25,936 research outputs found
Interview with Peter Dolog
No full textPeter Dolog is associate professor at the department of computer science, Aalborg University in Denmark where he works since September 2006. His main research interests are applied machine learning, personalization, recommender systems, user modeling, and web science and engineering. Peter Dolog is a general chair of ACM Hypertext 2017 conference. He was also a general chair of ICWE 2013 and UMAP 2014 conferences. He served on steering committee for ICWE conferences between 2013 and 2016. He participated in various roles in many conference and workshops. He also participated in several research and management roles in several EU project and networks. Peter Dolog received his doctoral degree in computer science from Leibniz University of Hannover in 2006 where he was employed at L3S Research Center between 2002 and 2006.</jats:p
Component-Based, Client-Oriented Web Engineering: Issues, Advancements and Opportunities
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Tag cloud generation for results of multiple keywords queries
No full textIn this paper we study tag cloud generation for retrieved results of multiple keyword queries. It is motivated by many real world scenarios such as personalization tasks, surveillance systems and information retrieval tasks defined with multiple keywords.We adjust the state-of-the-art tag cloud generation techniques for multiple keywords query results. Consequently, we conduct the extensive evaluation on top of three distinct collaborative tagging systems. The graph-based methods perform significantly better for the Movielens and Bibsonomy datasets. Tag cloud generation based on maximal coverage is more suitable for the Delicious dataset because of the different statistical properties of the dataset <br/
Web Engineering - 13th International Conference, ICWE 2013, Aalborg, Denmark, July 8-12, 2013. Proceedings
No full textAuthor Peter FitzSimons speaking at the National Library of Australia, Canberra, 13 November 2012 /
No full textTitle from acquisitions documentation.; Part of the collection: Portraits of author Peter FitzSimons speaking at the National Library of Australia, Canberra, 13 November 2012.; Acquired in digital format; access copy available online.; Mode of access: Online.; Photographed by a staff member of the National Library of Australia
Engineering Web Applications
No full textNowadays, Web applications are almost omnipresent. The Web has become a platform not only for information delivery, but also for eCommerce systems, social networks, mobile services, and distributed learning environments. Engineering Web applications involves many intrinsic challenges due to their distributed nature, content orientation, and the requirement to make them available to a wide spectrum of users who are unknown in advance. The authors discuss these challenges in the context of well-established engineering processes, covering the whole product lifecycle from requirements engineering through design and implementation to deployment and maintenance. They stress the importance of models in Web application development, and they compare well-known Web-specific development processes like WebML, WSDM and OOHDM to traditional software development approaches like the waterfall model and the spiral model. Important problem areas inherent to the Web, like localization, personalization, accessibility, and usage analysis, are dealt with in detail, and a final chapter provides both a description of and an outlook on recent Semantic Web and Web 2.0 developments. Overall, their book delivers a comprehensive presentation of the state-of-the-art in Web application development and thus forms an ideal basis for academic or industrial courses in this or related areas. It is equally suitable for self-study by researchers or advanced professionals who require an overview on how to use up-to-date Web technologies.</p
idSpace
No full textSloep, P.B. & Dolog, P. (2007, December) idSpace. Presentation given at Digital Libraries & technology-enhanced learning, Call 3 information days, 17-18 December 2007, Luxembourg.short summary of the idSpace project, presented in 8 slide
Moral Good, the Beatific Vision, and God’s Kingdom Writings by Germain Grisez and Peter Ryan, S.J.. Edited by Peter J. Weigel
No full textFor close to half a century, the work of Germain Grisez has been highly influential, and his writings continue to receive considerable attention from philosophers and theologians of diverse viewpoints. His co-author for this work is the professor and noted moral theologian Fr. Peter Ryan, S.J., currently the executive director of the Secretariat of Doctrine and Canonical Affairs of the United States Conference of Catholic Bishops (USCCB). These two eminent scholars explore fundamental questions about Christian eschatology, moral theory, the purpose of human life, and the promise of human fulfilment. The authors examine Christian teaching on the final destiny of persons, investigating the meaning of God's kingdom, the hope of the beatific vision, and the centrality of moral goodness and divine grace in one's final end. This work is an ideal source for students, scholars, ministers and lay persons interested in basic questions of Christian theology, the philosophy of religion, ethical theory, and Catholic doctrin
Distributed Bayesian Networks for User Modeling
No full textThe World Wide Web is a popular platform for providing eLearning applications to a wide spectrum of users. However – as users differ in their preferences, background, requirements, and goals – applications should provide personalization mechanisms. In the Web context, user models used by such adaptive applications are often partial fragments of an overall user model. The fragments have then to be collected and merged into a global user profile. In this paper we investigate and present algorithms able to cope with distributed, fragmented user models – based on Bayesian Networks – in the context of Web-based eLearning platforms. The scenario we are tackling assumes learners who use several systems over time, which are able to create partial Bayesian Networks for user models based on the local system context. In particular, we focus on how to merge these partial user models. Our merge mechanism efficiently combines distributed learner models without the need to exchange internal structure of local Bayesian networks, nor local evidence between the involved platforms
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