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Efficient Event Detection for the Blogosphere

Authors: Patrick Hennig; Philipp Berger 0001; Daniel Kurzynski; Hannes Rantzsch; Christoph Meinel;

Efficient Event Detection for the Blogosphere

Abstract

In this paper we come up with a novel approach for the early detection of events in blog entries. The detection of trend is already discussed pretty often. Nevertheless, in our understanding the detection of events goes one step further. The presented algorithms detects unique happenings at a given point in time by perceiving unusual frequent occurrences of words or word groups. We introduce an implementation of our algorithm, making use of the SAP HANA database in order to achieve high performance and the ability to answer live queries for events.

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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