Editing Granular computing (section)

=== System granulation (aggregation) ===

In [[database systems]], aggregations (see e.g. [[OLAP|OLAP aggregation]] and [[Business intelligence]] systems) result in transforming original data tables (often called information systems) into the tables with different semantics of rows and columns, wherein the rows correspond to the groups (granules) of original tuples and the columns express aggregated information about original values within each of the groups. Such aggregations are usually based on SQL and its extensions. The resulting granules usually correspond to the groups of original tuples with the same values (or ranges) over some pre-selected original columns.

There are also other approaches wherein the groups are defined basing on, e.g., physical adjacency of rows. For example, [[Infobright]] implemented a database engine wherein data was partitioned onto ''rough rows'', each consisting of 64K of physically consecutive (or almost consecutive) rows. Rough rows were automatically labeled with compact information about their values on data columns, often involving multi-column and multi-table relationships. It resulted in a higher layer of granulated information where objects corresponded to rough rows and attributes - to various aspects of rough information. Database operations could be efficiently supported within such a new framework, with an access to the original data pieces still available {{Harv|Slezak|Synak|Wojna|Wroblewski|2013}}.