Abstract: Comments are flexibly bound to keyfigures of an in-memory database, through reference to dimension dependency table(s). An in-memory database engine creates a comment bound to a first tuple via a first comment table. The first tuple comprises a first dimension (e.g., Product). A user then requests a second tuple comprising a second dimension (e.g., Product Group) related to the first dimension. In response, the in-memory database engine references a dimension dependency table to determine dimension dependency information. Based upon that information, the engine transforms the comment to also be bound to the second tuple via a second comment table. The second tuple is then returned to the user together with the comment, even though the second tuple may not explicitly share dimensions with the first tuple. Embodiments may leverage the processing power and data proximity of the in-memory database engine, to efficiently transform comments by executing aggregation (e.g., join) operations.

Abstract: Comments are flexibly bound to keyfigures of an in-memory database, through reference to dimension dependency table(s). An in-memory database engine creates a comment bound to a first tuple via a first comment table. The first tuple comprises a first dimension (e.g., Product). A user then requests a second tuple comprising a second dimension (e.g., Product Group) related to the first dimension. In response, the in-memory database engine references a dimension dependency table to determine dimension dependency information. Based upon that information, the engine transforms the comment to also be bound to the second tuple via a second comment table. The second tuple is then returned to the user together with the comment, even though the second tuple may not explicitly share dimensions with the first tuple. Embodiments may leverage the processing power and data proximity of the in-memory database engine, to efficiently transform comments by executing aggregation (e.g., join) operations.