysis, modelling and governance. To support this dialogue, eGovPoliNet will provide a knowledge base, which's conceptual development is the subject of this thesis. The knowledge base is to be filled with content from the area of ICT for strategic decision making and social simulation, such as publications, ICT solutions and project descriptions. This content needs to be structured, organised and managed in a way, so that it generates added value and the knowledge base is used as source of accumulated knowledge, which consolidates the previously fragmented research and development results in a central location.
The aim of this thesis is the development of a concept for a knowledge base, which provides the structure and the necessary functionalities to gather and process knowledge concerning ICT solutions for governance and policy modelling. This knowledge needs to be made available to users and thereby motivate them to contribute to the development and maintenance of the knowledge base.
An estimation of the number of multiplication and addition operations for training artififfcial neural networks by means of consecutive and parallel algorithms on a computer cluster is carried out. The evaluation of the efficiency of these algorithms is developed. The multilayer perceptron, the Volterra network and the cascade-correlation network are used as structures of artififfcial neural networks. Different methods of non-linear programming such as gradient and non-gradient methods are used for the calculation of the weight coefficients.
In this paper, we compare two approaches for exploring large,rnhierarchical data spaces of social media data on mobile devicesrnusing facets. While the first approach arranges thernfacets in a 3x3 grid, the second approach makes use of arnscrollable list of facets for exploring the data. We have conductedrna between-group experiment of the two approachesrnwith 24 subjects (20 male, 4 female) executing the same set ofrntasks of typical mobile users" information needs. The resultsrnshow that the grid-based approach requires significantly morernclicks, but subjects need less time for completing the tasks.rnFurthermore, it shows that the additional clicks do not hamperrnthe subjects" satisfaction. Thus, the results suggest thatrnthe grid-based approach is a better choice for faceted searchrnon touchscreen mobile devices. To the best of our knowledge,rnsuch a summative evaluation of different approaches for facetedrnsearch on mobile devices has not been done so far.
Augmented Reality bedeutet eine reale Umgebung mit, meistens grafischen, virtuellen Inhalten zu erweitern. Oft sind dabei die virtuellen Inhalte der Szene jedoch nur ein Overlay und interagieren nicht mit den realen Bestandteilen der Szene. Daraus ergibt sich ein Authentizitätsproblem für Augmented Reatliy Anwendungen. Diese Arbeit betrachtet Augmented Reality in einer speziellen Umgebung, mit deren Hilfe eine authentischere Darstellung möglich ist. Ziel dieserArbeitwar die Erstellung eines Systems, das Zeichnungen durch Techniken der Augmented Reality mit virtuellen Inhalten erweitert. Durch das Anlegen einer Repräsentation soll es der Anwendung dabei möglich sein die virtuellen Szeneelementemit der Zeichnung interagieren zu lassen. Dazu wurden verschiedene Methoden aus den Bereichen des Pose Tracking und der Sketch Recognition disktutiert und für die Implementierung in einem prototypischen System ausgewählt. Als Zielhardware fungiert ein Android Smartphone. Kontext der Zeichnungen ist eine Dungeon Karte, wie sie in Rollenspielen vorkommt. Die virtuellen Inhalte nehmen dabei die Form von Bewohnern des Dungeons an, welche von einer Agentensimulation verwaltet werden. Die Agentensimulation ist Gegenstand einer eigenen Diplomarbeit [18]. Für das Pose Tracking wurde ARToolkitPlus eingesetzt, ein optisches Tracking System, das auf Basis von Markern arbeitet. Die Sketch Recognition ist dafür zuständig die Inhalte der Zeichnung zu erkennen und zu interpretieren. Dafür wurde ein eigener Ansatz implementiert der Techniken aus verschiedenen Sketch Recognition Systemen kombiniert. Die Evaluation konzentriert sich auf die technischen Aspekte des Systems, die für eine authentische Erweiterung der Zeichnung mit virtuellen Inhalten wichtig sind.
We present a non-linear camera pose estimator, which is able to handle a combined input of point and line feature correspondences. For three or more correspondences, the estimator works on any arbitrary number and choice of the feature type, which provides an estimation of the pose on a preferably small and flexible amount of 2D-3D correspondences. We also give an analysis of different minimization techniques, parametrizations of the pose data, and of error measurements between 2D and 3D data. These will be tested for the usage of point features, lines and the combination case. The result shows the most stable and fast working non-linear parameter set for pose estimation in model-based tracking.
The novel mobile application csxPOI (short for: collaborative, semantic, and context-aware points-of-interest) enables its users to collaboratively create, share, and modify semantic points of interest (POI). Semantic POIs describe geographic places with explicit semantic properties of a collaboratively created ontology. As the ontology includes multiple subclassiffcations and instantiations and as it links to DBpedia, the richness of annotation goes far beyond mere textual annotations such as tags. With the intuitive interface of csxPOI, users can easily create, delete, and modify their POIs and those shared by others. Thereby, the users adapt the structure of the ontology underlying the semantic annotations of the POIs. Data mining techniques are employed to cluster and thus improve the quality of the collaboratively created POIs. The semantic POIs and collaborative POI ontology are published as Linked Open Data.
Only little information is available about the diffusion of cloud computing in German higher educational institutions. A better understanding of the state of the art in this field would support the modernization of the higher educational institutions in Germany and allow the development of more adequate cloud products and more appropriate business models for this niche. For this purpose, a literature research on Cloud Computing and IT-diffusion will be run and an empirical investigation with an online questionnaire addressed to higher educational institutions in Germany will be performed to illustrate the state of the art of Cloud Computing in German higher educational institutions as well as the threats and opportunities perceived by employees of higher educational institutions data centers connected to the usage of the cloud.
In addition to that, different experts from universities and businesses will be interviewed to complete the knowledge and information collected through the online questionnaire and during the research phase. The expected results will serve to create a recommendation for higher educational institutions in Germany about either they should migration to the cloud or not and introduce a list of guiding questions of critical issues to consider before using cloud-computing technologies.
Cloud Computing is a topic that has gained momentum in the last years. Current studies show that an increasing number of companies is evaluating the promised advantages and considering making use of cloud services. In this paper we investigate the phenomenon of cloud computing and its importance for the operation of ERP systems. We argue that the phenomenon of cloud computing could lead to a decisive change in the way business software is deployed in companies. Our reference framework contains three levels (IaaS, PaaS, SaaS) and clarifies the meaning of public, private and hybrid clouds. The three levels of cloud computing and their impact on ERP systems operation are discussed. From the literature we identify areas for future research and propose a research agenda.
Autonomous systems such as robots already are part of our daily life. In contrast to these machines, humans an react appropriately to their counterparts. People can hear and interpret human speech, and interpret facial expressions of other people.
This thesis presents a system for automatic facial expression recognition with emotion mapping. The system is image-based and employs feature-based feature extraction. This thesis analyzes the common steps of an emotion recognition system and presents state-of-the-art methods. The approach presented is based on 2D features. These features are detected in the face. No neutral face is needed as reference. The system extracts two types of facial parameters. The first type consists of distances between the feature points. The second type comprises angles between lines connecting the feature points. Both types of parameters are implemented and tested. The parameters which provide the best results for expression recognition are used to compare the system with state-of-the-art approaches. A multiclass Support Vector Machine classifies the parameters.
The results are codes of Action Units of the Facial Action Coding System. These codes are mapped to a facial emotion. This thesis addresses the six basic emotions (happy, surprised, sad, fearful, angry, and disgusted) plus the neutral facial expression. The system presented is implemented in C++ and is provided with an interface to the Robot Operating System (ROS).
Ontologies play an important role in knowledge representation for sharing information and collaboratively developing knowledge bases. They are changed, adapted and reused in different applications and domains resulting in multiple versions of an ontology. The comparison of different versions and the analysis of changes at a higher level of abstraction may be insightful to understand the changes that were applied to an ontology. While there is existing work on detecting (syntactical) differences and changes in ontologies, there is still a need in analyzing ontology changes at a higher level of abstraction like ontology evolution or refactoring pattern. In our approach we start from a classification of model refactoring patterns found in software engineering for identifying such refactoring patterns in OWL ontologies using DL reasoning to recognize these patterns.
The aim of this paper is to identify and understand the risks and issues companies are experiencing from the business use of social media and to develop a framework for describing and categorising those social media risks. The goal is to contribute to the evolving theorisation of social media risk and to provide a foundation for the further development of social media risk management strategies and processes. The study findings identify thirty risk types organised into five categories (technical, human, content, compliance and reputational). A risk-chain is used to illustrate the complex interrelated, multi-stakeholder nature of these risks and directions for future work are identified.