A learning context memory consisting of two main parts is
presented. The first part performs lossy data compression,
keeping the amount of stored data at a minimum by combining
similar context attributes — the compression rate for the
presented GPS data is 150:1 on average. The resulting data is
stored in an appropriate data structure highlighting the level
of compression. Elements with a high level of compression
are used in the second part to form the start and end points
of episodes capturing common activity consisting of consecutive
events. The context memory is used to investigate how
little context data can be stored containing still enough information
to capture regular human activity
