Reverse Data Management
Database research mainly focuses on forward-moving data flows: source data is subjected to transformations and evolves through queries, aggregations, and view definitions to form a new target instance, possibly with a different schema. This Forward Paradigm underpins most data management tasks today, such as querying, data integration, data mining, etc. The authors contrast this forward processing with Reverse Data Management (RDM), where the action needs to be performed on the input data, on behalf of desired outcomes in the output data. Some data management tasks already fall under this paradigm, for example updates through views, data generation, data cleaning and repair. RDM is, by necessity, conceptually more difficult to define, and computationally harder to achieve.