Distributed and Parallel Databases - Special issue: Research topics in distributed and parallel databases
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Maintenance of data cubes and summary tables in a warehouse
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Active Database Systems: Triggers and Rules for Advanced Database Processing
Active Database Systems: Triggers and Rules for Advanced Database Processing
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Lazy ETL in action: ETL technology dates scientific data
Proceedings of the VLDB Endowment
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In OLAP models, or data cubes, aggregates have to be recalculated when the underlying base data changes. This may cause performance problems in real-time OLAP systems, which continuously accommodate huge amounts of measurement data. To optimize the aggregate computations, a new consistency criterion called the tolerance invariant is proposed. Lazy aggregates are aggregates that are recalculated only when the tolerance invariant is violated, i.e., the error of the previously calculated aggregate exceeds the given tolerance. An industrial case study is presented. The prototype implementation is described, together with the performance results.