  The central detectors of the ALICE experiment at LHC will produce a data size
of up to 75 MByte/event at an event rate <200 Hz resulting in a data rate of
\~15 GByte/sec. This exceeds the foreseen mass storage bandwidth of 1.25
GByte/sec by one order of magnitude. Online processing of the data is necessary
in order to select interesting (sub)events ("High Level Trigger"), or to
compress data efficiently by modeling techniques.
  The largest computing challenge is imposed by the TPC, requiring realtime
pattern recognition. The work carried out investigates the performance of
various pattern recognition schemes applicable for online track reconstruction
within the ALICE HLT system. Furthermore, as an application for reducing the
data rate, very efficient TPC data compression algorithms based on track and
cluster modeling have been evaluated.
