57 research outputs found
Towards Mapping-Based Document Retrieval in Heterogeneous Digital Libraries
In many scientific domains, researchers depend on a timely and
efficient access to available publications in their particular
area. The increasing availability of publications in electronic
form via digital libraries is a reaction to this need. A remaining
problem is the fact that the pool of all available publications is
distributed between different libraries. In order to increase the
availability of information, these different libraries should be
linked in such a way, that all the information is available via
any one of them. Peer-to-peer technologies provide sophisticated
solutions for this kind of loose integration of information
sources. In our work, we consider digital libraries that organize
documents according to a dedicated classification hierarchy or
provide access to information on the basis of a thesaurus. These
kinds of access mechanisms have proven to increase the retrieval
result and are therefore widely used. On the other hand, this
causes new problems as different sources will use different
classifications and thesauri to organize information. This means,
that we have to be able to mediate between these different
structures. Integrating this mediation into the information
retrieval process is a problem that to the best of our knowledge
has not been addressed before
Zerber+R: Top-k Retrieval from a Confidential Index
Zerr, S., Olmedilla, D., Nejdl, W., & Siberski, W. (2009). Zerber+R: Top-k Retrieval from a Confidential Index. Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology (pp. 439-449). March, 24-26, 2009, Saint Petersburg, Russia (ISBN: 978-1-60558-422-5).Privacy-preserving document exchange among collaboration groups in an enterprise as well as across enterprises requires techniques for sharing and search of access-controlled information through largely untrusted servers. In these settings search systems need to provide confidentiality guarantees for shared information while offering IR properties comparable to the ordinary search engines. Top-k is a standard IR technique which enables fast query execution on very large indexes and makes systems highly scalable. However, indexing access-controlled information for top-k retrieval is a challenging task due to the sensitivity of the term statistics used for ranking.
In this paper we present Zerber+R -- a ranking model which allows for privacy-preserving top-k retrieval from an outsourced inverted index. We propose a relevance score transformation function which makes relevance scores of different terms indistinguishable, such that even if stored on an untrusted server they do not reveal information about the indexed data. Experiments on two real-world data sets show that Zerber+R makes economical usage of bandwidth and offers retrieval properties comparable with an ordinary inverted index.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org
Preference Search Service - Specification and Implementation
While the growing number of learning resources increases the
choice for learners, it also makes it more and more difficult to
find suitable courses. Thus, improved search capabilities on
learning resource repositories are required.
In this document, we describe the implementation of our
approach for learning resource search based on preference
queries. The implementation comprises a Web Service as well
as a java package supporting the client development for the
service. This Web Service acts as one part of the
TENCompetence Personalization Services developed in WP7.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org
In dieser Arbeit untersuchen wir die Ableitung
Gegensatz zu numerischen oder Score-basierten Präferenzen, definieren attribut-basierte Präferenzen dabei nicht immer eine totale Ordnung auf den Datenbankobjekten. Sie basieren typischerweise lediglich auf partiellen Ordnungen, die der Benutzer angibt. In solchen Ordnung sind daher viele Objekte unvergleichbar, was die durchschnittliche Größe der Skyline signifikant anwachsen lässt und die Effizienz ihrer Berechnung deutlich verbessert
Exploiting Indifference for Customization of Partial Order Skylines
Unlike numerical preferences, preferences on attribute values do not show an inherent total order, but skyline computation has to rely on partial orderings explicitly stated by the user. In such orders many object values are incomparable, hence skylines sizes become unpractical. However, the Pareto semantics can be modified to benefit from indifferences: skyline result sizes can be essentially reduced by allowing the user to declare some incomparable values as equally desirable. A major problem of adding such equivalences is that they may result in intransitivity of the aggregated Pareto order and thus efficient query processing is hampered. In this paper we analyze how far the strict Pareto semantics can be relaxed while always retaining transitivity of the induced Pareto aggregation. Extensive practical tests show that skyline sizes can indeed be reduced about two orders of magnitude when using the maximum possible relaxation still guaranteeing the consistency with all user preferences
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