DATA MINING AND DATA WAREHOUSE .doc (Abstract)

TITTLE: DATA MINING AND DATA WAREHOUSE

ABSTRACT:

Data mining refers to extraction or “mining” knowledge from large amounts of data. The term is actually a misnomer. Thus, data mining should have been more appropriately named “knowledge mining from data,” which is unfortunate somewhat long.”Knowledge mining,” a shorter term may not reflect the emphasis on mining from large amounts of data. Data warehouse, in its simplest perception, is no more than a collection of the key pieces of information used to manage and direct the business for the most profitable outcome.

This paper describes a new approach to fast multimedia information retrieval with data mining and data ware housing techniques. To tackle the key issues such as multimedia data indexing, similarity measures, search methods and query processing in retrieval for large multimedia data archives; we extend the concepts of conventional data warehouse and multimedia database to multimedia data warehouse for effective data representation and storage.

KEYWORDS:

· Retrospective

· Prospective

· Regression

· Retention

· Bespoke

· Data Dippers

· Intuitive Data

· Aggregation

· Generalization

· Data Mart

· Meta Data

CONCLUSION:

Comprehensive data warehouses that integrate operational data with customer, supplier, and market information have resulted in an explosion of information. Both relational and OLAP technologies have tremendous capabilities for navigating massive data warehouses, but brute force navigation of data is not enough. The data mining tools can make this leap.However; data warehouses are still an expensive solution and typically found in large firms. The development of central warehouse is a huge under taking and capital intensive with large, potentially unmanageable risks