Phylogenetic tree analysis using molecular sequences continues to expand beyond the 16S rRNA marker. By addressing the multi-copy issue known as the intra-heterogeneity, this paper restores the focus in using the 16S rRNA marker. Through use of a novel learning and model building algorithm, the multiple gene copies are integrated into a compact complex signature using the Extensible Markov Model (EMM). The method clusters related sequence segments while preserving their inherent order to create an EMM signature for a microbial organism. A library of EMM signatures is generated from which samples are drawn for phylogenetic analysis. By matching the components of two signatures, referred to as quasi-alignment, the differences are highlighted and scored. Scoring quasi-alignments is done using adapted Karlin-Altschul statistics to compute a novel distance metric. The metric satisfies conditions of identity, symmetry, triangular inequality and the four point rule required for a valid evolution distance metric. The resulting distance matrix is input to PHYologeny Inference Package (PHYLIP) to generate phylogenies using neighbor joining algorithms. Through control of clustering in signature creation, the diversity of similar organisms and their placement in the phylogeny is explained. The experiments include analysis of genus Burkholderia, a random microbial sample spanning several phyla and a diverse sample that includes RNA of Eukaryotic origin. The NCBI sequence data for 16S rRNA is used for validation.

Finding the right pricing for music downloads is of ample importance to the recording industry and music download service providers. For the recently introduced music downloads, reference prices are still developing and to find a revenue maximizing pricing scheme is a challenging task. The most commonly used approach is to employ linear pricing (e.g., iTunes, musicload). Lately, subscription models have emerged, offering their customers unlimited access to streaming music for a monthly fee (e.g., Napster, RealNetworks). However, other pricing strategies could also be used, such as quantity rebates starting at certain download volumes. Research has been done in this field and Buxmann et al. (2005) have shown that price cuts can improve revenue. In this paper we apply different approaches to estimate consumer's willingness to pay (WTP) for music downloads and compare our findings with the pricing strategies currently used in the market. To make informed decisions about pricing, knowledge about the consumer's WTP is essential. Three approaches based on adaptive conjoint analysis to estimate the WTP for bundles of music downloads are compared. Two of the approaches are based on a status-quo product (at market price and alternatively at an individually self-stated price), the third approach uses a linear model assuming a fixed utility per title. All three methods seem to be robust and deliver reasonable estimations of the respondent's WTPs. However, all but the linear model need an externally set price for the status-quo product which can introduce a bias.

The free and extensible statistical computing environment R with its enormous number of extension packages already provides many state-of-the-art techniques for data analysis. Support for association rule mining, a popular exploratory method which can be used, among other purposes, for uncovering cross-selling opportunities in market baskets, has become available recently with the R extension package arules. After a brief introduction to transaction data and association rules, we present the formal framework implemented in arules and demonstrate how clustering and association rule mining can be applied together using a market basket data set from a typical retailer. This paper shows that implementing a basic infrastructure with formal classes in R provides an extensible basis which can very efficiently be employed for developing new applications (such as clustering transactions) in addition to association rule mining.

Mining association rules is an important technique for discovering meaningful patterns in transaction databases. In the current literature, the properties of algorithms to mine association rules are discussed in great detail. We present a simple probabilistic framework for transaction data which can be used to simulate transaction data when no associations are present. We use such data and a real-world grocery database to explore the behavior of confidence and lift, two popular interest measures used for rule mining. The results show that confidence is systematically influenced by the frequency of the items in the left-hand-side of rules and that lift performs poorly to filter random noise in transaction data. The probabilistic data modeling approach presented in this paper not only is a valuable framework to analyze interest measures but also provides a starting point for further research to develop new interest measures which are based on statistical tests and geared towards the specific properties of transaction data.

Though reservation prices are needed for many business decision processes, e.g., pricing new products, it often turns out to be difficult to measure them. Many researchers reuse conjoint analysis data with price as an attribute for this task (e.g., Kohli and Mahajan (1991)). In this setting the information if a consumer buys a product at all is not elicited which makes reservation price estimation impossible. We propose an additional interview scene at the end of the adaptive conjoint analysis (Johnson (1987)) to estimate reservation prices for all product configurations. This will be achieved by the usage of product stimuli as well as price scales that are adapted for each proband to reflect individual choice behavior. We present preliminary results from an ongoing large-sample conjoint interview of customers of a major mobile phone retailer in Germany.

ePubWU ist eine elektronische Plattform für wissenschaftliche Publikationen der Wirtschaftsuniversität Wien, wo forschungsbezogene Veröffentlichungen der WU im Volltext über das WWW zugänglich gemacht werden. ePubWU wird als Gemeinschaftsprojekt der Universitätsbibliothek der Wirtschaftsuniversität Wien und der Abteilung für Informationswirtschaft betrieben. Derzeit werden in ePubWU zwei Publikationsarten gesammelt - Working Papers und Dissertationen. In dem Beitrag werden Erfahrungen der über zweijährigen Laufzeit des Projektes dargestellt, u.a. in den Bereichen Akquisition, Workflows, Erschließung, Vermittlung.

With customer relationship management (CRM) companies move away from a mainly product-centered view to a customer-centered view. Resulting from this change, the effective management of how to keep contact with customers throughout different channels is one of the key success factors in today's business world. Company Web sites have evolved in many industries into an extremely important channel through which customers can be attracted and retained. To analyze and optimize this channel, accurate models of how customers browse through the Web site and what information within the site they repeatedly view are crucial. Typically, data mining techniques are used for this purpose. However, there already exist numerous models developed in marketing research for traditional channels which could also prove valuable to understanding this new channel. In this paper we propose the application of an extension of the Logarithmic Series Distribution (LSD) model repeat-usage of Web-based information and thus to analyze and optimize a Web Site's capability to support one goal of CRM, to retain customers. As an example, we use the university's blended learning web portal with over a thousand learning resources to demonstrate how the model can be used to evaluate and improve the Web site's effectiveness.

In this paper we discusses in detail a possible methodology for collecting repository data on a large number of open source software projects from a single project hosting and community site. The process of data retrieval is described along with the possible metrics that can be computed and which can be used for further analyses. Example research areas to be addressed with the available data and first results are given. Then, both advantages and disadvantages of the proposed methodology are discussed together with implications for future approaches.

In this paper we report possibilities and experiences from employing Counter-Strike, a popular multi-player Internet computer game and its resulting online community in research on cooperative behaviour. Advantages from using this game include easy availability of rich data, the emphasis on team-playing, as well as numerous possibilities to change the experiment settings. We use descriptive game theory and statistical methods to explore cooperation within the game as well as the way the player community deals with disruptive behaviour. After a quick introduction to the basic rules of Counter-Strike, we describe the setup of the Internet game server used. We then present empirical results from the game server logs where cooperation within the game is analyzed from a game theoretic perspective. Finally we discuss the applications of our results to other online communities, including cooperation and self-regulation in open source teams.

Scientific library systems are a very promising application area for recommender services. Scientific libraries could easily develop customer-oriented service portals in the style of amazon.com. Students, university teachers and researchers can reduce their transaction cost (i.e. search and evaluation cost of information products). For librarians, the advantage is an improvement of the customer support by recommendations and the additional support in marketing research, product evaluation, and book selection. In this contribution we present a strategy for integrating a behavior-based distributed recommender service in legacy library systems with minimal changes in the legacy system.

In this contribution we present a systematic evaluation and comparison of recommender systems based on simple association rules and on repeat-buying theory. Both recommender services are based on the customer purchase histories of a medium-sized B2B-merchant for computer accessories. With the help of product managers an evaluation set for recommendations was generated. With regard to this evaluation set, recommendations produced by both methods are evaluated and several error measures are computed. This provides an empirical test whether frequent item sets or outliers of a stochastic purchase incidence model are suitable concepts for automatically generation recommendations. Furthermore, the loss function (performance measures) of the two models are compared and the sensitivity with regard to a misspecification of the model parameters is discussed.

Immer mehr Unternehmen setzen betriebswirtschaftliche Standardsoftwarepakete wie beispielsweise SAP R/3 oder BaaN ein. Die Auswahl und die Einführung solcher Systeme stellt für die meisten Unternehmen ein strategisch wichtiges IT-Projekt dar, das mit massiven Risiken verbunden ist. Bei der Auswahl des am besten geeigneten Systems gilt es einen Gruppenentscheidungsprozess zu unterstützen. Das darauf folgende Einführungsprojekt muss effizient, den ”best practices” entsprechend, durchgeführt werden. In dieser Arbeit wird anhand von Beispielen aufgezeigt, wie beide Prozesse - die Auswahl und die Einführung - durch die Data Envelopment Analysis unterstützt werden können.

Wissenschaftliche Bibliotheken stellen ein vielversprechendes Anwendungsfeld für Recommenderdienste dar. Wissenschaftliche Bibliotheken können leicht kundenzentrierte Serviceportale im Stil von amazon.com entwickeln. Studenten, Universitätslehrer und -forscher können ihren Anteil an den Transaktionskosten (z.B. Such- und Bewertungskosten für Informationsprodukte) reduzieren. Für Bibliothekare liegt der Vorteil in einer Verbesserung der Kundenberatung durch Empfehlungen und einer zusätzlichen Unterstützung bei der Marktforschung, Produktbewertung und dem Bestandsmanagement. In diesem Beitrag präsentieren wir eine Strategie, mit der verhaltensbasierte, verteilte Recommenderdienste in bestehende Bibliothekssysteme mit minimalem Aufwand integriert werden können und berichten über unsere Erfahrungen bei der Einführung eines solchen Dienstes an der Universitätsbibliothek der Universität Karlsruhe (TH).

The purpose of this article is to promote reuse of domain knowledge by introducing patterns already in the analysis phase of the software life-cycle. We propose an outline template for analysis patterns that strongly supports the whole analysis process from the requirements analysis to the analysis model and further on to its transformation into a flexible and reusable design and implementation. As an example we develop a family of analysis patterns in this paper that deal with a series of pressing problems in cooperative work, collaborative information filtering and sharing, and knowledge management. We evaluate the reuse potential of these patterns by analyzing several components of an information system, that was developed for the Virtual University project of the Vienna University of Economics and Business Administration. The findings of this analysis suggest that using patterns in the analysis phase has the potential to reducing development time significantly by introducing reuse already at the analysis stage and by improving the interface between analysis and design phase.

Association rules are a widely used technique to generate recommendations in commercial and research recommender systems. Since more and more Web sites, especially of retailers, offer automatic recommender services using Web usage mining, evaluation of recommender algorithms becomes increasingly important. In this paper we first present a framework for the evaluation of different aspects of recommender systems based on the process of discovering knowledge in databases of Fayyad et al. and then we focus on the comparison of the performance of two recommender algorithms based on frequent itemsets. The first recommender algorithm uses association rules, and the other recommender algorithm is based on the repeat-buying theory known from marketing research. For the evaluation we concentrated on how well the patterns extracted from usage data match the concept of useful recommendations of users. We use 6 month of usage data from an educational Internet information broker and compare useful recommendations identified by users from the target group of the broker with the results of the recommender algorithms. The results of the evaluation presented in this paper suggest that frequent itemsets from purchase histories match the concept of useful recommendations expressed by users with satisfactory accuracy (higher than 70%) and precision (between 60% and 90%). Also the evaluation suggests that both algorithms studied in the paper perform similar on real-world data if they are tuned properly.

In the context of a virtual university's information broker we study the consumption patterns for information goods and we investigate if Ehrenberg's repeat-buying theory which successfully models regularities in a large number of consumer product markets can be applied in electronic markets for information goods too. First results indicate that Ehrenberg's repeat-buying theory succeeds in describing the consumption patterns of bundles of complementary information goods reasonably well and that this can be exploited for automatically generating anonymous recommendation services based on such information bundles. An experimental anonymous recommender service has been implemented and is currently evaluated in the Virtual University of the Vienna University of Economics and Business Administration at http://vu.wu-wien.ac.at.

Recently recommender systems started to gain ground in commercial Web-applications. For example, the online-bookseller amazon.com recommends his customers books similar to the ones they bought using the analysis of observed purchase behavior of consumers. In this article we describe a generic architecture for recommender services for information markets which has been implemented in the setting of the Virtual University of the Vienna University of Economics and Business Administration (http://vu.wu-wien.ac.at). The architecture of a recommender service is defined as an agency of interacting software agents. It consists of three layers, namely the meta-data management system, the broker management system and the business-to-customer interface.

In diesem Beitrag wird die Rolle von Recommendersystemen und ihr Potential in der Lehr-, Lern- und Forschungsumgebung einer Virtuellen Universität untersucht.Die Hauptidee dieses Beitrags besteht darin, die Informationsaggregationsfähigkeiten von Recommendersystemen in einer Virtuellen Universität auszunutzen, um Tutoren-und Beratungsdienste in einer Virtuellen Universität automatisch zu verbessern, um damit Betreuung und Beratung von Studierenden zu personalisieren und für eine größere Anzahl von Teilnehmern bei gleichzeitiger Entlastung der Lehrenden verfügbar zu machen. Im zweiten Teil dieses Beitrags werden die Recommenderdienste von myVU, der Sammlung der personalisierten Dienste der Virtuellen Universität (VU) der Wirtschaftsuniversität Wien und ihre nicht-personalisierten Variantenbeschrieben, die im Wesentlichen auf beobachtetem Benutzerverhalten und, in der personalisierten Variante, zusätzlich auf Selbstselektion durch Selbsteinschätzung der Erfahrung in einem Fachgebiet beruhen. Abschließend wird noch der innovative Einsatz solcher Systeme diskutiert und an einigen Szenarien beschrieben.

In this contribution we transfer a customer purchase incidence model for consumer products which is based on Ehrenberg's repeat-buying theory to Web-based information products. Ehrenberg's repeat-buying theory successfully describes regularities in a large number of consumer product markets. We show that these regularities exist in electronic markets for information goods too, and that purchase incidence models provide a well founded theoretical foundation for recommender and alert systems. The article consists of three parts. First, we present the architecture of an information market and its instrumentation for collecting data on customer behavior. In the second part Ehrenberg's repeat-buying theory and its assumptions are reviewed and adapted for Web-based information markets. Finally, we present the empirical validation of the model based on data collected from the information market of the Virtual University of the Vienna University of Economics and Business Administration at http://vu.wu-wien.ac.at

Today users of Internet information services like e.g. Yahoo! or AltaVista often experience high search costs. One important reason for this is the necessity to browse long reference lists manually, because of the well-known problems of relevance ranking. A possible remedy is to complement the references with automatically generated labels which provide valuable information about the referenced information source. Presenting suitably labelled lists of references to users aims at improving the clarity and thus comprehensibility of the information offered and at reducing the search cost. In the following we survey several dimensions for labelling (time, frequency of usage, region, language, subject, industry, and preferences) and the corresponding classification problems. To solve these problems automatically we sketch for each problem a pragmatic mix of machine learning methods and report selected results.

Navigation design for web-based information systems (e.g. e-commerce sites, intranet solutions) that ignores user-participation reduces the system's value and can even lead to system failure. In this paper we introduce a user-centered, explorative approach to re-designing navigation structures of web-based information systems, and describe how it can be implemented in order to provide flexibility and reduce maintenance costs. We conclude with lessons learned from the navigation re-design project at the Vienna University of Economics and Business Administration.