CBSE Class 12 Informatics Practices Advanced Database Technologies Notes

Advanced Database Technologies

DATA WAREHOUSE AND DATA-MINING

Definition

The term data warehouse was coined with the definition of Inmon: "A warehouse is a subjectoriented, integrated, time variant and non-volatile collection of data in support of management's decision making process" . Characteristics of DW

• Subject-oriented : Means that all relevant data about a subject is gathered and stored as a single set in a useful format. Information is presented according to specific subjects or areas of interest.

• Time-variant : Means that the data warehouse contains a history of the subject, as well as current information. It may be long-term data from five to ten years in contrast to the 30 to 60 day time..

• Non-volatile : Means stable information, Information is consistent; Data in the database is never over-written or deleted once committed.

• Integrated : Stored in a globally acceptable fashion with consistent naming conventions, measurements, encoding structures, and physical attributes, though underlying operational systems store the data differently;

Data Warehouse Architecture:
• Warehouse database server: Which is almost always a relational DBMS; rarely flat files.

• OLAP servers:- Which may either be a ROLAP or MOLAP

– Relational OLAP (ROLAP): Extended relational DBMS that maps operations on multidimensional data to standard relational operations.

– Multidimensional OLAP (MOLAP): Special purpose server that directly implements
multidimensional data and operations.

• Clients:-The Users or the client of Data warehouses are various Query and reporting tools, Analysis
tools and Data mining tools (e.g., trend analysis, prediction)

Processors in Datawarehouse server
- MPP
   – Massively parallel processing, a computer configuration that is able to use hundreds or thousands of CPUs simultaneously.

– SMP
   – Symmetric multi-processing is a computer configuration where many CPUs share a common operating system, main memory and disks. They can work on different parts of a problem at the same time

Steps involved in creating a Datawarehouse
• Data extraction
• Data cleaning, also called data cleansing or scrubbing,
• Data transformation
• Convert from legacy/host format to warehouse format
• Load
• Sort, summarize, consolidate, compute views, check integrity, build indexes, partition
• Refresh
• Propagate updates from sources to the warehouse

Advantages
• Data warehouses enhance end-user access to a wide variety of data.
• Decision support system users can obtain specified trend reports, e.g. the item with the most sales in a
particular area within the last two years.
• Data warehouses can be a significant enabler of commercial business applications. 

Data Mining
• Definition
– Data mining, the extraction of hidden predictive information from large databases

– The nontrivial extraction of implicit, previously unknown, and potentially useful information from data" and "the science of extracting useful information from large data sets or databases".

• Techniques used in Data Mining: -

Artificial neural networks

Genetic algorithms

Decision trees

Nearest neighbor method

Rule induction

Evolution of Datamining

Please refer to attached file for CBSE Class 12 Informatics Practices Advanced Database Technologies Study Notes