Thomas Vanhove

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Online social networks (OSNs) are becoming increasingly popular every day. The vast amount of data created by users and their actions yields interesting opportunities, both socially and economically. Unfortunately, these online communities are prone to abuse and inappropriate behaviour such as cyber bullying. For victims, this kind of behaviour can lead to(More)
Data is abundantly present in today's world and the amount of data we generate continues to grow. The representation and structure of this data, however, differs greatly depending on the software or platform. The wide variety of software available shows there is no one optimal way to model data for all software, but when you want to deploy software in the(More)
Big data applications have stringent service requirements for scalability and fault-tolerance and involve high volumes of data, high processing speeds and large varieties of database technologies. In order to test big data management solutions, large experimentation facilities are needed, which are expensive in terms of both resource cost and configuration(More)
Vendor lock-in is one of the major issues preventing companies from moving their big data applications to the cloud or changing between cloud providers. A choice in provider based on used datastores can be advantageous at first, but with ever-changing applications the chosen datastore may no longer be optimal after some time. Namely, applications'(More)
The Internet of Things (IoT) is starting to take a prevalent role in our daily lives. Smart offices that automatically adapt their environment to make life at the office as pleasant as possible, are slowly becoming reality. In this paper we present a user-friendly semantic-based smart office platform that allows, through easy configuration, a personalized(More)
SUMMARY In a world of continuously expanding amounts of data, retrieving interesting information from enormous data sets becomes more complex every day. Solutions for precomputing views on these big data sets mostly follow either an offline approach, which is slow but can take into account the entire data set, or a streaming approach, which is fast but only(More)
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