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An in-depth exploration of data warehousing concepts, focusing on dimensional modeling, star schema, snowflake schema, fact constellations, and cube aggregation. It covers the concepts of fact tables, dimension tables, measures, and dimension hierarchies, as well as the use of operators and the 'having' clause for aggregation.
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Conceptual Modeling of Data
Warehouses
orderId date custId prodId storeId qty amt
customer custId name address city
product prodId name price
store storeId city
store storeId cityId tId mgr s5 sfo t1 joe s7 sfo t2 fred s9 la t1 nancy
city cityId pop regId sfo 1M north la 5M south
region regId name north cold region south warm region
sType tId size location t1 small downtown t2 large suburbs
sale prodId storeId amt p1 c1 12 p2 c1 11 p1 c3 50 p2 c2 8
c1 c2 c p1 12 50 p2 11 8
sale prodId storeId date amt p1 c1 1 12 p2 c1 1 11 p1 c3 1 50 p2 c2 1 8 p1 c1 2 44 p1 c2 2 4
sale prodId storeId date amt p1 c1 1 12 p2 c1 1 11 p1 c3 1 50 p2 c2 1 8 p1 c1 2 44 p1 c2 2 4
day 2 c1 c2 c p1 44 4 p2 c1 c2 c p1 12 50 p2 11 8
day 1
c1 c2 c p1 56 4 50 p2 11 8
c1 c2 c3 * p1 56 4 50 110 p2 11 8 19
day 2 c1^ c2^ c3^ * p1 44 4 48 p
c1 c2 c3 * p1 12 50 62 p2 11 8 19
day 1
Aggregation Using
Hierarchies
day 2 c1 c2 c p1 44 4 p2 c1 c2 c p1 12 50 p2 11 8
day 1
region A region B p1 56 54 p2 11 8
(customer c1 in Region A; customers c2, c3 in Region B)