Attribute oriented induction with star schema

This paper will propose a novel star schema attribute induction as a new attribute induction paradigm and as improving from current attribute oriented induction. A novel star schema attribute induction will be examined with current attribute oriented induction based on characteristic rule and using non rule based concept hierarchy by implementing both of approaches. In novel star schema attribute induction some improvements have been implemented like elimination threshold number as maximum tuples control for generalization result, there is no ANY as the most general concept, replacement the role concept hierarchy with concept tree, simplification for the generalization strategy steps and elimination attribute oriented induction algorithm. Novel star schema attribute induction is more powerful than the current attribute oriented induction since can produce small number final generalization tuples and there is no ANY in the results.Comment: 23 Pages, IJDM

An Approach for Automatically Generate Star Schema from Natural Language

The star schema is a form of data warehouse modelling, which acts primary storage for dimensional data that enables efficient retrieval of business information for decision making. Star schemas can be generated from business needs that we refer to as a user business key or from a relational schema of the operational system. There are many tools available to automatically generate star schema from relational schema, such as BIRST and SAMSTAR; however, there is no application that can automatically generate it from a user business key that is represented in the form of human language. In this paper, we offered an approach for automatically generating star schema from user business key(s). It begins by processing the user business key using a syntactical parsing process to identify noun words. Those identified words will be used to generate dimension table candidates and a fact table. The evaluation result indicates that the tool can generate star schema based on the inputted user business key(s) with some limitations in that the star schema will not be formed if the dimensional tables do not have a direct relationship

An Automatic Tool to Transform Star Schema Data Warehouse to Physical Data Model

Data warehouse is used to store very large data for supporting company to perform data analysis. Star schema is data warehouse model most widely used by companies today. Sometimes, data stored in star schema need to be exported to conventional model so that others may use them without knowing the OLTP (Online Transaction Processing) or source model, particularly for backup and recovery case. Therefore, this research aimed to transform star schema data model to physical data model. Two cases have been identified case, which are: 1) the star schema with simple star schema and the multifact star schema (standard case); and 2) the multi star schema (nonstandard case). There are five processes to build the physical model from the star schema model, namely: 1) finding fact table, 2)finding dimension table, 3) deleting time dimension table, and adding date attribute to fact table, 4) changing fact table to relational table, and 5) changing dimension table to relational table. The prototype was built to implement this phase, and it was tested using some cases. The prototype transformed star schema to physical data model properly (complete design with table, attribute, relation, data type). Some results were different (were not consistent) from the source model because there are many possibilities of star schema for one model, and there is no metadata that are stored when the star schema model was built

Converting Operation Database to Star Schema Structure

Today's, structural data representation has been developed rapidly in term of complexity. Different studies addressed the advantages of employing five star schemas in simplifying and managing the data components based techniques. Moreover, other researchers highlighted the importance of using data warehouse in enhancing the database abilities and handling large amounts of data in different structural representation. Hence, this study aimed to apply the five star schema structures in simplifying the data retrieval process. System development research methodology has been employed in this study for analyzing and developing the proposed system. Result has been conducted based on the using of use test case technique

Implementation of the Multidimensional Modeling Concepts into Object-Relational Databases

A key to survival in the business world is being able to analyze, plan and react to changing business conditions as fast as possible. With multidimensional models the managers can explore information at different levels of granularity and the decision makers at all levels can quickly respond to changes in the business climate-the ultimate goal of business intelligence. This paper focuses on the implementation of the multidimensional concepts into object-relational databases.e-business, database