Found 422 Articles for Data Mining

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

A data warehouse architecture exhibits several layers of data in which data from one layer are changed from data of the lower layer. Data sources, also known as stored in open databases, form the lowest layer. They include structured data saved in the open database system and legacy systems, or unstructured or semi-structured data saved in files. There are several types of success associated with data warehousing which are as follows −Economic success − The data warehouse has a specific impact on the bottom line.Political success − People like what is done. If the data warehouse is not required, it’s ... Read More

A bitmap filter is beneficial only if it is selective. The query optimizer decides when an optimized bitmap filter is selective enough to be helpful and to which operators the filter is used. The optimizer field the optimized bitmap filters on all departments of a star join and uses costing rules to decide whether the plan supports the smallest estimated implementation cost.When the optimized bitmap filter is nonselective, the cost estimate is generally too high and the plan is rejected. When considering where to locate optimized bitmap filters in the plan, the optimizer views for hash join variants include a ... Read More

HOLAP represents Hybrid OLAP. It can manage a tradeoff between ROLAP’s scalability and MOLAP’s query implementation, some commercial OLAP servers are depended on the HOLAP method. In this case, the user determines which portion of the data to save in the MOLAP and which in the ROLAP. For example, generally, the low-level data are saved using a relational database, while higher-level data, including aggregations, are stored in an independent MOLAP.HOLAP is a mixture of ROLAP (relational OLAP) and MOLAP (multidimensional OLAP) which are different implementations of OLAP. HOLAP enables storing elements of the data in a MOLAP store and another ... Read More

MOLAP represents Multidimensional OLAP. It supports tuples as the data storage unit. The MOLAP applies a dedicated n-dimensional array storage engine and OLAP middleware to handle data. Hence, OLAP queries are completed through direct addressing to the associated multidimensional views (data cubes).This structure focuses on the pre-computation of the transactional information into the aggregations, which results in fast query execution performance. Particularly, MOLAP pre-computes and stores aggregated measures at each hierarchy level at load time, and stores and indexes these values for immediate retrieval.The full pre-computation needed a large amount of overhead, both in processing time and in the storage ... Read More

ROLAP represents Relational OLAP. It can save the data based on the familiar relational DBMS technology. In this method, data and the related aggregations are saved in RDBMS, and OLAP middleware is used to execute the handling and exploration of data cubes.This architecture targets the optimization of the RDBMS back end and supports more tools and services including data cube navigation logic. Because of the use of the RDBMS back end, the main benefit of ROLAP is scalability in managing high data volumes.These are the intermediate servers that stand in among a relational back-end server and client front-end tools. They ... Read More