Data analysts today have at their disposal a seemingly endless supply of data and repositories (datasets) from which to draw. New datasets become available daily thus making the choice of which dataset to use difficult. Furthermore, traditional data analysis has been conducted using structured data repositories such as relational database management systems (RDBMS). These systems prohibit duplication for indexed collections forcing analysts to choose one value for each of the available attributes for an item in the collection. These variances, or versions of attribute values, contribute meaning to the evolution and analysis of the entity and its relationship to other entities. A new, distinct storage and retrieval mechanism will enable analysts to efficiently store, analyze, and retrieve the attribute versions without unnecessary complexity or additional alterations of the original or derived dataset schemas. This work presents technologies and innovations that assist data analysts in discovering meaning within their data while preserving all of the original data for every entity in the RDBMS.

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