• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2004.05b
• /
• pp.354-357
• /
• 2004

Data warehouse is subject-oriented, integrated, non-volatiled data, and it used for OLAP(On-Line Analytical Processing) the extraction of information from making decision processing. In the present, lots of study have been devoted to multidimensional modeling between OLAP operator and star schema. In this paper, the design of using the snowflake schema for object-oriented conceptual relation is more extended than using drill-across operator. The object-oriented relation schema which was not applicable has been improved through the design.

• Spatial Information Research
• /
• v.13 no.3 s.34
• /
• pp.239-252
• /
• 2005

The major reason that spatial data warehousing has attracted a great deal of attention in business GIS in recent years is due to the wide availability of huge amount of spatial data and the imminent need for fuming such data into useful geographic information. Therefore, this research has been focused on designing and implementing the pilot tested system for spatial decision making. The purpose of the system is to predict targeted marketing area by discriminating the customers by using both transaction quantity and the number of customer using credit card in department store. Moreover, the pilot tested system of this research provides OLAP tools for interactive analysis of multidimensional data of geographically various granularities, which facilitate effective spatial data mining. focused on the analysis methodology, the case study is aiming to use GIS and clustering for knowledge discovery. Especially, the importance of this study is in the use of snowflake schema model capabilities for GIS framework.

• Journal of Information Technology Applications and Management
• /
• v.20 no.1
• /
• pp.87-106
• /
• 2013

This study is to explore the airline network patterns of Incheon and Narita International Airports using passenger flight departure and arrival data of the two airports. The so-called Origin-Destination data is collected from the airports' websites and some of the important data items are flight number, city of origin, destination city, departure/arrival time, number of flights, and delay time. A snowflake schema dimensional model is proposed and implemented. Tableau Public, a well-known visual analytic tool, is used to connect the dimensional model and played an important role in navigating the data space to find interesting and visual patterns among corresponding airports and airlines. For the efficiency of analyzing this spacious data mart, data visualization method was used. Four types of visualization method proposed by Yau was used; visualizing patterns over time, visualizing proportions, visualizing relationships, and visualizing spatial relationships. The strength of connectivity of each flight segments is calculated to evaluate the degree of globalization of Seoul and Tokyo. We anticipate that various patterns and new findings produced by the data mart would provide airline managers, airport authorities, and policy makers in the field of travel and transportation with insightful information.

• Asia pacific journal of information systems
• /
• v.14 no.3
• /
• pp.169-187
• /
• 2004

Self-maintainability of data warehouse(DW) views is an ability to maintain the DW views without requiring an access to (i) any underlying databases or (ii) any information beyond the DW views and the delta of the databases. With our proposed method, DW views can be updated by using only the old views and the differential files such as different files, referential integrity differential files, linked differential files, and backward-linked differential files that keep the truly relevant tuples in the delta. This method avoids accessing the underlying databases in that the method achieves self-maintainability even in preparing auxiliary information. We showed that out method can be applicable to the DW views that contain joins over relations in a star schema, a snowflake schema, or a galaxy schema.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.6A
• /
• pp.870-884
• /
• 2000

Among OLAP(On-Line Analytical Processing) approaches, ROLAP(Relational OLAP) based on the star, snowflake schema which offer the multidimensional analytical method has performance problem and MOLAP (Multidimensional OLAP) based on Multidimensional Database System has scalability problem. In this paper, to solve the limitaions of previous approaches, design and implementation of multidimensional data model based on Object-Relation DBMS was proposed. With the extensibility of Object-Relation DBMS, it is possible to advent multidimensional data model which more expressively define multidimensional concept and analysis functions that are optimized for the defined multidimensional data model. In addition, through the hierarchy between data objects supported by Object-Relation DBMS, the aggregated data model which is inherited from the super-table, multidimensional data model, was designed. One these data models and functions are defined, they behave just like a built-in function, w th the full performance characteristics of Object-Relation DBMS engine.