  The ALICE High Level Trigger has to process data online, in order to select
interesting (sub)events, or to compress data efficiently by modeling
techniques.Focusing on the main data source, the Time Projection Chamber (TPC),
we present two pattern recognition methods under investigation: a sequential
approach "cluster finder" and "track follower") and an iterative approach
("track candidate finder" and "cluster deconvoluter"). We show, that the former
is suited for pp and low multiplicity PbPb collisions, whereas the latter might
be applicable for high multiplicity PbPb collisions, if it turns out, that more
than 8000 charged particles would have to be reconstructed inside the TPC.
Based on the developed tracking schemes we show, that using modeling techniques
a compression factor of around 10 might be achievable
