The entity-relationship model—toward a unified view of data
ACM Transactions on Database Systems (TODS) - Special issue: papers from the international conference on very large data bases: September 22–24, 1975, Framingham, MA
On Normalization of Relations in Relational Databases
Programming and Computing Software
IEEE Transactions on Knowledge and Data Engineering
Temporal Entity-Relationship Models-A Survey
IEEE Transactions on Knowledge and Data Engineering
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Exploiting functional dependence in query optimization
Exploiting functional dependence in query optimization
C-store: a column-oriented DBMS
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Schema versioning in data warehouses: enabling cross-version querying via schema augmentation
Data & Knowledge Engineering - Special issue: WIDM 2004
ER '09 Proceedings of the 28th International Conference on Conceptual Modeling
What time is it in the data warehouse?
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
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Maintaining and evolving data warehouses is a complex, error prone, and time consuming activity. The main reason for this state of affairs is that the environment of a data warehouse is in constant change, while the warehouse itself needs to provide a stable and consistent interface to information spanning extended periods of time. In this article, we propose an agile information modeling technique, called Anchor Modeling, that offers non-destructive extensibility mechanisms, thereby enabling robust and flexible management of changes. A key benefit of Anchor Modeling is that changes in a data warehouse environment only require extensions, not modifications, to the data warehouse. Such changes, therefore, do not require immediate modifications of existing applications, since all previous versions of the database schema are available as subsets of the current schema. Anchor Modeling decouples the evolution and application of a database, which when building a data warehouse enables shrinking of the initial project scope. While data models were previously made to capture every facet of a domain in a single phase of development, in Anchor Modeling fragments can be iteratively modeled and applied. We provide a formal and technology independent definition of anchor models and show how anchor models can be realized as relational databases together with examples of schema evolution. We also investigate performance through a number of lab experiments, which indicate that under certain conditions anchor databases perform substantially better than databases constructed using traditional modeling techniques.