Contributions published at Dynamic and Distributed Information Systems (Abraham Bernstein)

This thesis is based on research within the domain of context-awareness. The aim is to determine whether it is possible to increase work performance by using a mobile device. An experiment was developed, implemented and conducted in order to verify a potential impact. The aim was to find a correlation between a person’s environmental context and the work performance. In the experiment sensors attached to a mobile device were used. These sensors recorded the context of a situation in which test subjects were doing a psychological performance test. By analysing the test results and the recorded context information, we tried to find coherence to predict the performance level in certain situations. Furthermore, we analysed these predictions and tried to estimate the effect of a potential work performance improvement.

Day by day banking institutes process millions of transactions. When handling such a mass of funds it is easy to find people, who get tempted to illicitally take a piece of the cake. Such cases of fraud, specially when initiated by employees of financial institutes, are particularly severe respecting the loss expenses and loss of reputation. Until a few years ago, fraud detection based only upon the experience of anti-fraud employees and just recently computer systems, build to support the fraud prevention by applying filters on data of monetary transactions, have come up. On the search for further analytic methods to find patterns of fraud, to include these into existing software-systems, TVIS (Transaction Visualization System), a project between the University of Zurich (DDIS group) with a banking institute, has been developed. The goal of this software is to find hotspots algorithmically in the huge amount of data in information systems and to allow a paradigm shift from a case-friven approach to a broader data-monitoring approach. While TVIS uses supervised learning methods, concretely by supporting the visual search for fraud-schemes, which get identified by humans as such, this thesis is dedicated to the analysis of unsupervised methods and how they can support the work of anti-fraud employees. Traditional unsupervised data-mining-based methods require propositional data, while the data used to detect fraud-cases is typically highly relational. The problem-space becomes therefore more complicated, as single objects or attributes acquire their real importance only after analysis of their relations to other objects / entities. This is specially the case in the banking environment, where fraud schemes often encompass a multitude of transactions, accounts, and (unknowing) clients. Traditional approaches cannot deal with such relational data. This forces the search for new practices, which explicitely take relations into account and therefore deal successfully with the present multi-relational data-world. In this work, relational data-mining-methods will be evaluated, where the focus will be set on how good this approaches can be used for the practical search for fraud-patterns. With the inviii sights of this analysis a method will be presented, which shall help to carry out data-mining tasks for fraud-prevention. To prove the validity of this method the aim was to test it in its real environment, a data warehouse, as realistic data-structures and amounts of data are believed to bring more valuable insights than a laboratory environment, where the danger is big that the environment is adapted to the procedure. For this reason an application was developed, which extracts the necessary data from the data warehouse and prepares them for the evaluated algorithms / toolkits (SUBDUE and YALE). The implementation of this software gave already important insights into special peculiarities of the IT-infastructure mada available by the financial institute. Also the first tests with the presented method could be carried out. These have shown, that security aspects and the amount of data proved to make the usage of unsupervised methods in the banking environment rather difficult. It was also experienced that SUBDUE is not suited for the handling of huge amount of data. On the basis of these first experiences, and the insights, recommendations are presented on how the evaluated procedure can be optimized and how relational data-mining-methods and their environment should be designed in future, to be able to successfully accomplish the task of analytic fraud detection within the bank.

In our everyday life, we move through different environments and undertake different activities. During some of those moments, we can handle disturbances, in others they become intolerable. Because of this, context sensitive mobile-phones become a potentially important part of our future. This paper presents an experiment, in which a series of sensors was analysed for their capacity to predict the context of a mobile phone on which they were attached to. This test device was then used to record data in real world scenarios, and the accuracy of the resulting predictions was measured. It could be shown that both activities and locations could be detected quite reliably in real world conditions and that certain sensors fare better then others.

The information in bug reports influences the speed at which bugs are fixed. However, bug reports differ in their quality of information. We conducted a survey responses among the ECLIPSE developers to determine the information in reports that they widely used and the problems frequently encountered. Our results show that steps to reproduce and stack traces are most sought after by developers, while inaccurate steps to reproduce and incomplete information pose the largest hurdles. Surprisingly, developers are indifferent to bug duplicates. Such insight is useful to design new bug tracking tools that guide reporters at providing more helpful information. We also present a prototype of a quality-meter tool that measures the quality of bug reports by scanning its content.

Das Ziel dieser Arbeit ist es, einen adaptiven Eintrittstest fu?r ein bereits existierendes Lernsystem zu gestalten. Dabei handelt es sich um das CasIS-Portal, welches das Bearbeiten von Fallstudien elektronisch unterstu?tzt. In dieser Arbeit wird auf den Eintrittstest und dessen Schnittstellen zu diesem Portal eingegangen. Der Eintrittstest soll dem Benutzer vor der Bearbeitung einer Fallstudie helfen, indem er sein aktuelles Können einschätzt und ihn dementsprechend berät. Nachdem der Test die Kompetenz des Benutzers gepru?ft hat, werden ihm Lernmaterialien angeboten, damit ihm die Möglichkeit eingeräumt wird, sich optimal auf die Fallstudie vorzubereiten. Der Eintrittstest ist dabei nicht als Hu?rde zu verstehen, sondern bietet vielmehr eine Option, um Informationen u?ber das eigene Können zu erhalten. Die Arbeit umfasst sowohl die Entwicklung eines Authoring-Tools um Tests zu erstellen als auch eine Delivery-Engine, welche den Benutzer mit Fragen beliefert und ihm anschliessend Vorbereitungsmodule anbietet.The aim of this thesis is to develop an adaptive assessment test for the CasIS-Portal - an already existing e-learning system, which assists users electronically with solving case studies. This thesis is concerned with designing and attaching an assessment test to the portal. The test should give support to a user by estimating his current knowledge and then informing him accordingly. After a test has been taken and the candidate’s competence has been assessed, he will be shown the learning material, such that he has the possibility of preparing himself optimally for the subsequent case study. The test itself is not to be understood as an obstacle, but rather it provides an opportunity of obtaining information about one’s current knowledge level. The work done comprises not only the development of an authoring-tool to create tests but also a delivery-engine, which presents questions to a candidate and offers him preparation modules.

Currently proposed Web Service Technologies allow describing services syntactically and semantically such that users and software agents are able to discover, invoke, compose and monitor these services with a high degree of automation. Thereby, the services can be connected with an ontology-based semantic description. Up to the present however, none of these standards defines a concrete and self-contained way of connecting these services among each other. This thesis demonstrates how web service creation and web service discovery can benefit from such connections among services and how these benefits can be accomplished by introducing Inheritance Relationships (IR) for OWL-S (OWL-S: Semantic Markup for Web Services, 2004) using ideas from computer science about inheritance. For service creation, this thesis provides the possibility to share specific elements among these services. This sharing is expected to substantially reduce the amount of work necessary for creating and maintaining services. For service discovery, an interpretation of IRs among these services is provided in order to discover service substitutes. These substitutes increase the choice of a service user or the availability of a specific service. Together with the developed prototype, the thesis demonstrates the basic feasibility of applying inheritance for OWL-S by illustrating several use cases. In addition, the thesis provides a basis for further tool development.

The popularity of the Internet is growing steadily and with it the amount of data shared online. As a consequence, it is getting more and more difficult to find and organize the data on the Web. With the Semantic Web a way to structure this data has been provided, and to query it, SQL-like query languages were developed. Since these formal languages are not likely to be used by “standard” users, the development of natural language interfaces for querying Semantic Web knowledge bases has become a popular subject. The Ginseng project approaches this subject by offering a controlled natural language query interface for the Semantic Web to the user. In this thesis, the extension of the Ginseng application to a multilingual system as well as the implementation of a user interface for managing the semantic annotation of ontologies in Ginseng are presented.

We used the datasets of the NIPS 2003 challenge on feature selection as part of the practical work of an undergraduate course on feature extraction. The students were provided with a toolkit implemented in Matlab. Part of the course requirements was that they should outperform given baseline methods. The results were beyond expectations: the student matched or exceeded the performance of the best challenge entries and achieved very effective feature selection with simple methods. We make available to the community the results of this experiment and the corresponding teaching material. These results also provide a new baseline for researchers in feature selection.

In dieser Bachelorarbeit wird ein regel- und statistikbasiertes Empfehlungssystem für die Planung des Masterstudiums in Informatik an der Universität Zürich spezifiziert, entworfen und an einem Prototypen erprobt. Das System unterstützt Informatikstudierende bei der automatischen Erstellung von Studienplänen. Dadurch wird zum einen die Einhaltung der Studienreglemente garantiert. Andererseits erhalten die Studierenden ein Bild darüber, wie ihr Masterstudium aussehen könnte. Sie müssen dazu die Daten ihres Leistungsausweises zur Verfügung stellen und Präferenzen zur Studienrichtung und zur Modulwahl angeben. Aufgrund dieser Daten erstellt das System mittels mehrerer Filter- und Sortierfunktionen die gewünschten Studienpläne. In einer Evaluation wurden Studierende um die manuelle Erstellung eines Studienplans und der Angabe der Daten zur automatischen Erstellung angefragt. Eine Analyse der Resultate und ein Vergleich zwischen dem manuellen und dem automatisch erstellten Studienplan hat ergeben, dass die Qualität von letzterem stark von der Qualität und der Menge der Präferenzangaben des Studenten abhängt. Zudem kam heraus, dass das System zur optimalen Nutzung mit zusätzlichen Features ausgestattet werden sollte. This Bachelor Thesis describes the specification, design and implementation of a rule- and statistics based recommendation system for the planning of the master study in informatics at the University of Zurich. The system supports students in automatically generating study plans. On one hand, this guaranties the compliance with the reglements of study. On the other hand, the students quickly get a picture of how a master study plan can look like. For this to work, the student has to provide data of his transcript of records, some details concerning his course of study and a choice of preferred lecture contents. Based on this data, the system generates the study plans using filtering and sorting functions. In an evaluation, some students were asked to provide a manually created study plan and the data for automatically generating study plans. The analysis of the results and a comparison of the manually and automatically generated study plans showed that the quality and quantity of the provided data have a strong impact on the quality of the resulting study plans. To enhance the system, further features should be implemented.

In the last few years the e-learning offers from the higher education schools have increased. They supply whole courses and learning materials in the internet for their students. So the internet gives the possibility to have another learning method. The more learning ressources exist in a portal, it is much more difficult for the students to find the right materials and courses. Today the main approach is to offer a full text search or a catego-risation of course materials. New developments like faceted browsing or semantic annotation is rarely used. This thesis describes the CasIS portal. It is developed for the master studies in Computer Science at the University of Zurich. It supplies different case studies in the dissimilar areas of Information Systems. For a better usability the searching of the right ressources is very important. With the aid of the semantic web and a facetted browsing tool, the student gets the ability to find the appropriate ressources easily.

Predicting the defects in the next release of a large software system is a very valuable asset for the pro ject manger to plan her resources. In this paper we argue that temporal features (or aspects) of the data are central to prediction performance. We also argue that the use of non-linear models, as opposed to traditional regression, is necessary to uncover some of the hidden interrelationships between the features and the defects and maintain the accuracy of the prediction in some cases. Using data obtained from the CVS and Bugzilla repositories of the Eclipse pro ject, we extract a number of temporal features, such as the number of revisions and number of reported issues within the last three months. We then use these data to predict both the location of defects (i.e., the classes in which defects will occur) as well as the number of reported bugs in the next month of the pro ject. To that end we use standard tree-based induction algorithms in comparison with the traditional regression. Our non-linear models uncover the hidden relationships between features and defects, and present them in easy to understand form. Results also show that using the temporal features our prediction model can predict whether a source file will have a defect with an accuracy of 99% (area under ROC curve 0.9251) and the number of defects with a mean absolute error of 0.019 (Spearman’s correlation of 0.96).

Almost every Internet user knows about the problem of SPAM. At work, especially, it costs time to sort out irrelevant emails. This thesis deals with the problem of SPAM and its consequences on productivity at work. For this reason an experiment has been conducted to examine the distraction of SPAM and its perception. A few hypotheses, stated in advance, have been reviewed on basis of this experiment. The results and the interpretation are presented and discussed in this thesis.

Der Java RDF Parser (JRP) ist ein Programm zum Einlesen von Files im RDF Format und Extrahieren von transaktionsbasierten Daten, die anschliessend in einer Datenbank gespeichert werden können. Diese Arbeit handelt von der Entwicklung von JRP und bietet dem Leser einen Einblick in das Design des Codes, das Datenbank-Schema und die Anbindung sowie eine Evaluation von Jena, der Java Library die fu?r das Parsen der Daten benutzt wird. Selbstverständlich wurde das Programm mit realen Daten getestet und bewies auf diese Art und Weise seine korrekte Funktionalität. Leider kann bis jetzt nichts u?ber die Skalierbarkeit des Parsers gesagt werden, da fu?r die Performance Tests keine grossen Datensätze vorhanden waren. The Java RDF Parser (JRP) is a program to read in files in RDF format and extract transactional data from it that can be stored in a database afterwards. This thesis is about the development of JRP and gives an insight into the design of the code, the database schema and connection, as well as an evaluation of Jena, the Java library that is used to parse the input files. Naturally, the program was tested with real data and proved the desired functionality. Unfortunately, nothing can be said about the scalability of the parser, because there were no large datasets available for performance tests.

The aim of this thesis is to implement a platform to enable the alumni associations to analyse their member-databases. Using statistical methods and data-mining algorithms, this platform should allow the visualization and appraisal of member behaviour and member structure. Four different alumni organisations utilise this platform in form of a web-based application to maintain their databases. They form the base of the following evaluations.

So far, culture has played a minor role in the design of software. Our experience with imbuto, a program designed for Rwandan agricultural advisors, has shown that cultural adaptation increased efficiency, but was extremely time-consuming and, thus, prohibitively expensive. In order to bridge the gap between cost-savings on one hand, and international usability on the other, this paper promotes the idea of culturally adaptive software. In contrast to manual localization, adaptive software is able to acquire details about an individual's cultural identity during use. Combining insights from the related fields international usability, user modeling and user interface adaptation, we show how research findings can be exploited for an integrated approach to automatically adapt software to the user's cultural frame.

The goal of this thesis is the understanding of temporal patterns in the Austrian Social Security Database to derive labor market dynamics. As these structures are very complex, conventional data mining approaches turned out to be inadequate for interpretation and knowledge discovery. The main challenge is the intuitive representation of the time dimension. Therefore, we keep the time dimension by generating movies of concatenated probabilistic model visualizations. Using this combination of temporal and visual data mining allows us to identify various effects such as seasonal hiring cycles, gender and age-related employment dynamics, and demographic influences.

The increasing proliferation of mobile phones has a significant influence on our daily lifes. Allthough the increasing use of mobile devices has brought several advantages, it also has the negative effect of unwanted disturbance and interruptions. It is desirable that a mobile phone has the ability to adapt to the current situation it is in. For such an adaption to become possible, the mobile phone would need to have information about its current context. To achieve this goal, a software is implemented which gathers data from a variety of sensors on a mobile phone. This software is then being used in a prototype experiment. In this experiment we try to determine if it is possible to predict a users activity and location based on the collected data. The software implemented in this thesis and the results of the experiment help to prepare and conduct follow-up experiments in the field of context awareness and human interuptibility research.

This proposal explores a unified framework to solve Semantic Web tasks that often require similarity measures, such as RDF retrieval, ontology alignment, and semantic service matchmaking. Our aim is to see how far it is possible to integrate user-defined similarity functions (UDSF) into SPARQL to achieve good results for these tasks.We present some research questions, summarize the experimental work conducted so far, and present our research plan that focuses on the various challenges of similarity querying within the Semantic Web.

null

Today’s telecommunication companies have at their disposal large quantities of detailed transaction data. Methods of data mining can be utilized for generating information on product use, customer behaviour and interaction between customers. Cross-selling analyses, customer segmentation and social network analysis thereby represent only some of the practices which can be employed for facilitating direct marketing procedures. This present thesis illustrates an approach for identifying customer groups and their networks, from which management implications may be derived by means of propositional as well as relational data mining. In this context triple play customers - i.e. subscribers of broadband internet, fixed-line telephony and digital TV - were segmented on the basis of data generated from product use. In addition, network analysis and the search for multi-relational patterns provided further insight into both customer types and their respective needs.