Designing Data Marts for Data Warehouses

							  Designing Data Marts for Data
  Warehouses

ACM Transactions on software Engineering
    and Methodology, October,2001
                        Present by :L.W.Lu

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Outline
   Introduction
   Design Data Warehouse Schemas
   Conclusion




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Introduction
   This paper presents a method to
    support the identification and design of
    data marts. The method is based on
    three basic steps. The first top-down
    step makes it possible to elicit and
    consolidate user requirements and
    expectations.


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Introduction                       (conti.)


   The second bottom-up step extracts
    candidate data marts from the
    conceptual schema of the information
    system. The final step compares idel
    and candidate data marts to derive a
    collection of data marts that are
    supported by the underlying
    information system and maximally
    satisfy user requirements.
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Top-Down Phase
   Collect user requirements through
    interviews.




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Top-Down Phase   (conti.)




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    Top-Down Phase                      (conti.)


 Represent requirements as GQM goals:
(a) For each goal, fill in the goal definition,
  i.e., define the object of the study, the
  purpose, the quality focus, the viewpoint,
  and the environment.




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  Top-Down Phase                        (conti.)


(b) For each goal, detail the goal by filling in
  the abstraction sheet: define the quality
  focus, the variation factors, the baseline
  hypotheses, and the impact on baseline
  hypotheses.




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Top-Down Phase   (conti.)




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Top-Down Phase                     (conti.)


(c) Compare and assess the goals:
  identify similarities and implications
  among goals to reduce them to a
  manageable number, i.e., merge
  related goals and drop goals that are
  subsumed by others



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Top-Down Phase                     (conti.)


   Derive ideal schema fragments for each
    goal.




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Bottom-up Analysis
(Derivation of candidate star schemas from DB conceptual schemas)


        Obtain the Star Join Graphs:
       (a) Map the E/R schema into a
         connectivity graph.




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Bottom-up Analysis                    (conti.)


   (b) Run the Snowflake Graph Algorithm,
    to find all the possible snowflake graphs.




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Bottom-up Analysis   (conti.)




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Integration and Ranking
 Match the ideal schema with the star
  join graphs:
(a) Identify common terms between the
  ideal schema and the star join graphs,
  through direct/indirect mapping.
(b) Match ideal and candidate schemas,
  taking into account the matching
  attributes of the schemas, the matching
  dimensions, the additional attributes,
  and the additional dimensions .
(c) Rank the solutions.                  17/18
Conclusion
   The combination of top-down and
    bottom-up steps helps in coherently
    evaluating both the coverage of users’
    requirements and the feasibility of data
    warehouse design.




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