Data Warehousing and OLAP Technology for Data Mining?

Data Warehousing and OLAP Technology for Data Mining?

1) What is a data warehouse?

1)Defined in many different ways, but not rigorously.

2)A decision support database that is maintained separately from the organization’s operational database.

3)Support information processing by providing a solid platform of consolidated, historical data for analysis.

4)A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process.”—W. H. Inmon



Data Warehouse—Subject-Oriented


1) Organized around major subjects, such as customer, product, sales.

2) Focusing on the modeling and analysis of data for decision makers, not on daily operations or transaction processing.

3) Provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process.



Data Warehouse—Integrated

1) Constructed by integrating multiple, heterogeneous data sources

2) relational databases, flat files, on-line transaction records

3) Data cleaning and data integration techniques are applied.

4) Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources

E.g., Hotel price: currency, tax, breakfast covered, etc.

5) When data is moved to the warehouse, it is converted.

Data Warehouse—Time Variant

1) The time horizon for the data warehouse is significantly longer than that of operational systems.

2) Operational database: current value data.

3) Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years)

4) Every key structure in the data warehouse

5) Contains an element of time, explicitly or implicitly

6) But the key of operational data may or may not contain “time element”.



Data Warehouse—Non-Volatile


1) A physically separate store of data transformed from the operational environment.

2) Operational update of data does not occur in the data warehouse environment.

a) Does not require transaction processing, recovery, and concurrency control mechanisms

b) Requires only two operations in data accessing:
initial loading of data and access of data.


Data Warehouse vs. Operational DBMS


1)OLTP (on-line transaction processing)

a) Major task of traditional relational DBMS

b) Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc.

2) OLAP (on-line analytical processing)


Why Separate Data Warehouse?


1) High performance for both systems

a) DBMS— tuned for OLTP: access methods, indexing, concurrency control, recovery

b) Warehouse—tuned for OLAP: complex OLAP queries, multidimensional view, consolidation.

2) Different functions and different data:

a) missing data: Decision support requires historical data which operational DBs do not typically maintain

b) data consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources

c) data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled

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2) A multi-dimensional data model

From Tables and Spreadsheets to Data Cubes

1) A data warehouse is based on a multidimensional data model which views data in the form of a data cube

2) A data cube, such as sales, allows data to be modeled and viewed in multiple dimensions

a) Dimension tables, such as item (item_name, brand, type), or time(day, week, month, quarter, year)

b) Fact table contains measures (such as dollars_sold) and keys to each of the related dimension tables


a) Major task of data warehouse system

b) Data analysis and decision making



Typical OLAP Operations


1) Roll up (drill-up): summarize data

a) by climbing up hierarchy or by dimension reduction

2) Drill down (roll down): reverse of roll-up

a) from higher level summary to lower level summary or detailed data, or introducing new dimensions

3) Slice and dice:

a) project and select

4) Pivot (rotate):

a) reorient the cube, visualization, 3D to series of 2D planes.

5) Other operations

a) drill across: involving (across) more than one fact table

b) drill through: through the bottom level of the cube to its back-end relational tables (using SQL)