A granule is any atomic element that is not distinguishable from its peers for manifest features but only for the fact that it represents a singleton (eventually overarching a subset of elements) among other singletons. The importance of granule in Computational Intelligence (CI) is testified by the recent development of Granular Computing (GrC) whose aim is to provide computational methodologies and tools to properly handle information processing at different granularity levels. One important aspect, sometimes dismissed by mainstream research in GrC, is the way interpretations are hidden in observational data at multiple granule scales. It is often the case, in fact, that certain patterns showing coarse statistical evidence at a given observation level have a number of well-defined rules of interpretation at a finer granule level. Currently available CI tools seem to lack on this point. This work reports on the experience gained in developing a CI tool for data analysis named H-GIS (Holonic-Granularity Inference System). The tool is specifically conceived to focus on measurement data interpretation at multiple granularity scales by employing the modeling framework of the so-called holonic systems.
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