Informatik
Refine
Document Type
- Doctoral Thesis (18) (remove)
Is part of the Bibliography
- yes (18)
Institute
- Informatik (18)
Este trabajo se enmarca dentro del vasto contexto de Ciudades Inteligentes, y se centra en el área de la conducción inteligente de vehículos, tanto en zonas urbanas como interurbanas, mediante la recogida de datos en tiempo real, medidos con sensores, por parte de los propios conductores, así como de datos capturados mediante simulación.
El objetivo de este trabajo es doble. Por un lado, el estudio y aplicación de las diferentes técnicas y métodos de detección de valores atípicos en bases de datos multivariantes, además de una comparativa entre ellos mediante las pruebas llevadas a cabo con datos de tráfico real. Y por otro lado, establecer una relación entre las situaciones anómalas de tráfico, como puedan ser atascos o accidentes, con los valores atípicos multivariantes encontrados.
La detección de valores atípicos representa una de las tareas más importantes a la hora de realizar cualquier análisis de datos, sea cual sea el dominio o área de estudio, ya que entre sus funciones primordiales se encuentra el descubrir información útil, que resulta de gran valor, y que por lo general queda oculta por la alta dimensión de los datos.
Con el uso de mecanismos de detección de valores atípicos junto con métodos de clasificación supervisada, se va a poder llevar a cabo el reconocimiento de elementos de la infraestructura vial urbana como pueden ser rotondas, pasos de cebra, cruces o semáforos.
Unternehmen stehen aktuell aufgrund der Digitalisierung, des stetigen technologischen Fortschritts und immer kürzer werdenden Produktlebenszyklen vor großen Herausforderungen. Um am Markt bestehen zu können, müssen Geschäftsmodelle öfter und schneller an sich verändernde Marktverhältnisse angepasst werden als dies früher der Fall war. Eine schnelle Anpassungsfähigkeit, auch Agilität genannt, ist in der heutigen Zeit ein entscheidender Wettbewerbsfaktor. Aufgrund des stetig wachsenden IT Anteils in Produkten sowie der Tatsache, dass diese IT-gestützt hergestellt werden, haben Änderungen des Geschäftsmodells große Auswirkungen auf die Unternehmensarchitektur eines Unternehmens.
Eine Unternehmensarchitektur umspannt das Unternehmen, indem diese die fachlichen und technischen Strukturen, insbesondere die gesamte IT, des Unternehmens beinhaltet und integriert. Das Management der Unternehmensarchitektur ist die Disziplin zur Beherrschung und Abstimmung dieser Strukturen. An der Gestaltung der Unternehmensarchitektur wirken viele Stakeholder mit individuellen und teils gegensätzlichen Interessen aus den unterschiedlichsten Bereichen des Unternehmens mit. Dies macht die Entscheidungsfindung zu einer komplexen Aufgabe.
Die in dieser Arbeit entworfene integrative Methode für die Entscheidungsfindung hat das Ziel, die Betroffenen und Beteiligten, im Folgenden Stakeholder, bei ihren Entscheidungen zu unterstützen. Die Grundidee hierbei ist die systematische Einbeziehung der Interessen der Stakeholder und davon abgeleiteter Visualisierungen. Dies verleiht der Methode ihren integrativen Charakter und hilft Abhängigkeiten zwischen Stakeholdern zu erkennen. Dadurch wird die Zusammenarbeit zwischen den an Entscheidungen beteiligten Stakeholdern gefördert. Neben der systematischen Einbeziehung von Visualisierungen wird im Rahmen dieser Arbeit das Konzept der Technik eingeführt. Techniken werden ebenfalls von den Interessen der Stakeholder abgeleitet und dienen der Unterstützung bei der Durchführung von Aktivitäten der Entscheidungsfindung, indem Vorgehensweisen bei bestimmten Aufgaben vorgegeben oder Teilprozesse der Entscheidungsfindung sogar automatisiert durchgeführt werden. Das Konzept der Technik, die systematische Ableitung von den Interessen der Stakeholder sowie das Zusammenspiel mit Visualisierungen wird in dieser Arbeit in Form einer erweiterten Konzeptualisierung der Architekturbeschreibung definiert.
Da die Werkzeugunterstützung in der Praxis häufig eine Herausforderung darstellt, rundet diese Arbeit ein eigens konzipiertes und prototypisch validiertes Architekturcockpit ab. Das Cockpit ist eine auf einem elektronischen Sitzungsraum basierende Werkzeugunterstützung der eingeführten integrativen Methode.
Knowledge is an important resource, whose transfer is still not completely understood. The underlying belief of this thesis is that knowledge cannot be transferred directly from one person to another but must be converted for the transfer and therefore is subject to loss of knowledge and misunderstanding. This thesis proposes a new model for knowledge transfer and empirically evaluates this model. The model is based on the belief that knowledge must be encoded by the sender to transfer it to the receiver, who has to decode the message to obtain knowledge.
To prepare for the model this thesis provides an overview about models for knowledge transfer and factors that influence knowledge transfer. The proposed theoretical model for knowledge transfer is implemented in a prototype to demonstrate its applicability. The model describes the influence of the four layers, namely code, syntactic, semantic, and pragmatic layers, on the encoding and decoding of the message. The precise description of the influencing factors and the overlapping knowledge from sender and receiver facilitate its implementation.
The application area of the layered model for knowledge transfer was chosen to be business process modelling. Business processes incorporate an important knowledge resource of an organisation as they describe the procedures for the production of products and services. The implementation in a software prototype allows a precise description of the process by adding semantic to the simple business process modelling language used.
This thesis contributes to the body of knowledge by providing a new model for knowledge transfer, which shows the process of knowledge transfer in greater detail and highlights influencing factors. The implementation in the area of business process modelling reveals the support provided by the model. An expert evaluation indicates that the implementation of the proposed model supports knowledge transfer in business process modelling. The results of the qualitative evaluation are supported by the findings of a qualitative evaluation, performed as a quasi-experiment with a pre-test/post-test design and two experimental groups and one control group. Mann-Whitney U tests indicated that the group that used the tool that implemented the layered model performed significantly better in terms of completeness (the degree of completeness achieved in the transfer) in comparison with the group that used a standard BPM tool (Z = 3.057, p = 0.002, r = 0.59) and the control group that used pen and paper (Z = 3.859, p < 0.001, r = 0.72). The experiment indicates that the implementation of the layered model supports the creation of a business process and facilitates a more precise representation.
Data collected from internet applications are mainly stored in the form of transactions. All transactions of one user form a sequence, which shows the user´s behaviour on the site. Nowadays, it is important to be able to classify the behaviour in real time for various reasons: e.g. to increase conversion rate of customers while they are in the store or to prevent fraudulent transactions before they are placed. However, this is difficult due to the complex structure of the data sequences (i.e. a mix of categorical and continuous data types, constant data updates) and the large amounts of data that are stored. Therefore, this thesis studies the classification of complex data sequences. It surveys the fields of time series analysis (temporal data mining), sequence data mining or standard classification algorithms. It turns out that these algorithms are either difficult to be applied on data sequences or do not deliver a classification: Time series need a predefined model and are not able to handle complex data types; sequence classification algorithms such as the apriori algorithm family are not able to utilize the time aspect of the data. The strengths and weaknesses of the candidate algorithms are identified and used to build a new approach to solve the problem of classification of complex data sequences. The problem is thereby solved by a two-step process. First, feature construction is used to create and discover suitable features in a training phase. Then, the blueprints of the discovered features are used in a formula during the classification phase to perform the real time classification. The features are constructed by combining and aggregating the original data over the span of the sequence including the elapsed time by using a calculated time axis. Additionally, a combination of features and feature selection are used to simplify complex data types. This allows catching behavioural patterns that occur in the course of time. This new proposed approach combines techniques from several research fields. Part of the algorithm originates from the field of feature construction and is used to reveal behaviour over time and express this behaviour in the form of features. A combination of the features is used to highlight relations between them. The blueprints of these features can then be used to achieve classification in real time on an incoming data stream. An automated framework is presented that allows the features to adapt iteratively to a change in underlying patterns in the data stream. This core feature of the presented work is achieved by separating the feature application step from the computational costly feature construction step and by iteratively restarting the feature construction step on the new incoming data. The algorithm and the corresponding models are described in detail as well as applied to three case studies (customer churn prediction, bot detection in computer games, credit card fraud detection). The case studies show that the proposed algorithm is able to find distinctive information in data sequences and use it effectively for classification tasks. The promising results indicate that the suggested approach can be applied to a wide range of other application areas that incorporate data sequences.
Saving energy and protecting the environment became fundamental for society and politics, why several laws were enacted to increase the energy-efficiency. Furthermore, the growing number of vehicles and drivers leaded to more accidents and fatalities on the roads, why road safety became an important factor as well. Due to the increasing importance of energy-efficiency and safety, car manufacturers started to optimise the vehicle in terms of energy-effciency and safety. However, energy-efficiency and road safety can be also increased by adapting the driving behaviour to the given driving situation. This thesis presents a concept of an adaptive and rule based driving system that tries to educate the driver in energy-efficient and safe driving by showing recommendations on time. Unlike existing driving-systems, the presented driving system considers energy-efficiency and safety relevant driving rules, the individual driving behaviour and the driver condition. This allows to avoid the distraction of the driver and to increase the acceptance of the driving system, while improving the driving behaviour in terms of energy-efficiency and safety. A prototype of the driving system was developed and evaluated. The evaluation was done on a driving simulator using 42 test drivers, who tested the effect of the driving system on the driving behaviour and the effect of the adaptiveness of the driving system on the user acceptance. It has been proven during the evaluation that the energy-efficiency and safety can be increased, when the driving system was used. Furthermore, it has been proven that the user acceptance of the driving system increases when the adaptive feature was turned on. A high user acceptance of the driving system allows a steady usage of the driving system and, thus, a steady improvement of the driving behaviour in terms of energy-efficiency and safety.
Vertrauen ist eine wesentliche Ressource für die Zusammenarbeit zwischen Anbietern und Kunden. In der postmodernen Gesellschaft sind beide Seiten auf Kooperation angewiesen. Ohne Vertrauen führen gemeinsame Beziehungen jedoch selten zu den erwünschten Resultaten. Alexander Rossmann zeigt auf, wie sich das Vertrauen von Kunden stimulieren lässt und welche Verhaltensweisen zu vermeiden sind. Dabei werden personale und organisationale Vertrauensstrategien konzeptionell entwickelt und am Beispiel der IT-Branche empirisch untersucht. Eine Analyse der Auswirkungen von Vertrauen bietet differenzierte Einblicke in die Chancen und Risiken von Vertrauen aus Anbieter- und Kundenperspektive.
Im Fokus der Arbeit steht die Unterstützung der Stentgraftauswahl bei endovaskulärer Versorgung eines infrarenalen Aortenaneurysmas. Im Rahmen der Arbeit wurde eine Methode zur Auswertung von Ergebnissen einer Finite Elemente-Analyse zum Stentgraftverhalten konzipiert, implementiert und im Rahmen einer deutschlandweiten Benutzerstudie mit 16 Chirurgen diskutiert. Die entwickelte Mensch-Maschine-Schnittstelle ermöglicht dem Gefäßmediziner eine interaktive Analyse berechneter Fixierungskräfte und Kontaktzustände mehrerer Stentgrafts im Kontext mit dem zu behandelnden Aortenabschnitt. Die entwickelte Methode ermöglicht eine tiefergehende Auseinandersetzung der Mediziner mit numerischen Simulationen und Stentgraftbewertungsgrößen. Hierdurch konnte im Rahmen der Benutzerstudie das Einsatzpotenzial numerischer Simulationen zur Unterstützung der Stentgraftauswahl ermittelt und eine Anforderungsspezifikation an ein System zur simulationsbasierten Stentgraftplanung definiert werden. Im Ergebnis wurde als wesentliches Einsatzpotenzial die Festlegung eines Mindestmaßes an Überdimensionierung, die Optimierung der Schenkellänge von bifurkativen Stentgrafts sowie der Vergleich unterschiedlicher Stentgraftdesigns ermittelt. Zu den wesentlichen Funktionen eines Systems zur simulationsbasierten Stentgraftauswahl gehören eine Übersichtskarte zu farbkodiertem Migrationsrisiko pro Stentgraft und Landungszone, die Visualisierung des Abdichtungszustandes der Stentkomponenten sowie die Darstellung von Stentgraft- und Gefäßdeformationen im 3D-Modell.
This thesis studies concurrency control and composition of transactions in computing environments with long living transactions where local data autonomy of transactions is indispensable. This kind of computing architecture is referred to as a Disconnected System where reads are segregated -disconnected- from writes enabling local data autonomy. Disconnecting reads from writes is inspired by Bertrand Meyer's "Command Query Separation" pattern. This thesis provides a simple yet precise definition for a Disconnected System with a focus on transaction management. Concerning concurrency control, transaction management frameworks implement a'one concurrency control mechanism fits all needs strategy'. This strategy, however, does not consider specific characteristics of data access. The thesis shows the limitations of this strategy if transaction load increases, transactions are long lived, local data autonomy is required, and serializability is aimed at isolation level. For example, in optimistic mechanisms the number of aborts suddenly increases if load increases. In pessimistic mechanisms locking causes long blocking times and is prone to deadlocks. These findings are not new and a common solution used by database vendors is to reduce the isolation. This thesis proposes the usage of a novel approach. It suggests choosing the concurrency control mechanism according to the semantics of data access of a certain data item. As a result a transaction may execute under several concurrency control mechanisms. The idea is to introduce lanes similar to a motorway where each lane is dedicated to a certain class of vehicle with the same characteristics. Whereas disconnecting reads and writes sets the traffic's direction, the semantics of data access defines the lanes. This thesis introduces four concurrency control classes capturing the semantics of data access and each of them has an associated tailored concurrency control mechanism. Class O (the optimistic class) implements a first-committer-wins strategy, class R (the reconciliation class) implements a first-n-committers-win strategy, class P (the pessimistic class) implements a first-reader-wins strategy, and class E (the escrow class) implements a first-n-readers-win strategy. In contrast to solutions that adapt the concurrency control mechanism during runtime, the idea is to classify data during the design phase of the application and adapt the classification only in certain cases at runtime. The result of the thesis is a transaction management framework called O|R|P|E. A performance study based on the TPC-C benchmark shows that O|R|P|E has a better performance and a considerably higher commit rate than other solutions. Moreover, the thesis shows that in O|R|P|E aborts are due to application specific limitations, i.e., constraint violations and not due to serialization conflicts. This is a result of considering the semantics.