Parallel Algorithms for a Visual Text Mining Platform

M.O. Almeida, R.F. Mello, and M.C.F. Oliveira (Brazil)


Visual text mining, visualization, parallel algorithms, scal ability, visual mining platform.


Current visual text mining platforms are still focused on small or medium-scale datasets and sequential algorithms. However, as document collections increase in size and complexity, more computing resources are required in or der to achieve the expected interactive experience. In order to address the scalability problem, this paper proposes and evaluates parallel implementations for three critical visual text mining algorithms. Experiments with the parallel solu tions were conducted for varying dataset sizes and different numbers of processors. The results show a good speedup for the proposed solutions and indicate the potential ben efits of exploring task parallelism in critical algorithms to improve scalability of an interactive visual text mining plat form.

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