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  1. EDUCAUSE Center for Applied Research (ECAR): Information Technology Alignment in Higher Education. A recent survey to discover how information technology priorities, plans, resources, intentions, and actions are aligned with the broad vision of higher education institutions.
  2. Kimball, Ralph. 1996. The Data Warehouse Toolkit. New York: John Wiley and Sons. A child’s first book of data warehousing.
  3. Kimball, Ralph. 1997. The Dimensional Modeling Manifesto. DBMS (August, 1997). An article by the father of dimensional modeling explaining its value to the reporting process.
  4. Kimball, Ralph, L. Reeves, M. Ross and W Thornthwaite. 1998. The Data Warehouse Lifecycle Toolkit. New York: John Wiley and Sons. The definitive work on how to build a data warehouse, including the principles of dimensional modeling.
  5. Allan, Ronald Gage. 2004. The Impact of the OLAP/OLTP Culture Conflict On Data Warehousing. Business Intelligence Journal (Vol 9, No 1, Winter 2004). Explanation of the difference between data warehousing's "old mindset" (OLTP) and the "new mindset" (OLAP) for building data warehouses. In particular it explains why so many data warehouse projects struggle.
  6. Allan, Ronald Gage. Bridge Tables and Grain. 2002. Journal of Data warehousing (Vol 7, No 4, Fall 2002). Definition of grain, analysis of some of its issues within star schemas developed for student record data models, and suggestions for dealing with many-to-many dimensions, including the application of bridge tables.
  7. Allan, Ronald Gage. Data Models for a Registrar’s Data Mart. 2001. Journal of Data warehousing (Vol 6, No 3, Summer 2001). Article addressing the issues associated with the modeling of student record data for use in the student record data mart (SRDM) portion of a data warehouse for a college or university.
  8. Allan, Ronald Gage. Snowflakes and Grain: Snowflaking to Meet a Design Constraint. 2000. Journal of Data warehousing (Vol 5, No 3, Summer 2000). Article explaining how to deal with conflicts between design requirements, fact table grain, and referential integrity in a star schema, and especially on how to rationalize differences between the granularity of a fact table with a periodic time component and a many-to-many dimension with an "overlapping" time stream.
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