Intelligent Schema Integrator (ISI): A tool to solve the problem of naming conflict for schema integration

The data stored in the data warehouse are mostly coming from different sources. It may be developed using different model or structure for the schema. In order to improve the usability of these data, the process of combining or integrating is needed so that it can provide users with a unified view or a global view of these data. The most important issue in data integration is the schema integration: that is to solve the problem of “how can equivalent real-world entities from multiple data sources be matched up?” This is referred to as entity identification process. Terms may be given a different interpretation at different sources by different people. For example, how can data analyst be sure that customer_id in one database and cust_number in another refer to the same entity? In this paper, a tool which is called an Intelligent Schema Integrator (ISI) is built to increase the uses of data from the data warehouse and to make the process more simple, systematic and impressive. ISI is an intelligent tool which can be used to integrate two different schemas from different sources into a unified schema (global schema). ISI is developed to solve the problems of naming conflict which are homonym conflict and synonym conflict. Homonym conflict means the same element name is used to represent different concept. Synonym conflict means different element name is used to represent the same concept. Thesaurus is used to get the meaning of each element concept and compares it with the other concept. An interface is built to allow the user to choose which elements are going to be renamed or removed, if there are occurrences of homonym and synonym conflicts in the schemas. These are the intelligence features built for ISI. The methodology used in this study consists of 4 phases: Design the Input and Output, Extraction, Comparison, and Integration. The development of this tool is an important direction for more efficient and effective implementation of data integrati- - on in data warehousing.