Passive safety systems like airbags have significantly improved road safety. These occupant safety systems help in reducing the severity of injuries, and save lives in the event of a road accident. The airbag systems must be configured correctly to minimize the impact of collision and protect the occupants. To configure the airbag, test crashes are performed and data is recorded. This data is simulated to find out appropriate parameters for the airbag deployment. The airbag simulation results are stored into databases. Airbag application tools are used to handle the data stored in databases. The airbag simulation results must be extracted efficiently and required computations needs to be performed. This data is then stored to reports. RSDBnext is an airbag application tool, it stands for Result Database next generation. This tool is used for extraction of data from the database. The RSDBnext tool should be adapted to generate Standard Customer Reports. These reports are to be generated based on customer requirements. The existing methodology to generate Standard Customer Reports used Excel macros, which took a lot of time to generate the reports. This method was complex and unstable. Hence, a new methodology was proposed without using macros. In the proposed method, an XML file and XSLT StyleSheet were used to generate the report in Excel using C# with Visual Studio. This approach reduces report generation time, and overcomes the drawbacks of the previous approach. From the results, this methodology to generate reports is faster, easier, and more reliable.

A wealth of multidimensional OLAP models has been suggested in the past, tackling various problems of modeling multidimensional data. However, all of these models focus on navigational and query operators for grouping, selection and aggregation. We argue that planning functionality is, next to reporting and analysis, an important part of OLAP in many businesses and as such should be represented as part of a multidimensional model. Navigational operators are not enough for planning, instead new factual data is created or existing data is changed. To our knowledge we are the first to suggest a multidimensional model with support for planning. Because the main data entities of a typical multidimensional model are used both by planning and reporting, we concentrate on the extension of an existing model, where we add a set of novel operators that support an extensive set of typical planning functions.

The increasing amount and variety of data available in the web leads to new possibilities in end-user focused data analysis. While the classic data base technologies for data integration and analysis (ETL and BI) are too complex for the needs of end users, newer technologies like web mashups are not optimal for data analysis. To make productive use of the data available on the web, end users need easy ways to find, join and visualize it. We propose a domain specific language (DSL) for querying a repository of heterogeneous web data. In contrast to query languages such as SQL, this DSL describes the visualization of the queried data in addition to the selection, filtering and aggregation of the data. The resulting data mashup can be made interactive by leaving parts of the query variable. We also describe an abstraction layer above this DSL that uses a recommendation-driven natural language interface to reduce the difficulty of creating queries in this DSL.

An increasing number of application fields represent dynamic and open discourses characterized by high mutability, variety, and pluralism in data. Data in dynamic and open discourses typically exhibits an irregular schema. Such data cannot be directly represented in the traditional relational data model. Mapping strategies allow representation but increase development and maintenance costs. Likewise, NoSQL systems offer the required schema flexibility but introduce new costs by not being directly compatible with relational systems that still dominate enterprise information systems. With the Flexible Relational Data Model (FRDM) we propose a third way. It allows the direct representation of data with irregular schemas. It combines tuple-oriented data representation with relation-oriented data processing. So that, FRDM is still relational, in contrast to other flexible data models currently in vogue. It can directly represent relational data and builds on the powerful, well-known, and proven set of relational operations for data retrieval and manipulation. In addition to FRDM, we present the flexible constraint framework FRDM-C. It explicitly allows restricting the flexibility of FRDM when and where needed. All this makes FRDM backward compatible to traditional relational applications and simplifies the interoperability with existing pure relational databases.

Im naturwissenschaftlichen Unterricht werden Daten z.B. in Erkenntnisgewinnungsprozessen eingesetzt und somit ist hier auch die Evaluation von Daten wichtig. Es können Daten aus erster und zweiter Hand unterschieden werden, wobei die Unterscheidung auf Basis des Autoren und der Beteiligung an der Datenerhebung geschieht. Im Physikunterricht werden Schüler*innen mit Daten aus unterschiedlichen Quellen konfrontiert. Es wird angenommen, dass die Evaluation von Daten durch Schüler*innen als Glaubwürdigkeitsbewertung dieser Daten verstanden werden kann. Ergänzend zur Theorie wurde in einer Studie untersucht, welche Kriterien Schüler*innen bei der Evaluation unterschiedlicher Datensätze verwenden, die sich nur durch den Autor unterschieden. Dafür wurden 17 Interviews mit Schüler*innen durchgeführt (13-16 Jahre). Eine qualitative Inhaltsanalyse führte zu einem Codesystem mit vier Codes und diversen Subcodes. Die Codes bezogen sich auf die Themen Eigenschaften des Experiments, Eigenschaften von Autoren, Eigenschaften der Daten und Prüfen/Abgleichen. Unterschiede in der Verwendung der Kriterien für verschiedene Datentypen, wurden in einer zweiten Studie überprüft. Dazu wurden 42 Interviews mit schüler*innen (14-16 Jahre) durchgeführt. Alle Probanden erzeugten selbstständig physikalische Daten und wählten Hypothesen über den Ausgang des Experiments aus. Im Anschluss wurden sie mit einem von drei verschiedenen Datensätzen konfrontiert: ihren eigenen Daten, den Daten eines anderen Schülers oder Daten eines Lehrers. Das Codesystem war die Grundlage einer quantitativen Inhaltsanalyse dieser Interviews. Diese erlaubte es, Unterschiede zwischen den Versuchsgruppen zu finden. Es ergaben sich keine Unterschiede für das Hypothesenwechselverhalten, die Verwendung von Kriterien für die Glaubwürdigkeitsbewertung oder das Rating der Wichtigkeit der Codes zwischen den Versuchsgruppen. Folgerungen für den Unterricht und die Unterscheidung der Datentypen werden erläutert. / In science education data is used in the process of gaining knowledge and it is therefore important to evaluate the data. First- and second-hand-data can be differentiated, whereby the distinction is based on the authorship and the involvement in the data acquisition. In physics education students are regularly confronted with data from different sources. It is assumed that the evaluation of data by students can be understood as the evaluation of the datas credibility. To complement a theoretical model, an interview study was conducted to find out which criteria for the evaluation of the credibility of data are used by students when evaluating different types of data. 17 students (13-16 years) were interviewed. A qualitative content analysis yielded a systems of four different codes and several subcodes. These codes where representations of statements that dealt with properties of the experiment, properties of the author, properties of the data, and testing and comparing. A second study was conducted to test if there are differences in the use of those criteria when students are confronted with different types of data. 42 Interviews with students (14-16 years) were conducted. All subjects acquired a set of first-hand data in an experiment and where asked to choose between 3 hypotheses about the outcome. Afterwards they were confronted with three different sets of data: their own data, another student's set of data, and a teacher's set of data. The system of codes from the previous study was used as the basis for a quantitative content analyses of these interviews. This analysis made it possible to find differences between the experimental groups. No differences were found for the change of the hypothesis, the use of criteria for the evaluation of credibility or the rating of importance of the codes. Implications for education and the differentiation of types of data are discussed.

This thesis is about visualizing a kind of data that is trivial to process by computers but difficult to imagine by humans because nature does not allow for intuition with this type of information: high-dimensional data. Such data often result from representing observations of objects under various aspects or with different properties. In many applications, a typical, laborious task is to find related objects or to group those that are similar to each other. One classic solution for this task is to imagine the data as vectors in a Euclidean space with object variables as dimensions. Utilizing Euclidean distance as a measure of similarity, objects with similar properties and values accumulate to groups, so-called clusters, that are exposed by cluster analysis on the high-dimensional point cloud. Because similar vectors can be thought of as objects that are alike in terms of their attributes, the point cloud\'s structure and individual cluster properties, like their size or compactness, summarize data categories and their relative importance. The contribution of this thesis is a novel analysis approach for visual exploration of high-dimensional point clouds without suffering from structural occlusion. The work is based on implementing two key concepts: The first idea is to discard those geometric properties that cannot be preserved and, thus, lead to the typical artifacts. Topological concepts are used instead to shift away the focus from a point-centered view on the data to a more structure-centered perspective. The advantage is that topology-driven clustering information can be extracted in the data\'s original domain and be preserved without loss in low dimensions. The second idea is to split the analysis into a topology-based global overview and a subsequent geometric local refinement. The occlusion-free overview enables the analyst to identify features and to link them to other visualizations that permit analysis of those properties not captured by the topological abstraction, e.g. cluster shape or value distributions in particular dimensions or subspaces. The advantage of separating structure from data point analysis is that restricting local analysis only to data subsets significantly reduces artifacts and the visual complexity of standard techniques. That is, the additional topological layer enables the analyst to identify structure that was hidden before and to focus on particular features by suppressing irrelevant points during local feature analysis. This thesis addresses the topology-based visual analysis of high-dimensional point clouds for both the time-invariant and the time-varying case. Time-invariant means that the points do not change in their number or positions. That is, the analyst explores the clustering of a fixed and constant set of points. The extension to the time-varying case implies the analysis of a varying clustering, where clusters appear as new, merge or split, or vanish. Especially for high-dimensional data, both tracking---which means to relate features over time---but also visualizing changing structure are difficult problems to solve.

This paper describes the format of monitoring data files that are collected for external measuring sites and at laboratory experiments at the Institute for Building Climatology (IBK). The Monitoring Data Files are containers for storing time series or event driven data collected as input for transient heat and moisture transport simulations. Further applications are the documentation of real world behaviour, laboratory experiments or the collection of validation data sets for simulation results ( whole building / energy consumption / HAM ). The article also discusses the application interface towards measurement data verification tools as well as data storage solutions that can be used to archive measurement data files conveniently and efficiently.

Understanding the development, organization, and function of tissues is a central goal in developmental biology. With modern time-lapse microscopy, it is now possible to image entire tissues during development and thereby localize subcellular proteins. A particularly productive area of research is the study of single layer epithelial tissues, which can be simply described as a 2D manifold. For example, the apical band of cell adhesions in epithelial cell layers actually forms a 2D manifold within the tissue and provides a 2D outline of each cell. The Drosophila melanogaster wing has become an important model system, because its 2D cell organization has the potential to reveal mechanisms that create the final fly wing shape. Other examples include structures that naturally localize at the surface of the tissue, such as the ciliary components of planarians. Data from these time-lapse movies typically consists of mosaics of overlapping 3D stacks. This is necessary because the surface of interest exceeds the field of view of todays microscopes. To quantify cellular tissue dynamics, these mosaics need to be processed in three main steps: (a) Extracting, correcting, and stitching individ- ual stacks into a single, seamless 2D projection per time point, (b) obtaining cell characteristics that occur at individual time points, and (c) determine cell dynamics over time. It is therefore necessary that the applied methods are capable of handling large amounts of data efficiently, while still producing accurate results. This task is made especially difficult by the low signal to noise ratios that are typical in live-cell imaging. In this PhD thesis, I develop algorithms that cover all three processing tasks men- tioned above and apply them in the analysis of polarity and tissue dynamics in large epithelial cell layers, namely the Drosophila wing and the planarian epithelium. First, I introduce an efficient pipeline that preprocesses raw image mosaics. This pipeline accurately extracts the stained surface of interest from each raw image stack and projects it onto a single 2D plane. It then corrects uneven illumination, aligns all mosaic planes, and adjusts brightness and contrast before finally stitching the processed images together. This preprocessing does not only significantly reduce the data quantity, but also simplifies downstream data analyses. Here, I apply this pipeline to datasets of the developing fly wing as well as a planarian epithelium. I additionally address the problem of determining cell polarities in chemically fixed samples of planarians. Here, I introduce a method that automatically estimates cell polarities by computing the orientation of rootlets in motile cilia. With this technique one can for the first time routinely measure and visualize how tissue polarities are established and maintained in entire planarian epithelia. Finally, I analyze cell migration patterns in the entire developing wing tissue in Drosophila. At each time point, cells are segmented using a progressive merging ap- proach with merging criteria that take typical cell shape characteristics into account. The method enforces biologically relevant constraints to improve the quality of the resulting segmentations. For cases where a full cell tracking is desired, I introduce a pipeline using a tracking-by-assignment approach. This allows me to link cells over time while considering critical events such as cell divisions or cell death. This work presents a very accurate large-scale cell tracking pipeline and opens up many avenues for further study including several in-vivo perturbation experiments as well as biophysical modeling. The methods introduced in this thesis are examples for computational pipelines that catalyze biological insights by enabling the quantification of tissue scale phenomena and dynamics. I provide not only detailed descriptions of the methods, but also show how they perform on concrete biological research projects.

Diese Arbeit behandelt die Frage, wie Daten grundsätzlich strukturiert und beschrieben sind. Im Gegensatz zu vorhandenen Auseinandersetzungen mit Daten im Sinne von gespeicherten Beobachtungen oder Sachverhalten, werden Daten hierbei semiotisch als Zeichen aufgefasst. Diese Zeichen werden in Form von digitalen Dokumenten kommuniziert und sind mittels zahlreicher Standards, Formate, Sprachen, Kodierungen, Schemata, Techniken etc. strukturiert und beschrieben. Diese Vielfalt von Mitteln wird erstmals in ihrer Gesamtheit mit Hilfe der phenomenologischen Forschungsmethode analysiert. Ziel ist es dabei, durch eine genaue Erfahrung und Beschreibung von Mitteln zur Strukturierung und Beschreibung von Daten zum allgemeinen Wesen der Datenstrukturierung und -beschreibung vorzudringen. Die Ergebnisse dieser Arbeit bestehen aus drei Teilen. Erstens ergeben sich sechs Prototypen, die die beschriebenen Mittel nach ihrem Hauptanwendungszweck kategorisieren. Zweitens gibt es fünf Paradigmen, die das Verständnis und die Anwendung von Mitteln zur Strukturierung und Beschreibung von Daten grundlegend beeinflussen. Drittens legt diese Arbeit eine Mustersprache der Datenstrukturierung vor. In zwanzig Mustern werden typische Probleme und Lösungen dokumentiert, die bei der Strukturierung und Beschreibung von Daten unabhängig von konkreten Techniken immer wieder auftreten. Die Ergebnisse dieser Arbeit können dazu beitragen, das Verständnis von Daten --- das heisst digitalen Dokumente und ihre Metadaten in allen ihren Formen --- zu verbessern. Spezielle Anwendungsgebiete liegen unter Anderem in den Bereichen Datenarchäologie und Daten-Literacy. / Many methods, technologies, standards, and languages exist to structure and describe data. The aim of this thesis is to find common features in these methods to determine how data is actually structured and described. Existing studies are limited to notions of data as recorded observations and facts, or they require given structures to build on, such as the concept of a record or the concept of a schema. These presumed concepts have been deconstructed in this thesis from a semiotic point of view. This was done by analysing data as signs, communicated in form of digital documents. The study was conducted by a phenomenological research method. Conceptual properties of data structuring and description were first collected and experienced critically. Examples of such properties include encodings, identifiers, formats, schemas, and models. The analysis resulted in six prototypes to categorize data methods by their primary purpose. The study further revealed five basic paradigms that deeply shape how data is structured and described in practice. The third result consists of a pattern language of data structuring. The patterns show problems and solutions which occur over and over again in data, independent from particular technologies. Twenty general patterns were identified and described, each with its benefits, consequences, pitfalls, and relations to other patterns. The results can help to better understand data and its actual forms, both for consumption and creation of data. Particular domains of application include data archaeology and data literacy.

Das Lernen aus strukturierten Eingabe- und Ausgabebeispielen ist die Grundlage für die automatisierte Verarbeitung natürlich auftretender Problemstellungen und eine Herausforderung für das Maschinelle Lernen. Die Einordnung von Objekten in eine Klassentaxonomie, die Eigennamenerkennung und das Parsen natürlicher Sprache sind mögliche Anwendungen. Klassische Verfahren scheitern an der komplexen Natur der Daten, da sie die multiplen Abhängigkeiten und Strukturen nicht erfassen können. Zudem ist die Erhebung von klassifizierten Beispielen in strukturierten Anwendungsgebieten aufwändig und ressourcenintensiv, während unklassifizierte Beispiele günstig und frei verfügbar sind. Diese Arbeit thematisiert halbüberwachte, diskriminative Vorhersagemodelle für strukturierte Daten. Ausgehend von klassischen halbüberwachten Verfahren werden die zugrundeliegenden analytischen Techniken und Algorithmen auf das Lernen mit strukturierten Variablen übertragen. Die untersuchten Verfahren basieren auf unterschiedlichen Prinzipien und Annahmen, wie zum Beispiel der Konsensmaximierung mehrerer Hypothesen im Lernen aus mehreren Sichten, oder der räumlichen Struktur der Daten im transduktiven Lernen. Desweiteren wird in einer Fallstudie zur Email-Batcherkennung die räumliche Struktur der Daten ausgenutzt und eine Lösung präsentiert, die der sequenziellen Natur der Daten gerecht wird. Aus den theoretischen Überlegungen werden halbüberwachte, strukturierte Vorhersagemodelle und effiziente Optmierungsstrategien abgeleitet. Die empirische Evaluierung umfasst Klassifikationsprobleme, Eigennamenerkennung und das Parsen natürlicher Sprache. Es zeigt sich, dass die halbüberwachten Methoden in vielen Anwendungen zu signifikant kleineren Fehlerraten führen als vollständig überwachte Baselineverfahren. / Learning mappings between arbitrary structured input and output variables is a fundamental problem in machine learning. It covers many natural learning tasks and challenges the standard model of learning a mapping from independently drawn instances to a small set of labels. Potential applications include classification with a class taxonomy, named entity recognition, and natural language parsing. In these structured domains, labeled training instances are generally expensive to obtain while unlabeled inputs are readily available and inexpensive. This thesis deals with semi-supervised learning of discriminative models for structured output variables. The analytical techniques and algorithms of classical semi-supervised learning are lifted to the structured setting. Several approaches based on different assumptions of the data are presented. Co-learning, for instance, maximizes the agreement among multiple hypotheses while transductive approaches rely on an implicit cluster assumption. Furthermore, in the framework of this dissertation, a case study on email batch detection in message streams is presented. The involved tasks exhibit an inherent cluster structure and the presented solution exploits the streaming nature of the data. The different approaches are developed into semi-supervised structured prediction models and efficient optimization strategies thereof are presented. The novel algorithms generalize state-of-the-art approaches in structural learning such as structural support vector machines. Empirical results show that the semi-supervised algorithms lead to significantly lower error rates than their fully supervised counterparts in many application areas, including multi-class classification, named entity recognition, and natural language parsing.