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OLAP, OLTP and Data Mining

TitleData WarehouseDW DiagramOLTPOLAPData MiningData Mining goalData Mining Elements Data Mining ApplicationDW VS Data MiningThanks

 

Data Warehouses Repository is a key data warehouse

component Data warehouses provide access to data for

complex analysis, knowledge discovery, and decision making.

Data warehousing more generally as a collection of decision support technologies, aimed at enabling the knowledge worker (executive, manager, analyst) to make better and faster decisions.

Extract, Transform and Load Pulling data out of the source system and

placing it into a data warehouse Cleaning Filtering Splitting a column into multiple columns Joining together.  loading the data into a data warehouse

On-line Transaction Processing Use in Traditional databases Includes insertions, updates, and

deletions, while also supporting information query requirements

On-line Analytical Processing To describe the analysis of complex

data from the data warehouse ROLAP (relational OLAP) and

MOLAP (multidimensional OLAP) functions

Knowledge Discovery ProcessThe knowledge discovery process comprises

six phases Data selection, Data cleansing, Enrichment, Data transformation or encoding,

Data mining, Reporting and display of the discovered

information.

Data Mining Data Mining as a Part of the

Knowledge Discovery Process Used for knowledge discovery, the

process of searching data for the new knowledge.

Data mining consists of five major elements:

Extract, transform, and load transaction data onto the data warehouse system.

Store and manage the data in a multidimensional database system.

Provide data access to business analysts and information technology professionals.

Analyse the data by application software. Present the data in a useful format, such as a

graph or table.

Goal of Data Mining

Prediction Identification Classification(combinations of parameters) Optimization(Goal of data mining may be

to optimize the use of limited resources such as time, space, money, or materials and to maximize output variables such as sales or profits under a given set of constraints)

Applications of Data Mining

Marketing Finance Manufacturing Health Care Many people only need read-access to data, but still

need a very rapid access to a larger volume of data than can conveniently be downloaded to the desktop. Data comes from multiple databases

Such types of functionality provide:- Data warehousing, on-line analytical processing

(OLAP), and data mining

DW VS DM Data warehousing can be seen as a

process that requires a variety of activities to precede it;

Data mining may be thought as an activity that draws knowledge from an existing data warehouse.