  The Web has become a large-scale real-time information system forcing us to
revise both how to effectively assess relevance of information for a user and
how to efficiently implement information retrieval and dissemination
functionality. To increase information relevance, Real-time Web applications
such as Twitter and Facebook, extend content and social-graph relevance scores
with "real-time" user generated events (e.g. re-tweets, replies, likes). To
accommodate high arrival rates of information items and user events we explore
a publish/subscribe paradigm in which we index queries and update on the fly
their results each time a new item and relevant events arrive. In this setting,
we need to process continuous top-k text queries combining both static and
dynamic scores. To the best of our knowledge, this is the first work addressing
how non-predictable, dynamic scores can be handled in a continuous top-k query
setting.
