Because of the USA PATRIOT Act and similar legislation that allows the government to track the actions of individuals suspected of terrorist activities, many librarians are concerned about protecting information about library use at any cost. Some propose that the solution is to delete all data from the operational databases whenever possible; in fact, a recent New York Times article discusses daily shredding of library records from the Santa Cruz Public Library System (“Librarians Use Shredder to Show Opposition to New F.B.I. Powers”, Apr. 7th, 2003). However, deleting all data associated with library transactions will make data-based evaluation and justification of library services difficult; therefore, libraries must seek a balance between protecting the privacy of patrons and maintaining a history of library transactions.

The goal of this brief article is to explain the bibliomining process. Emphasis is placed on data warehousing and patron privacy issues because they are required before anything else can begin. It is essential to capture our data-based institutional records while still protecting the privacy of users. By using a data warehouse, both goals can be met. Once the data warehouse is in place, the library can use reporting and exploration tools to gain a more thorough knowledge of their user communities and resource utilization.

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This thesis covers the analysis of sparse and irregular fuel consumption data of long distance haulage articulate trucks. It is shown that this kind of data is hard to analyse with multivariate as well as with functional methods. To be able to analyse the data, Principal Components Analysis through Conditional Expectation (PACE) is used, which enables the use of observations from many trucks to compensate for the sparsity of observations in order to get continuous results. The principal component scores generated by PACE, can then be used to get rough estimates of the trajectories for single trucks as well as to detect outliers. The data centric approach of PACE is very useful to enable functional analysis of sparse and irregular data. Functional analysis is desirable for this data to sidestep feature extraction and enabling a more natural view on the data.

There are two common approaches to implement a virtual machine (VM) for a dynamic object-oriented language. On the one hand, it can be implemented in a C-like language for best performance and maximum control over the resulting executable. On the other hand, it can be implemented in a language such as Java that allows for higher-level abstractions. These abstractions, such as proper object-oriented modularization, automatic memory management, or interfaces, are missing in C-like languages but they can simplify the implementation of prevalent but complex concepts in VMs, such as garbage collectors (GCs) or just-in-time compilers (JITs). Yet, the implementation of a dynamic object-oriented language in Java eventually results in two VMs on top of each other (double stack), which impedes performance. For statically typed languages, the Maxine VM solves this problem; it is written in Java but can be executed without a Java virtual machine (JVM). However, it is currently not possible to execute dynamic object-oriented languages in Maxine. This work presents an approach to bringing object models and execution models of dynamic object-oriented languages to the Maxine VM and the application of this approach to Squeak/Smalltalk. The representation of objects in and the execution of dynamic object-oriented languages pose certain challenges to the Maxine VM that lacks certain variation points necessary to enable an effortless and straightforward implementation of dynamic object-oriented languages' execution models. The implementation of Squeak/Smalltalk in Maxine as a feasibility study is to unveil such missing variation points. / Es gibt zwei übliche Wege, um eine virtuelle Maschine (VM) für eine dynamische objektorientierte Programmiersprache zu implementieren. Einerseits kann sie in einer C-ähnlichen Programmiersprache geschrieben werden, um bestmögliche Leistung zu erzielen und größtmöglichen Einfluss auf das sich ergebende ausführbare Programm zu haben. Andererseits kann sie in einer Sprache wie Java geschrieben werden, die weitreichendere Abstraktionen als die C-ähnlichen Sprachen erlaubt. Diese Abstraktionen, beispielsweise echte objektorientierte Modularisierung, automatische Speicherverwaltung oder explizite Schnittstellen, erlauben eine vereinfachtes Implementieren von VM-typischen, aber komplexen Konzepten, wie Speicherbereinigung (garbage collector, GCs) oder just-in-time-Compilern (JITs). Wird eine dynamische objektorientierte Programmiersprache in Java geschrieben, ist jedoch mit Leistungseinbußen zu rechen, da effektiv zwei aufeinander aufbauende virtuelle Maschinen entstehen. Die Maxine VM löst diese Problem für statisch getypte Programmiersprachen; sie ist in Java geschrieben, kann aber ohne jegliche Java VM (JVM) laufen. Es ist jedoch momentan nicht möglich, dynamische objektorientierte Programmiersprachen in Maxine auszuführen. Diese Arbeit stellt einen Ansatz zur Umsetzung von Objekt- und Ausführungsmodellen dynamischer objektorientierter Programmiersprachen innerhalb der Maxine VM vor, sowie dessen Anwendung auf Squeak/Smalltalk. Dabei stellt die Representation von Objekten und die Ausführung dynamischer objektorientierter Programmiersprachen bestimmte Heraus- und Anforderungen an die Maxine VM, die trotz des Fokus auf Modularität und Konfigurierbarkeit einige Variationspunkte vermissen lässt, welche wiederum für eine einfache Implementierung von besagten Ausführungsmodellen jedoch unerlässlich sind. Daher sollen durch eine testweise Implementierung von Squeak/Smalltalk in Maxine eben jene Variationspunkte identifiziert werden.

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