  How to analyse Terabytes of photometric data, and extract knowledge on
variable stars? How to detect variable phenomena? How to combine different
photometric bands? Which algorithm to search for periods? How to characterize
and classify the detected variable objects? Many questions, but certainly no
definitive answers yet. We present several aspects which are at the interface
of photometric surveys and variable stars. Fully automated analyses of
photometric surveys are still not at an optimized level. We will take the
example of a future survey, the Gaia mission project of the European Space
Agency, to show different steps of a possible automated pipeline scheme.
Principal component analysis can be applied to the Gaia photometric bands. We
give some illustrative examples of classification methods such as Support
Vector Machine, Self-Organising Map, or Bayesian classifier.
