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Lab3

CPIT 440 Data Mining and Warehouse. Lab3. CPIT 440 Data Mining and Warehouse. Lab3: Outlines . Introduction to Data Warehouse What is Data Warehouse ? Difference between Data Warehouse and Database Introduction to OLAP operations Introduction to cubes Cube structure OLAP Operations

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Lab3

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  1. CPIT 440 Data Mining and Warehouse Lab3

  2. CPIT 440 Data Mining and Warehouse Lab3: Outlines • Introduction to Data Warehouse • What is Data Warehouse ? • Difference between Data Warehouse and Database • Introduction to OLAP operations • Introduction to cubes • Cube structure • OLAP Operations • Exercises

  3. CPIT 440 Data Mining and Warehouse Data Warehouse • What is Data Warehouse ? • A data warehouse is a repository of an organization's stored data that is designed for query and analysis rather than for transaction processing to facilitate reporting and analysis. • It usually contains historical data derived from transaction data, but it can include data from other sources. • It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.

  4. CPIT 440 Data Mining and Warehouse Data Warehouse

  5. CPIT 440 Data Mining and Warehouse Difference between Data Warehouse and Database • A question we often asks out in the field is: I already have a database, so why do I need a data warehouse ? What is the difference between a database vs. a data warehouse?

  6. CPIT 440 Data Mining and Warehouse Introduction to OLAP Operations • Introduction to cubes: • A cube is a set of data that is usually constructed from a subset of a data warehouse and is organized and summarized into a multidimensional structure defined by a set of dimensions and measures. • Cubes are the main objects in online analytic processing (OLAP), • It is a technology that provides fast access to data in a data warehouse.

  7. CPIT 440 Data Mining and Warehouse Introduction to OLAP Operations

  8. CPIT 440 Data Mining and Warehouse Introduction to OLAP Operations • Cube Structure: • Every cube has a schema, which is the set of joined tables in the data warehouse from which the cube draws its source data. • The central table in the schema is the fact table, the source of the cube's measures. • The other tables are dimension tables, the sources of the cube's dimensions.

  9. CPIT 440 Data Mining and Warehouse Introduction to OLAP Operations • Cube Structure • A cube's structure is defined by its measures and dimensions. • They are derived from tables in the cube's data source. • The set of tables from which a cube's measures and dimensions are derived is called the cube's schema. • Every cube schema consists of a single fact table and one or more dimension tables. • The cube's measures are derived from columns in the fact table. • The cube's dimensions are derived from columns in the dimension tables.

  10. CPIT 440 Data Mining and Warehouse Introduction to OLAP Operations • Cube Structure • Star schema: A fact table in the middle connected to a set of dimension tables • Snowflake schema: A refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape similar to snowflake • Fact constellations: Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy schema or fact constellation

  11. CPIT 440 Data Mining and Warehouse Introduction to OLAP Operations • OLAP Operations: • Roll up: summarize data / dimension reduction • Roll down: reverse of roll-up • Make detailed data, or introducing new dimensions • Slice and dice • Pivot (rotate)

  12. CPIT 440 Data Mining and Warehouse Roll up and Roll down

  13. CPIT 440 Data Mining and Warehouse Slice and Dice

  14. CPIT 440 Data Mining and Warehouse Pivot (Rotate)

  15. CPIT 440 Data Mining and Warehouse Exercise 1 • Suppose that a data warehouse consists of the three dimensions: time, doctor, and patient, and the two measures count and charge, where charge is the fee that a doctor charges a patient for a visit.

  16. CPIT 440 Data Mining and Warehouse Exercise 1 (a) Enumerate three classes of schemas that are popularly used for modeling data warehouses. • Three classes of schemas popularly used for modeling data warehouses are • The star schema, • The snowflake schema • The fact constellations schema.

  17. CPIT 440 Data Mining and Warehouse Exercise 1 (b) Draw a schema diagram for the above data warehouse using one of the schema classes listed in part (a).

  18. CPIT 440 Data Mining and Warehouse Exercise 1 (c) Starting with the base cuboid [day; doctor; patient], what specific OLAP operations should be performed in order to list the total fee collected by each doctor in 2004? • The operations to be performed are: • Roll-up on time from day to year. • Slice for time=2004. • Roll-up on patient from individual patient to all.

  19. CPIT 440 Data Mining and Warehouse Exercise 2 • Suppose that a data warehouse for Big University consists of the following four dimensions: student, course, semester, and instructor, and two measures count and avg. grade. • When at the lowest conceptual level (e.g.,for a given student, course, semester, and instructor combination), the avg. grade measure stores the actual course grade of the student. • At higher conceptual levels, avg. grade stores the average grade for the given combination.

  20. CPIT 440 Data Mining and Warehouse Exercise 2 (a) Draw a snowflake schema diagram for the data warehouse.

  21. CPIT 440 Data Mining and Warehouse Exercise 2 (b) Starting with the base cuboid [student; course; semester; instructor], what specific OLAP operations should perform in order to list the average grade of CS courses for each Big University student. • The specific OLAP operations to be performed are: • Roll-up on course from course id to department. • Roll-up on student from student id to university. • Dice on course, student with department=\CS" and university = \Big University". • Drill-down on student from university to student name.

  22. CPIT 440 Data Mining and Warehouse Exercise 3 • Suppose that a data warehouse consists of the four dimensions; date, spectator, location, and game, and the two measures, count and charge, where charge is the fee that a spectator pays when watching a game on a given date. • Spectators may be students, adults, or seniors, with each category having its own charge rate.

  23. CPIT 440 Data Mining and Warehouse Exercise 3 (a) Draw a star schema diagram for the data warehouse.

  24. CPIT 440 Data Mining and Warehouse Exercise 3 (b) Starting with the base cuboid [date; spectator; location; game], what specific OLAP operations should perform in order to list the total charge paid by student spectators at GM Place in 2004? • The specific OLAP operations to be performed are: • Roll-up on date from date id to year. • Roll-up on spectator from spectator id to status. • Roll-up on location from location id to location name. • Roll-up on game from game id to all. • Dice with status=\students", location name=\GM Place", and year=2004.

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