Anchor Modeling

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 paper, we propose a modeling technique for data warehousing, called anchor modeling, that offers non-destructive extensibility mechanisms, thereby enabling robust and flexible management of changes in source systems. A key benefit of anchor modeling is that changes in a data warehouse environment only require extensions, not modifications, to the data warehouse. This ensures that existing data warehouse applications will remain unaffected by the evolution of the data warehouse, i.e. existing views and functions will not have to be modified as a result of changes in the warehouse model.

[1]  Enrico Franconi,et al.  Reasoning with enhanced temporal entity-relationship models , 1999, Proceedings. Tenth International Workshop on Database and Expert Systems Applications. DEXA 99.

[2]  Ramez Elmasri,et al.  Fundamentals of Database Systems , 1989 .

[3]  Esteban Zimányi,et al.  Temporal aggregates and temporal universal quantification in standard SQL , 2006, SGMD.

[4]  Matthias Nicola,et al.  Storage Layout and I/O Performance in Data Warehouses , 2003, DMDW.

[5]  W. H. Inmon,et al.  Building the data warehouse , 1992 .

[6]  Michael Stonebraker,et al.  C-Store: A Column-oriented DBMS , 2005, VLDB.

[7]  Johann Eder,et al.  Creation and management of versions in multiversion data warehouse , 2004, SAC '04.

[8]  Wenfei Fan,et al.  Keys with Upward Wildcards for XML , 2001, DEXA.

[9]  Terry Halpin,et al.  Information modeling and relational databases: from conceptual analysis to logical design , 2001 .

[10]  Antoni Olivé,et al.  Conceptual modeling of information systems , 2007 .

[11]  W. H. Inmon,et al.  Building the Data Warehouse,3rd Edition , 2002 .

[12]  Ralph Kimball,et al.  The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling , 1996 .

[13]  Christian S. Jensen,et al.  Temporal Entity-RelationshipModels | a Survey , 1996 .

[14]  Petia Wohed,et al.  Anchor Modeling An Agile Modeling Technique Using the Sixth Normal Form for Structurally and Temporally Evolving Data , 2009 .

[15]  Frank Wm. Tompa,et al.  Exploiting functional dependence in query optimization , 2000 .

[16]  Christian S. Jensen,et al.  Temporal Data Management , 1999, IEEE Trans. Knowl. Data Eng..

[17]  Matteo Golfarelli,et al.  What Time Is It in the Data Warehouse? , 2006, DaWaK.

[18]  Martin Fowler,et al.  Analysis patterns - reusable object models , 1996, Addison-Wesley series in object-oriented software engineering.

[19]  Peter P. Chen The entity-relationship model: toward a unified view of data , 1975, VLDB '75.

[20]  David C. Hay,et al.  Data Model Patterns: Conventions of Thought , 1965 .

[21]  E. F. Codd,et al.  Further Normalization of the Data Base Relational Model , 1971, Research Report / RJ / IBM / San Jose, California.

[22]  Gottfried Vossen,et al.  Schema versioning in data warehouses: Enabling cross-version querying via schema augmentation , 2006, Data Knowl. Eng..

[23]  Arie Segev,et al.  A consensus glossary of temporal database concepts , 1994, SIGMOD 1994.

[24]  John F. Roddick,et al.  A survey of schema versioning issues for database systems , 1995, Inf. Softw. Technol..

[25]  Timos K. Sellis,et al.  Dynamic Data Warehouse Design , 1999, DaWaK.

[26]  Ivar Jacobson,et al.  The Unified Modeling Language User Guide , 1998, J. Database Manag..

[27]  C. J. Date,et al.  Temporal data and the relational model , 2002 .

[28]  V. V. Khodorovskii On Normalization of Relations in Relational Databases , 2004, Programming and Computer Software.