  Web 2.0 applications like Twitter or Facebook create a continuous stream of
information. This demands new ways of analysis in order to offer insight into
this stream right at the moment of the creation of the information, because
lots of this data is only relevant within a short period of time. To address
this problem real time search engines have recently received increased
attention. They take into account the continuous flow of information
differently than traditional web search by incorporating temporal and social
features, that describe the context of the information during its creation.
Standard approaches where data first get stored and then is processed from a
peristent storage suffer from latency. We want to address the fluent and rapid
nature of text stream by providing an event based approach that analyses
directly the stream of information. In a first step we want to define the
difference between real time search and traditional search to clarify the
demands in modern text filtering. In a second step we want to show how event
based features can be used to support the tasks of real time search engines.
Using the example of Twitter we present in this paper a way how to combine an
event based approach with text mining and information filtering concepts in
order to classify incoming information based on stream features. We calculate
stream dependant features and feed them into a neural network in order to
classify the text streams. We show the separative capabilities of event based
features as the foundation for a real time search engine.
