Continuous Top-k Queries over Real-Time Web Streams

Abstract : 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 pub-lish/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.
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Pré-publication, Document de travail
2016
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Nelly Vouzoukidou, Bernd Amann, Vassilis Christophides. Continuous Top-k Queries over Real-Time Web Streams. 2016. 〈hal-01411893〉

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