An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Proceedings of the 2nd ACM international workshop on Data warehousing and OLAP
Essential Oracle8i Data Warehousing: Designing, Building, and Managing Oracle Data Warehouses (with Website)
The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
Multiple View Consistency for Data Warehousing
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Selection of Views to Materialize Under a Maintenance Cost Constraint
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Reasoning about Summarizability in Heterogeneous Multidimensional Schemas
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Extending Practical Pre-Aggregation in On-Line Analytical Processing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Consistency in Data Warehouse Dimensions
IDEAS '02 Proceedings of the 2002 International Symposium on Database Engineering & Applications
STORM: A Statistical Object Representation Model
Proceedings of the 5th International Conference SSDBM on Statistical and Scientific Database Management
Summarizability in OLAP and Statistical Data Bases
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
Maintaining Data Cubes under Dimension Updates
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Capturing summarizability with integrity constraints in OLAP
ACM Transactions on Database Systems (TODS)
Consistent query answering in databases
ACM SIGMOD Record
Repairing inconsistent dimensions in data warehouses
Data & Knowledge Engineering
A multidimensional data model with subcategories for flexibly capturing summarizability
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Extended dimensions for cleaning and querying inconsistent data warehouses
Proceedings of the sixteenth international workshop on Data warehousing and OLAP
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On-Line Analytical Processing (OLAP) dimensions are usually modelled as a hierarchical set of categories (the dimension schema), and dimension instances. The latter consist in a set of elements for each category, and relations between these elements (denoted rollup). To guarantee summarizability, a dimension is required to be strict, that is, every element of the dimension instance must have a unique ancestor in each of its ancestor categories. In practice, elements in a dimension instance are often reclassified, meaning that their rollups are changed (e.g., if the current available information is proved to be wrong). After this operation the dimension may become non-strict. To fix this problem, we propose to compute a set of minimal r-repairs for the new non-strict dimension. Each minimal r-repair is a strict dimension that keeps the result of the reclassification, and is obtained by performing a minimum number of insertions and deletions to the instance graph. We show that, although in the general case finding an r-repair is NP-complete, for real-world dimension schemas, computing such repairs can be done in polynomial time. We present algorithms for this, and discuss their computational complexity.