  We have performed an in-depth concept study of a gravitational wave data
analysis method which targets repeated long quasi-monochromatic transients
(triggers) from cosmic sources. The algorithm concept can be applied to
multi-trigger data sets in which the detector-source orientation and the
statistical properties of the data stream change with time, and does not
require the assumption that the data is Gaussian. Reconstructing or limiting
the energetics of potential gravitational wave emissions associated with
quasi-periodic oscillations (QPOs) observed in the X-ray lightcurve tails of
soft gamma repeater flares might be an interesting endeavour of the future.
Therefore we chose this in a simplified form to illustrate the flow,
capabilities, and performance of the method. We investigate performance aspects
of a multi-trigger based data analysis approach by using O(100 s) long
stretches of mock data in coincidence with the times of observed QPOs, and by
using the known sky location of the source. We analytically derive the PDF of
the background distribution and compare to the results obtained by applying the
concept to simulated Gaussian noise, as well as off-source playground data
collected by the 4-km Hanford detector (H1) during LIGO's fifth science run
(S5). We show that the transient glitch rejection and adaptive differential
energy comparison methods we apply succeed in rejecting outliers in the S5
background data. Finally, we discuss how to extend the method to a network
containing multiple detectors, and as an example, tune the method to maximize
sensitivity to SGR 1806-20 flare times.
