| Summary: | In the year 2011 the construction of IceCube, a neutrino observatory buried in deep clear Antarctic ice, was completed. IceCube now consists of an array of 5160 digital light detection modules assembled on 86 strings, which encloses a instrumented volume of roughly 1~km$^3$ optimized for detection of neutrinos down to energies of 100~GeV. In this detector eight of these strings are arranged in a denser configuration of the low energy extension DeepCore, which pushes the neutrino energy threshold further down to 10~GeV.\\This allows probing for fluxes from various astrophysical sources. Of special interest in context of Dark Matter theories is the Sun as a potential source of energetic neutrinos. There neutrinos can be messenger particles created in annihilations of trapped Dark Matter particles (WIMPs). Searches for solar WIMPs have a tradition in IceCube and shall be continued with data recorded in the completed detector configuration (IC86). Since the detector configuration does not substantially change further, it is worthwhile to revisit, investigate and refine analysis methods developed during the construction phases and improve on them.\\Described in this thesis is the preparation work for such an improved analysis: filter and data treatment studies have been conducted during three years ensuring the quality of the experimental data stream. In parallel the simulation codes 'WimpSim' and 'WimpSim-Reader' have been improved, which provide the signal definition for solar WIMP studies. Also in an extensive investigation about event splitting and hit clustering algorithms has been conducted. This yielded an alternative event splitting and recombination approach using 'MaxDist-Splitter' and 'CoincidentSuite'. In a subsequent study it could be shown that thereby the performance was increased compared to previous solutions by up to 50\%. Also the general benefit of these alternative solutions for general data processing has been investigated, which can remedy so far unregarded problems in lowest level data ...
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