  For light curve generation, a pre-planned photometry survey is needed
nowadays, where all of the exposure coordinates have to be given and don't
change during the survey. This thesis shows it is not required and we can
data-mine these light curves from astronomical data that was never meant for
this purpose. With this approach, we can recycle all of the photometric surveys
in the world and generate light curves of observed objects for them.
  This thesis is addressing mostly the catalog generation process, which is
needed for creating the light curves. In practice, it focuses on one of the
most important problems in astroinformatics which is clustering data volumes on
Big Data scale where most of the traditional techniques stagger. We consider a
wide variety of possible solutions from the view of performance, scalability,
distributability, etc. We defined criteria for time and memory complexity which
we evaluated for all of the tested solutions. Furthermore, we created quality
standards which we also take into account when evaluating the results.
  We are using relational databases as a starting point of our implementation
and compare them with the newest technologies potentially usable for solving
our problem. These are noSQL Array databases or transferring the heavy
computations of clustering towards supercomputers by using parallelism.
