Design of Data Warehouse: A Business Analysis Framework

  • Four views regarding the design of a data warehouse
    • Top-down view
      • allows selection of the relevant information necessary for the data warehouse
    • Data source view
      • exposes the information being captured, stored, and managed by operational systems
    • Data warehouse view
      • consists of fact tables and dimension tables
    • Business query view
      • sees the perspectives of data in the warehouse from the view of end-user

Data Warehouse Design Process

  • Top-down, bottom-up approaches or a combination of both
    • Top-down: Starts with overall design and planning (mature)
    • Bottom-up: Starts with experiments and prototypes (rapid)
  • From software engineering point of view
    • Waterfall: structured and systematic analysis at each step before proceeding to the next
    • Spiral: rapid generation of increasingly functional systems, short turn around time, quick turn around
  • Typical data warehouse design process
    • Choose a business process to model, e.g., orders, invoices, etc.
    • Choose the grain (atomic level of data) of the business process
    • Choose the dimensions that will apply to each fact table record
    • Choose the measure that will populate each fact table record

Data Warehouse Development: A Recommended Approach

Data Warehouse Usage

  • Three kinds of data warehouse applications
    • Information processing
      • supports querying, basic statistical analysis, and reporting using crosstabs, tables, charts and graphs
    • Analytical processing
      • multidimensional analysis of data warehouse data
      • supports basic OLAP operations, slice-dice, drilling, pivoting
    • Data mining
      • knowledge discovery from hidden patterns
      • supports associations, constructing analytical models, performing classification and prediction, and presenting the mining results using visualization tools

From On-Line Analytical Processing (OLAP) to On Line Analytical Mining (OLAM)

  • Why online analytical mining?
    • High quality of data in data warehouses
      • DW contains integrated, consistent, cleaned data
    • Available information processing structure surrounding data warehouses
      • ODBC, OLEDB, Web accessing, service facilities, reporting and OLAP tools
    • OLAP-based exploratory data analysis
      • Mining with drilling, dicing, pivoting, etc.
    • On-line selection of data mining functions
      • Integration and swapping of multiple mining functions, algorithms, and tasks

Creator: sidraaslam


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