This thesis investigates the automated near real time science analysis performed at the INTEGRAL Science Data Centre. The structure of the Quick-Look Analysis pipeline and individual analysis stages are detailed. The stage performing pattern recognition for two-dimensional coordinate lists, i.e. source identification, is tested in-depth. The lists contain sources located in a randomly selected 9ÌŠ by 9ÌŠ area of the sky. Using the current live version and default parameters; a simulated new source was correctly identified 98% of the time, fields with no new sources produced false detections 8% of the time. The testing reveals two separate flaws; a code error and a methodological error. The sensitivity of recognizing that a new source has been detected is reduced because of the code error. The methodological error causes the algorithm to report the detection of previously unknown sources where none exists. A possible solution is presented. New source detection was improved to well above 99% and false detections reduced below 2% with the new solution. A second methodological error causes the algorithm used to correct for the pointing error of the instrument to produce unreliable results. Fortuitously this problem is serious only for small pointing errors where the source matching algorithm is able to compensate for it.
