Glossary
Ad Hoc Query:
A query which is designed runtime means at the time of execution as opposed to the canned query is called as Ad Hoc Query. Ad Hoc queries are generally used to meed the ad hoc requirement. Now a days most commercial BI software’s have this features using drag-drop functionality.
Aggregation:
The process of summarizing or combining data.
Catalog:
A component of a data dictionary that describes and organizes the various aspects of a database such as objbects, tables, function and other database objects.
Cross Tab Report:
A type of multi-dimensional report that displays values or measures in cells created by the intersection of two or more dimensions in a table format.
Cube:
A multi-dimensional representation of data that has multiple dimensions and measures that are created by an Online Analytical Processing System (OLAP) on top of Datawarehouse. Each dimension may be organized into a hierarchy with multiple levels. The intersection of two or more dimensional categories is referred to as a cell.
Data Cleansing:
The process of cleaning or removing errors in order to produce quality data for reporting purpose is call ed as Data Cleansing, In this process redundancies and inconsistencies in the data is removed before loading it into a data mart or data warehouse. It is part of the quality assurance process.
Data Mart:
A database that is similar in structure to a data warehouse, but is typically smaller and is focused on a more limited subject area. Multiple, integrated data marts are sometimes referred to as an Integrated Data Warehouse. Data marts may be used in place of a larger data warehouse or in conjunction with it. They are typically less expensive to develop and faster to deploy and are therefore becoming more popular with smaller organizations.
Data Migration:
The transfer of data from one platform to another. This may include conversion from one language, file structure and/or operating environment to another.
Data Mining:
The process of researching data marts and data warehouses to detect specific patterns in the data sets. Data mining may be performed on databases and multi-dimensional data cubes with ad hoc query tools and OLAP software. The queries and reports are typically designed to answer specific questions to uncover trends or hidden relationships in the data.
Data Transformation:
The modification of transaction data extracted from one or more data sources before it is loaded into the data mart or warehouse. The modifications may include data cleansing, translation of data into a common format so that is can be aggregated and compared, summarizing the data, etc.
Data Warehouse:
An integrated, non-volatile database of historical information that is designed around specific content areas and is used to answer questions regarding an organizations operations and environment.
Database Management System:
The software that is used to create data warehouses and data marts. For the purposes of data warehousing, they typically include relational database management systems and multi-dimensional database management systems. Both types of database management systems create the database structures, store and retrieve the data and include various administrative functions.
Decision Support System (DSS):
A set of queries, reports, rule-based analyses, tables and charts that are designed to aid management with their decision-making responsibilities. These functions are typically “wrapped around” a data mart or data warehouse. The DSS tends to employ more detailed level data than an EIS.
Dimension:
A variable, perspective or general category of information that is used to organize and analyze information in a multi-dimensional data cube.
Drill Down:
The ability of a data-mining tool to move down into increasing levels of detail in a data mart, data warehouse or multi-dimensional data cube.
Drill Up:
The ability of a data-mining tool to move back up into higher levels of data in a data mart, data warehouse or multi-dimensional data cube.
Executive Information Management System (EIS):
A type of decision support system designed for executive management that reports summary level information as opposed to greater detail derived in a decision support system.
Extraction, Transformation and Loading (ETL) Tool:
Software that is used to extract data from a data source like a operational system or data warehouse, modify the data and then load it into a data mart, data warehouse or multi-dimensional data cube.
Granularity:
The level of detail in a fact table or report.
Hierarchy:
The organization of data, e.g. a dimension, into a outline or logical tree structure. The strata of a hierarchy are referred to as levels. The individual elements within a level are referred to as categories. The next lower level in a hierarchy is the child; the next higher level containing the children is their parent.
Measure:
A quantifiable variable or value stored in a multi-dimensional OLAP cube. It is a value in the cell at the intersection of two or more dimensions.
Member:
One of the data points for a level of a dimension.
Meta Data:
Information in a data mart or warehouse that describes the tables, fields, data types, attributes and other objects in the data warehouse and how they map to their data sources. Meta data is contained in database catalogs and data dictionaries.
Multi-Dimensional Online Processing (MOLAP):
Software that creates and analyzes multi-dimensional cubes to store its information.
Non-Volatile Data:
Data that is static or that does not change. In transaction processing systems the data is updated on a continual regular basis. In a data warehouse the database is added to or appended, but the existing data seldom changes.
Normalization:
The process of eliminating duplicate information in a database by creating a separate table that stores the redundant information. For example, it would be highly inefficient to re-enter the address of an insurance company with every claim. Instead, the database uses a key field to link the claims table to the address table. Operational or transaction processing systems are typically “normalized”. On the other hand, some data warehouses find it advantageous to de-normalize the data allowing for some degree of redundancy.
Online Analytical Processing (OLAP):
The process employed by multi-dimensional analysis software to analyze the data resident in data cubes. There are different types of OLAP systems named for the type of database employed to create them and the data structures produced.
Open Database Connectivity (ODBC):
A database standard developed by Microsoft and the SQL Access Group Consortium that defines the “rules” for accessing or retrieving data from a database.
Relational Online Analytical Processing (ROLAP):
OLAP software that employs a relational strategy to organize and store the data in its database.
Replication:
The process of copying data from one database table to another.
Synchronization:
The process by which the data in two or more separate database are synchronized so that the records contain the same information. If the fields and records are updated in one database the same fields and records are updated in the other.