004 Datenverarbeitung; Informatik
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Currently, we observe a strong growth of services and applications, which use the Internet for data transport. However, the network requirements of these applications differ significantly. This makes network management difficult, since it complicated to separate network flows into application classes without inspecting application layer data. Network virtualization is a promising solution to this problem. It enables running different virtual network on the same physical substrate. Separating networks based on the service supported within allows controlling each network according to the specific needs of the application. The aim of such a network control is to optimize the user perceived quality as well as the cost efficiency of the data transport. Furthermore, network virtualization abstracts the network functionality from the underlying implementation and facilitates the split of the currently tightly integrated roles of Internet Service Provider and network owner. Additionally, network virtualization guarantees that different virtual networks run on the same physical substrate do not interfere with each other. This thesis discusses different aspects of the network virtualization topic. It is focused on how to manage and control a virtual network to guarantee the best Quality of Experience for the user. Therefore, a top-down approach is chosen. Starting with use cases of virtual networks, a possible architecture is derived and current implementation options based on hardware virtualization are explored. In the following, this thesis focuses on assessing the Quality of Experience perceived by the user and how it can be optimized on application layer. Furthermore, options for measuring and monitoring significant network parameters of virtual networks are considered.
Computing Generic Causes of Revelation of the Quranic Verses Using Machine Learning Techniques
(2011)
Because many verses of the holy Quran are similar, there is high probability that, similar verses addressing same issues share same generic causes of revelation. In this study, machine learning techniques have been employed in order to automatically derive causes of revelation of Quranic verses. The derivation of the causes of revelation is viewed as a classification problem. Initially the categories are based on the verses with known causes of revelation, and the testing set consists of the remaining verses. Based on a computed threshold value, a naïve Bayesian classifier is used to categorize some verses. After that, using a decision tree classifier the remaining uncategorized verses are separated into verses that contain indicators (resultative connectors, causative expressions…), and those that do not. As for those verses having indicators, each one is segmented into its constituent clauses by identification of the linking indicators. Then a dominant clause is extracted and considered either as the cause of revelation, or post-processed by adding or subtracting some terms to form a causal clause that constitutes the cause of revelation. Concerning remaining unclassified verses without indicators, a naive Bayesian classifier is again used to assign each one of them to one of the existing classes based on features and topics similarity. As for verses that could not be classified so far, manual classification was made by considering each verse as a category on its own. The result obtained in this study is encouraging, and shows that automatic derivation of Quranic verses’ generic causes of revelation is achievable, and reasonably reliable for understanding and implementing the teachings of the Quran.
Learning a book in general involves reading it, underlining important words, adding comments, summarizing some passages, and marking up some text or concepts. Once deeper understanding is achieved, one would like to organize and manage her/his knowledge in such a way that, it could be easily remembered and efficiently transmitted to others. This paper discusses about modeling religious texts using semantic XML markup based on frame-based knowledge representation, with the purpose of assisting understanding, retention, and sharing of knowledge they contain. In this study, books organized in terms of chapters made up of verses are considered as the source of knowledge to model. Some metadata representing the multiple perspectives of knowledge modeling are assigned to each chapter and verse. Chapters and verses with their metadata form a meta-model, which is represented using frames, and published on a web mashup. An XML-based annotation and visualization system equipped with user interfaces for creating static and dynamic metadata, annotating chapters’ contents according to user selected semantics, and templates for publishing generated knowledge on the Internet, has been developed. The system has been applied to the Quran, and the result obtained shows that multiple perspectives of information modeling can be successfully applied to religious texts, in order to support analysis, understanding, and retention of the texts.
Given a collection of diverging documents about some lost original text, any person interested in the text would try reconstructing it from the diverging documents. Whether it is eclecticism, stemmatics, or copy-text, one is expected to explicitly or indirectly select one of the documents as a starting point or as a base text, which could be emended through comparison with remaining documents, so that a text that could be designated as the original document is generated. Unfortunately the process of giving priority to one of the documents also known as witnesses is a subjective approach. In fact even Cladistics, which could be considered as a computer-based approach of implementing stemmatics, does not present or recommend users to select a certain witness as a starting point for the process of reconstructing the original document. In this study, a computational method using a rule-based Bayesian classifier is used, to assist text scholars in their attempts of reconstructing a non-existing document from some available witnesses. The method developed in this study consists of selecting a base text successively and collating it with remaining documents. Each completed collation cycle stores the selected base text and its closest witness, along with a weighted score of their similarities and differences. At the end of the collation process, a witness selected more often by majority of base texts is considered as the probable base text of the collection. Witnesses’ scores are weighted using a weighting system, based on effects of types of textual modifications on the process of reconstructing original documents. Users have the possibility to select between baseless and base text collation. If a base text is selected, the task is reduced to ranking the witnesses with respect to the base text, otherwise a base text as well as ranking of the witnesses with respect to the base text are computed and displayed on a bar diagram. Additionally this study includes a recursive algorithm for automatically reconstructing the original text from the identified base text and ranked witnesses.
The question of why the Quran structure does not follow its chronology of revelation is a recurring one. Some Islamic scholars such as [1] have answered the question using hadiths, as well as other philosophical reasons based on internal evidences of the Quran itself. Unfortunately till today many are still wondering about this issue. Muslims believe that the Quran is a summary and a copy of the content of a preserved tablet called Lawhul-Mahfuz located in the heaven. Logically speaking, this suggests that the arrangement of the verses and chapters is expected to be similar to that of the Lawhul-Mahfuz. As for the arrangement of the verses in each chapter, there is unanimity that it was carried out by the Prophet himself under the guidance of Angel Gabriel with the recommendation of God. But concerning the ordering of the chapters, there are reports about some divergences [3] among the Prophet’s companions as to which chapter should precede which one. This paper argues that Quranic chapters might have been arranged according to months and seasons of revelation. In fact, based on some verses of the Quran, it is defendable that the Lawhul-Mahfuz itself is understood to have been structured in terms of the months of the year. In this study, philosophical and mathematical arguments for computing chapters’ months of revelation are discussed, and the result is displayed on an interactive scatter plot.
Der große Vorteil eines q-Gramm Indexes liegt darin, dass es möglich ist beliebige Zeichenketten in einer Dokumentensammlung zu suchen. Ein Nachteil jedoch liegt darin, dass bei größer werdenden Datenmengen dieser Index dazu neigt, sehr groß zu werden, was mit einem deutlichem Leistungsabfall verbunden ist. In dieser Arbeit wird eine neuartige Technik vorgestellt, die die Leistung eines q-Gramm Indexes mithilfe zusätzlicher M-Matrizen für jedes q-Gramm und durch die Kombination mit einem invertierten Index erhöht. Eine M-Matrix ist eine Bit-Matrix, die Informationen über die Positionen eines q-Gramms enthält. Auch bei der Kombination von zwei oder mehreren Q-Grammen bieten diese M-Matrizen Informationen über die Positionen der Kombination. Dies kann verwendet werden, um die Komplexität der Zusammenführung der q-Gramm Trefferlisten für eine gegebene Suchanfrage zu reduzieren und verbessert die Leistung des n-Gramm-invertierten Index. Die Kombination mit einem termbasierten invertierten Index beschleunigt die durchschnittliche Suchzeit zusätzlich und vereint die Vorteile beider Index-Formate. Redundante Informationen werden in dem q-Gramm Index reduziert und weitere Funktionalität hinzugefügt, wie z.B. die Bewertung von Treffern nach Relevanz, die Möglichkeit, nach Konzepten zu suchen oder Indexpartitionierungen nach Wichtigkeit der enthaltenen Terme zu erstellen.
Einleitung:
Multiple-Choice-Klausuren spielen immer noch eine herausragende Rolle für fakultätsinterne medizinische Prüfungen. Neben inhaltlichen Arbeiten stellt sich die Frage, wie die technische Abwicklung optimiert werden kann. Für Dozenten in der Medizin gibt es zunehmend drei Optionen zur Durchführung von MC-Klausuren: Papierklausuren mit oder ohne Computerunterstützung oder vollständig elektronische Klausuren. Kritische Faktoren sind der Aufwand für die Formatierung der Klausur, der logistische Aufwand bei der Klausurdurchführung, die Qualität, Schnelligkeit und der Aufwand der Klausurkorrektur, die Bereitstellung der Dokumente für die Einsichtnahme, und die statistische Analyse der Klausurergebnisse.
Methoden:
An der Universität Würzburg wird seit drei Semestern ein Computerprogramm zur Eingabe und Formatierung der MC-Fragen in medizinischen und anderen Papierklausuren verwendet und optimiert, mit dem im Wintersemester (WS) 2009/2010 elf, im Sommersemester (SS) 2010 zwölf und im WS 2010/11 dreizehn medizinische Klausuren erstellt und anschließend die eingescannten Antwortblätter automatisch ausgewertet wurden. In den letzten beiden Semestern wurden die Aufwände protokolliert.
Ergebnisse:
Der Aufwand der Formatierung und der Auswertung einschl. nachträglicher Anpassung der Auswertung einer Durchschnittsklausur mit ca. 140 Teilnehmern und ca. 35 Fragen ist von 5-7 Stunden für Klausuren ohne Komplikation im WS 2009/2010 über ca. 2 Stunden im SS 2010 auf ca. 1,5 Stunden im WS 2010/11 gefallen. Einschließlich der Klausuren mit Komplikationen bei der Auswertung betrug die durchschnittliche Zeit im SS 2010 ca. 3 Stunden und im WS 10/11 ca. 2,67 Stunden pro Klausur.
Diskussion:
Für konventionelle Multiple-Choice-Klausuren bietet die computergestützte Formatierung und Auswertung von Papierklausuren einen beträchtlichen Zeitvorteil für die Dozenten im Vergleich zur manuellen Korrektur von Papierklausuren und benötigt im Vergleich zu rein elektronischen Klausuren eine deutlich einfachere technische Infrastruktur und weniger Personal bei der Klausurdurchführung.