1. A method comprising: receiving, by at least one data processor, a plurality of data files from a plurality of data sources that comprise textual content; categorizing, by the at least one data processor, the plurality of data files into a taxonomy of categories in which each category has associated sample textual content defining a concept for the category and each category is a run-length encoded collection of at least one identification corresponding to at least one of the plurality of data files, the categorizing comprising, for each category: comparing, by the at least one data processor, for each of the plurality of data files, the textual content of the data file with the sample textual content for the category; calculating, by the at least one data processor, based on the comparing and for each of the plurality of data files, a file score corresponding to the degree of similarity between the defined concept of the category and a determined concept for the data file; and generating, by the at least one data processor, the identification stored in the run-length encoded collection by at least associating, for each of the plurality of data files, the data file with the category if the file score is equal to or greater than a pre-determined minimum score for the category; and providing, by the at least one data processor, at least a portion of the data file and/or the associated file score.
3. The method of claim 1 , wherein the associating is between the data file and only one category, the category being the category generating the highest file score equal to or greater than the minimum score.