  E-science of photometric data requires automatic procedures and a precise
recognition of periodic patterns to perform science as well as possible on
large data. Analytical equations that enable us to set the best constraints to
properly reduce processing time and hence optimize signal searches play a
crucial role in this matter. These are increasingly important because the
production of unbiased samples from variability indices and statistical
parameters has not been achievable so far. We discuss the constraints used in
periodic signals detection methods as well as the uncertainties in the
estimation of periods and amplitudes. The frequency resolution necessary to
investigate a time series is assessed with a new approach that estimates the
necessary sampling resolution from shifts on the phase diagrams for successive
frequency grid points.We demonstrate the underlying meaning of the oversampling
factor. We reassess the frequency resolutions required to find the variability
periods of EA stars and use the new resolutions to analyse a small sample of EA
up Catalina stars, i.e. EA stars previously classified as having insufficient
number of observations at the eclipses. As a result, the variability periods of
four EA up stars were determined. Moreover, we have a new approach to estimate
the amplitude and period variations. From these estimations information about
the intrinsic variations of the sources are obtained. For a complete
characterization of the light curve signal the period uncertainty and period
variation must be determined. Constraints on periodic signal searches were
analysed and delimited.
