Mining interesting events on large and dynamic data

databases that are updated through continuous insertion queries.

In social networks, users post text, images or videos and annotate each of them with a set of tags describing its content. We define sets of co-occurring tags to represent topics and track the correlations of co-occurring tags over time. We split the tags to multiple nodes and make each node responsible of computing the correlations of its assigned tags. We implemented our approach in Storm, a distributed processing engine, and conducted a user study to estimate the quality of our results.

In structured data stored in relational databases, top-k group-by queries are defined and an emerging topic is considered to be a change in the top-k results. We maintain the top-k result sets in the presence of updates minimizing the interaction with the underlying database. We implemented and experimentally tested our approach.