  Tensor decompositions are powerful tools for large data analytics as they
jointly model multiple aspects of data into one framework and enable the
discovery of the latent structures and higher-order correlations within the
data. One of the most widely studied and used decompositions, especially in
data mining and machine learning, is the Canonical Polyadic or CP
decomposition. However, today's datasets are not static and these datasets
often dynamically growing and changing with time. To operate on such large
data, we present OCTen the first ever compression-based online parallel
implementation for the CP decomposition. We conduct an extensive empirical
analysis of the algorithms in terms of fitness, memory used and CPU time, and
in order to demonstrate the compression and scalability of the method, we apply
OCTen to big tensor data. Indicatively, OCTen performs on-par or better than
state-of-the-art online and online methods in terms of decomposition accuracy
and efficiency, while saving up to 40-200 % memory space.
