what is data mining in data warehouse

              what is data mining in data warehouse

In this article today learn what is data mining in data warehouse and Data warehouse Design Process Data Warehouse Definition.

Data Warehouse:

A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision-making process.

What is Data Warehouse:

Data warehousing is the process of constructing and using a data warehouse. A data warehouse is constructed by integrating data warehouse from multiple heterogeneous sources that support analytical reporting, structured and/or ad hoc queries, and decision making.  in Data warehousing involves data cleaning, data integration, and data consolidations.

Subject Oriented

   A data warehouse can be used to analyze a particular subject area. For example, "Sales" can be a particular subject.

Integrated:

   A data warehouse integrates data from multiple data sources. For example, source A and source 11 may have different ways of identifying a product, but in a data warehouse, there will be only a single way of identifying a product.of the data mining in data warehouse

Time-Variant:

   Historical data is kept in a data warehouse. For example, one can retrieve data from 3 months, 6 months, 12 months, or even older data from a data warehouse. This contrasts with a transaction system, where often only the most recent data is kept. For example, a transaction system may hold the most recent address of a customer, where a data warehouse can hold all addresses associated with a customer. in the data mining in data warehouse

Non-volatile:

    Once data is in the data warehouse, it will not change. So, historical data in a data warehouse should never be altered.

Data Warehouse Design Process:

A data warehouse can be built using a top-down approach, a bottom-up approach, or a combination of both

The top-down approach starts with the overall design and planning. from data mining in data warehouse, It is useful in cases where the technology is mature and well known, and where the business problems that must be solved are clear and well understood.

The bottom-up approach starts with experiments and prototypes. This is useful in the early stage of business modeling and technology development. It allows an organization to move forward at considerably less expense and to evaluate the benefits of the technology before making significant commitments, from the data mining in data warehouse.

In the combined approach, an organization can exploit the planned and strategic nature of the top-down approach while retaining the rapid implementation and opportunistic application of the bottom-up approach

ments, inventory, account administration, sales, or the general ledger. If the business process is organizational and involves multiple complex object collections, a data warehouse model should be followed. However, if the process is departmental and focuses on the analysis of one kind of business process, a data mart model should be chosen

The warehouse design process consists of the following steps:

Choose a business process to model, for example, orders, invoices, ship. Choose the grain of the business process. The grain is the fundamental, atomic level of data to be represented in the fact table for this process, for example, individual transactions, individual daily snapshots, and so on.

  Choose the dimensions that will apply to each fact table record. Typical dimensions are time, item, customer, supplier, warehouse, transaction type, and status.

Choose the measures that will populate each fact table record. Typical measures are numeric additive quantities like dollars sold and units sold.