Extraction-Transformation-Loading Processes

A data warehouse (DW) is a collection of technologies aimed at enabling the knowledge worker (executive, manager, analyst, etc.) to make better and faster decisions. The architecture of a DW exhibits various layers of data in which data from one layer are derived from data of the lower layer (see Figure 1). The operational databases, also called data sources, form the starting layer. They may consist of structured data stored in open database and legacy systems, or even in files. The central layer of the architecture is the global DW. The global DW keeps a historical record of data that result from the transformation, integration, and aggregation of detailed data found in the data sources. An auxiliary area of volatile data, data staging area (DSA) is employed for the purpose of data transformation, reconciliation, and cleaning. The next layer of data involves client warehouses, which contain highly aggregated data, directly derived from the global warehouse. There are various kinds of local warehouses, such as data mart or on-line analytical processing (OLAP) databases, which may use relational database systems or specific multidimensional data structures. The whole environment is described in terms of its components, metadata, and processes in a central metadata repository, located at the DW site.

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Incorporating Data Stream Analysis into Decision Support Systems

Encyclopedia of Information Science and Technology, First Edition ◽

10.4018/978-1-59140-553-5.ch252 ◽

2005 ◽

pp. 1431-1439

Author(s):

Damianos Chatziantoniou ◽

George Doukidis

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Complex Analysis ◽

Support Systems ◽

Knowledge Worker ◽

Database Systems ◽

Executive Information Systems ◽

Executive Manager ◽

On Line ◽

Data Stream Analysis

Traditional decision support systems (DSS) and executive information systems (EIS) gather and present information from several sources for business purposes. It is an information technology to help the knowledge worker (executive, manager, analyst) make faster and better decisions. So far, this data was stored statically and persistently in a database, typically in a data warehouse. Data warehouses collect masses of operational data, allowing analysts to extract information by issuing decision support queries on the otherwise discarded data. In a typical scenario, an organization stores a detailed record of its operations in a database, which is then analyzed to improve efficiency, detect sales opportunities, and so on. Performing complex analysis on this data is an essential component of these organizations’ businesses. Chaudhuri and Dayal (1997), present an excellent survey on decision-making and on-line analytical processing (OLAP) technologies for traditional database systems.

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Database Queries, Data Mining, and OLAP

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch094 ◽

2011 ◽

pp. 598-603

Author(s):

Lutz Hamel

Keyword(s):

Data Mining ◽

Relational Database ◽

Query Language ◽

Data Retrieval ◽

Database Systems ◽

Database Queries ◽

On Line ◽

Analytical Processing ◽

Relational Database Systems ◽

Complementary Value

Modern, commercially available relational database systems now routinely include a cadre of data retrieval and analysis tools. Here we shed some light on the interrelationships between the most common tools and components included in today’s database systems: query language engines, data mining components, and on-line analytical processing (OLAP) tools. We do so by pair-wise juxtaposition which will underscore their differences and highlight their complementary value.

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