We have proposed a framework termed “Rough set Non-deterministic Information Analysis (RNIA),” which generates rules from tabular data and performs data analysis using the obtained rules. In RNIA, an execution environment powered by the DIS-Apriori algorithm and the NIS-Apriori algorithm has been constructed. At runtime, each table data is converted to an intermediate format file, and rules can be generated from this format file. We are now considering rule generation not only from tabular data but also from non-tabular data and are proposing rule generation from descriptor-based quotient spaces. We summarize the current functionalities of the implemented software tools and consider the possibility of generating rules using various descriptors. We will also consider generating rules that include the concepts of “fine” and “coarse” proposed in rough sets and granular calculations.