Items where author is affiliated with University of Hannover

The run-time binding of web services has been recently put forward in order to support rapid and dynamic web service compositions. With the growing number of alternative web services that provide the same functionality but differ in quality parameters, the service composition becomes a decision problem on which component services should be selected such that user’s end-to-end QoS requirements (e.g. availability, response time) and preferences (e.g. price) are satisfied. Although very efficient, local selection strategy fails short in handling global QoS requirements. Solutions based on global optimization, on the other hand, can handle global constraints, but their poor performance renders them inappropriate for applications with dynamic and realtime requirements. In this paper we address this problem and propose a solution that combines global optimization with local selection techniques to benefit from the advantages of both worlds. The proposed solution consists of two steps: first, we use mixed integer programming (MIP) to find the optimal decomposition of global QoS constraints into local constraints. Second, we use distributed local selection to find the best web services that satisfy these local constraints. The results of experimental evaluation indicate that our approach significantly outperforms existing solutions in terms of computation time while achieving close-tooptimal results.

Web 2.0 applications like Flickr, YouTube, or Del.icio.us are increasingly popular online communities for creating, editing and sharing content. The growing size of these folksonomies poses new challenges in terms of search and data mining. In this paper we introduce a novel methodology for automatically ranking and classifying photos according to their attractiveness for folksonomy members. To this end, we exploit image features known for having significant effects on the visual quality perceived by humans (e.g. sharpness and colorfulness) as well as textual meta data, in what is a multi-modal approach. Using feedback and annotations available in the Web 2.0 photo sharing system Flickr, we assign relevance values to the photos and train classification and regression models based on these relevance assignments. With the resulting machine learning models we categorize and rank photos according to their attractiveness. Applications include enhanced ranking functions for search and recommender methods for attractive content. Large scale experiments on a collection of Flickr photos demonstrate the viability of our approach.