MINING TIMED SEQUENCES TO FIND SIGNATURES

摘要

We introduce the problem of mining sequential patterns among timed messages in large database of sequences using a Stochastic Approach. An example of patterns we are interested in is: 50% of cases of engine stops in the car are happened between 0 and 2 minutes after observing a lack of the gas in the engine, produced between 0 and 1 minutes after the fuel tank is empty. We call this patterns "signatures". Previous research have considered some equivalent patterns, but such work have three mains problems: (1) the sensibility of their algorithms with the value of their parameters, (2) too large number of discovered patterns, and (3) their discovered patterns consider only "after" relation (succession in time) and omit temporal constraints between elements in patterns. To address this issue, we present TOM4L process (Timed Observations Mining for Learning process) which uses a stochastic representation of a given set of sequences on which an inductive reasoning coupled with an abductive reasoning is applied to reduce the space search. A very simple example is used to show the efficiency of the TOM4L process against others literature approaches.