Abstract
Much information stored in current databases is not always present at necessary different levels of detail or granularity for Decision-Making Processes (DMP). Some organizations have implemented the use of central database - Data Warehouse (DW) - where information performs analysis tasks. This fact depends on the Information Systems (IS) maturity, the type of informational requirements or necessities the organizational structure and business own characteristic. A further important point is the intrinsic structure of complex data; nowadays it is very common to work with complex data, due to syntactic or semantic aspects and the processing type (Darmont et al., 2006). Therefore, we must design systems, which can to maintain data complexity to improve the DMP. OLAP systems solve the problem of present different aggregation levels and visualization for multidimensional data through cube’s paradigm. The classical data analysis techniques (factorial analysis, regression, dispersion, etc.) are applied to individuals (tuples or individuals in transactional databases). The classic analysis objects are not expressive enough to represent tuples, which contain distributions, logic rules, multivaluate attributes, and intervals. Also, they must be able to respect their internal variation and taxonomy maintaining the dualism between individual and class. Consequently, we need a new data type holding these characteristics. This is just the mathematical concept model introduced by Diday called Symbolic Object (SO). SO allows modeling physic entities or real world concepts. The former are the tuples stored in transactional databases and the latter are high entities obtained from expert’s analysis, automatic classification or some particular aggregation taken from analysis units (Bock & Diday, 2000). The SO concept helps construct the DW and it is an important development for Data Mining (DM): for the manipulation and analysis of aggregated information (Nigro & González Císaro, 2005). According to Calvanese, data integration is a central problem in the design of DWs and Decision Support Systems (Calvanese, 2003; Cali, et al., 2003); we make the architecture for Symbolic Object Warehouse construction with integrative goal. Also, it combines with Data Analysis tasks or DM. This paper is presented as follows: First, Background: DW concepts are introduced. Second, Main Focus divided into: SOs Basic Concepts, Construing SOs and Architecture. Third, Future Trends, Conclusions, References and Key Terms.
TopIntroduction
Much information stored in current databases is not always present at necessary different levels of detail or granularity for Decision-Making Processes (DMP). Some organizations have implemented the use of central database - Data Warehouse (DW) - where information performs analysis tasks. This fact depends on the Information Systems (IS) maturity, the type of informational requirements or necessities the organizational structure and business own characteristic.
A further important point is the intrinsic structure of complex data; nowadays it is very common to work with complex data, due to syntactic or semantic aspects and the processing type (Darmont et al., 2006). Therefore, we must design systems, which can to maintain data complexity to improve the DMP.
OLAP systems solve the problem of present different aggregation levels and visualization for multidimensional data through cube’s paradigm. The classical data analysis techniques (factorial analysis, regression, dispersion, etc.) are applied to individuals (tuples or individuals in transactional databases). The classic analysis objects are not expressive enough to represent tuples, which contain distributions, logic rules, multivaluate attributes, and intervals. Also, they must be able to respect their internal variation and taxonomy maintaining the dualism between individual and class.
Consequently, we need a new data type holding these characteristics. This is just the mathematical concept model introduced by Diday called Symbolic Object (SO). SO allows modeling physic entities or real world concepts. The former are the tuples stored in transactional databases and the latter are high entities obtained from expert’s analysis, automatic classification or some particular aggregation taken from analysis units (Bock & Diday, 2000).
The SO concept helps construct the DW and it is an important development for Data Mining (DM): for the manipulation and analysis of aggregated information (Nigro & González Císaro, 2005). According to Calvanese, data integration is a central problem in the design of DWs and Decision Support Systems (Calvanese, 2003; Cali, et al., 2003); we make the architecture for Symbolic Object Warehouse construction with integrative goal. Also, it combines with Data Analysis tasks or DM.
This paper is presented as follows: First, Background: DW concepts are introduced. Second, Main Focus divided into: SOs Basic Concepts, Construing SOs and Architecture. Third, Future Trends, Conclusions, References and Key Terms.