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Dimensional modeling: identifying, classifying & applying patterns

Published: 04 November 2005 Publication History

Abstract

Software design is a complex activity. A successful designer requires knowledge and training in specific design techniques combined with practical experience. Designing a dimensional model embodies this challenge. This paper presents Dimensional Design Patterns (DDPs) and their applications to the design of dimensional models. We describe a metamodel of the DDPs and show their integration into Kimball's dimensional modeling design process so they can be applied to design problems using a known practice. By providing a metamodel and a method for DDP use, we combine theory and a practical design technique with the goal of increasing the efficiency and effectiveness of the software designer. The initial experimental results regarding the classroom use of DDPs revealed a significant increase in the efficiency of students to design a dimensional model, but more testing is necessary in order to evaluate the effectiveness measure.

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Cited By

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  • (2024)Research Landscape of Patterns in Software Engineering: Taxonomy, State-of-the-Art, and Future DirectionsSN Computer Science10.1007/s42979-024-02767-85:4Online publication date: 8-Apr-2024
  • (2016)Key Performance Indicators in Data WarehousesBusiness Intelligence10.1007/978-3-319-39243-1_5(111-129)Online publication date: 10-May-2016
  • (2013)Query-Driven Method for Improvement of Data Warehouse Conceptual ModelBuilding Sustainable Information Systems10.1007/978-1-4614-7540-8_41(531-542)Online publication date: 17-Oct-2013
  • Show More Cited By

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Reviews

Vladan Jovanovic

Practitioners and students interested in a simple method for logically designing data warehouses should read this paper. The method follows Kimball's dimensional modeling approach in addressing business processes with fact tables and shared dimensional tables. The authors abstract dimensional patterns from over 50 applications, and provide advice on selecting dimensions and attributes. The method was tested with students, and the data indicate a statistically significant increase in efficiency, measured as time to design. To describe the method in a nutshell: for each process of interest, the designers review common categories of entities patterned after traditional interrogatives (who, what-object, what-action, when, where, and why) to recognize domain-relevant dimensions, and then apply standardized questions, following the same pattern, to identify appropriate attributes for each of the discovered dimensions. One of the method's limitations is that it omits the dimensional hierarchies and roles a dimension may play in a given fact table, but this seems appropriate given that the primary goal of the method is to be useful to data warehouse designers with limited experience.

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cover image ACM Conferences
DOLAP '05: Proceedings of the 8th ACM international workshop on Data warehousing and OLAP
November 2005
122 pages
ISBN:1595931627
DOI:10.1145/1097002
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 04 November 2005

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Author Tags

  1. data warehouse
  2. dimensional modeling
  3. patterns
  4. software engineering

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Cited By

View all
  • (2024)Research Landscape of Patterns in Software Engineering: Taxonomy, State-of-the-Art, and Future DirectionsSN Computer Science10.1007/s42979-024-02767-85:4Online publication date: 8-Apr-2024
  • (2016)Key Performance Indicators in Data WarehousesBusiness Intelligence10.1007/978-3-319-39243-1_5(111-129)Online publication date: 10-May-2016
  • (2013)Query-Driven Method for Improvement of Data Warehouse Conceptual ModelBuilding Sustainable Information Systems10.1007/978-1-4614-7540-8_41(531-542)Online publication date: 17-Oct-2013
  • (2012)Archiving business operational data heritageProcedia Technology10.1016/j.protcy.2012.02.0211(107-111)Online publication date: 2012
  • (2010)OLAP Personalization with User-Describing ProfilesPerspectives in Business Informatics Research10.1007/978-3-642-16101-8_15(188-202)Online publication date: 2010
  • (2009)Multidimensional Modeling of Complex DataEncyclopedia of Data Warehousing and Mining, Second Edition10.4018/978-1-60566-010-3.ch210(1358-1364)Online publication date: 2009
  • (2007)SAMSTARProceedings of the ACM tenth international workshop on Data warehousing and OLAP10.1145/1317331.1317334(9-16)Online publication date: 9-Nov-2007
  • (2006)A report on the Eighth ACM International Workshop on Data Warehousing and OLAP (DOLAP'05)ACM SIGMOD Record10.1145/1228268.122828035:4(48-50)Online publication date: 1-Dec-2006
  • (2006)Research in data warehouse modeling and designProceedings of the 9th ACM international workshop on Data warehousing and OLAP10.1145/1183512.1183515(3-10)Online publication date: 10-Nov-2006

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