Session Analysis using Plan Recognition
  This paper presents preliminary results of our work with a major financial
company, where we try to use methods of plan recognition in order to
investigate the interactions of a costumer with the company's online interface.
In this paper, we present the first steps of integrating a plan recognition
algorithm in a real-world application for detecting and analyzing the
interactions of a costumer. It uses a novel approach for plan recognition from
bare-bone UI data, which reasons about the plan library at the lowest
recognition level in order to define the relevancy of actions in our domain,
and then uses it to perform plan recognition.
  We present preliminary results of inference on three different use-cases
modeled by domain experts from the company, and show that this approach manages
to decrease the overload of information required from an analyst to evaluate a
costumer's session - whether this is a malicious or benign session, whether the
intended tasks were completed, and if not - what actions are expected next.
