KDD represents Knowledge Discovery in Databases. It defines the broad process of discovering knowledge in data and emphasizes the high-level applications of definite data mining techniques. It is an area of interest to researchers in several fields, such as artificial intelligence, machine learning, pattern recognition, databases, statistics, knowledge acquisition for professional systems, and data visualization.The knowledge discovery process is iterative and interactive, includes nine steps. The process is iterative at every stage, implying that transforming back to the previous actions can be required. The process has several imaginative methods in the sense that one cannot present one formula or create ... Read More

KDD represents Knowledge Discovery in Databases. It defines the broad process of discovering knowledge in data and emphasizes the high-level applications of definite data mining techniques. It is an area of interest to researchers in several fields, such as artificial intelligence, machine learning, pattern recognition, databases, statistics, knowledge acquisition for professional systems, and data visualization.The main objective of the KDD process is to extract data from information in the context of huge databases. It does this by utilizing Data Mining algorithms to recognize what is considered knowledge. The Knowledge Discovery in Databases is treated as a programmed, exploratory analysis and ... Read More

Data mining is the process of finding useful new correlations, patterns, and trends by transferring through a high amount of data saved in repositories, using pattern recognition technologies including statistical and mathematical techniques. It is the analysis of factual datasets to discover unsuspected relationships and to summarize the records in novel methods that are both logical and helpful to the data owner.It is the procedure of selection, exploration, and modeling of high quantities of information to find regularities or relations that are at first unknown to obtain clear and beneficial results for the owner of the database.Data Mining is similar ... Read More

The traditional techniques of turning data into knowledge depend on manual analysis and interpretation. For instance, in the healthcare industry, it is familiar for specialists to systematically analyze current trends and changes in healthcare data, every quarter.The specialists support a report detailing the analysis to the sponsoring healthcare organization; this report becomes the basis for future decision making and planning for healthcare management. There are several types of applications, including planetary geologists sifting through remotely sensed images of planets and asteroids, carefully situating and cataloging such geologic objects of interest as impact craters.This form of manual probing of a data ... Read More

Data Warehousing is a technique that is generally used to collect and manage data from multiple sources to provide the business a meaningful business insight. A data warehouse is specifically created for the goals of support management decisions.In simple terms, a data warehouse defines a database that is maintained independently from an organization’s operational databases. Data warehouse systems enable the integration of several application systems. They provide data processing by supporting a solid platform of consolidated, historical data for analysis.There are various types of data warehouse users which are as follows −Statisticians − There are generally only a handful of ... Read More

Data Warehouse DatabaseData Warehousing is a technique that is generally used to collect and manage data from multiple sources to provide the business a meaningful business insight. A data warehouse is specifically created for the goals of support management decisions.In simple terms, a data warehouse defines a database that is maintained independently from an organization’s operational databases. Data warehouse systems enable the integration of several application systems. They provide data processing by supporting a solid platform of consolidated, historical data for analysis.A data warehouse provides an OLTP system by supporting a place for the OLTP database to offload data as ... Read More

A data warehouse defines a database that is maintained independently from an organization’s operational databases. Data warehouse systems enable the integration of several application systems. They support data processing by supporting a solid platform of consolidated, historical records for analysis.A data warehouse can be viewed as a set of materialized views represented over remote base relations. When a query is formal, it is computed locally, using the materialized views, without accessing the initial data sources.The data warehouse is an active entity that derives continuously over time. As time passes, new queries are required to be answered by them. Various queries ... Read More

In the data warehousing process, the data staging area is collected of the data staging server software and the data store archive (repository) of the results of extraction, transformation, and loading activity.The data staging software server temporarily saves and changes data extracted from OLTP data sources and the archival repository stores cleaned, transformed data and attributes for loading into data marts and data warehouses.The data staging process imports information either as streams or files, change it, produces integrated, cleaned data, and stages it for loading into data warehouses, data marts, or Operational Data Stores.A data staging tool is accessible, and ... Read More

Data Warehousing is a method that is generally used to collect and handle data from various other sources to provide the business a meaningful business insight. A data warehouse is specifically created for the goals of support management decisions. The Data Warehouse has two main parts which are as follows −Physical store − A Microsoft SQL Server database that it can query using SQL queries, and an OLAP database that it can need to run reports.Logical schema − A conceptual model that maps to the data in the physical store.Physical StoreThe physical store for the Data Warehouse contains one database ... Read More

The Metadata hub is used for handling the interchange and distributing of technical Metadata between decision processing products. It is designed for use primarily by technical staff during the growth and maintenance of data warehouses. There are four requirements of this hub are −A Metadata hub should provide the interchange of Metadata among systems and products in a shared Meta data environment. The hub must have a record and open programmatic object interface (employing COM or CORBA, for example) that allows third-party tools to control the services of the hub. A file transfer structure supporting industry-identified file formats (comma delimited ... Read More