Evolution of Data Cube Computational Approaches

2011 ◽  
pp. 469-476
Author(s):  
Rebecca Boon-Noi Tan
Keyword(s):  
Decision Support ◽  
Data Analysis ◽  
Support System ◽  
Database Systems ◽  
Data Cube ◽  
Multidimensional Data ◽  
Multidimensional Data Analysis ◽  
Computational Approaches ◽  
Simultaneous Group ◽  
Analytical Processing

Aggregation is a commonly used operation in decision support database systems. Users of decision support queries are interested in identifying trends rather than looking at individual records in isolation. Decision support system (DSS) queries consequently make heavy use of aggregations, and the ability to simultaneously aggregate across many sets of dimensions (in SQL terms, this translates to many simultaneous group-bys) is crucial for Online Analytical Processing (OLAP) or multidimensional data analysis applications (Datta, VanderMeer, & Ramamritham, 2002; Dehne, Eavis, Hambrusch, & Rau-Chaplin, 2002; Elmasri & Navathe, 2004; Silberschatz, Korth & Sudarshan, 2002).

Schema Evolution Models and Languages for Multidimensional Data Warehouses

2009 ◽  
pp. 119-128
Author(s):  
Edgard Benítez-Guerrero ◽  
Ericka-Janet Rechy-Ramírez
Keyword(s):  
Data Analysis ◽  
Data Warehouse ◽  
Historical Data ◽  
Multidimensional Systems ◽  
Multidimensional Data ◽  
Schema Evolution ◽  
Time Dimension ◽  
On Line ◽  
Analytical Processing ◽  
Factual Data

A Data Warehouse (DW) is a collection of historical data, built by gathering and integrating data from several sources, which supports decisionmaking processes (Inmon, 1992). On-Line Analytical Processing (OLAP) applications provide users with a multidimensional view of the DW and the tools to manipulate it (Codd, 1993). In this view, a DW is seen as a set of dimensions and cubes (Torlone, 2003). A dimension represents a business perspective under which data analysis is performed and organized in a hierarchy of levels that correspond to different ways to group its elements (e.g., the Time dimension is organized as a hierarchy involving days at the lower level and months and years at higher levels). A cube represents factual data on which the analysis is focused and associates measures (e.g., in a store chain, a measure is the quantity of products sold) with coordinates defined over a set of dimension levels (e.g., product, store, and day of sale). Interrogation is then aimed at aggregating measures at various levels. DWs are often implemented using multidimensional or relational DBMSs. Multidimensional systems directly support the multidimensional data model, while a relational implementation typically employs star schemas(or variations thereof), where a fact table containing the measures references a set of dimension tables.

TRENDS IN THE DEVELOPMENT OF MODERN DATA ANALYSIS SYSTEMS IN THE ECONOMY

2020 ◽  
pp. 79-84
Author(s):  
A.N. Borodulin ◽  
Keyword(s):  
Data Analysis ◽  
Economic Crisis ◽  
Support System ◽  
Software Tools ◽  
Information Support ◽  
Economic Data ◽  
Business Analysis ◽  
Global Economic Crisis ◽  
Analytical Processing ◽  
Information Support System

The article discusses the main trends in the development of software in the field of business analysis. Promising directions of using software tools for analytical processing of economic data in recent years are identified. The authors also substantiate ways to solve problems in the information support system for companies activities caused by the pandemic and the global economic crisis.

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