Transformasi Teknologi Database Untuk Mendukung Peningkatan Daya Saing Organisasi

Abstract

Competition between organizations in this digital era is getting tighter and tougher. Organizations must be able to take advantage of information technology to increase productivity and produce superior products.  More than that, organizations must also be able to utilize this technology to build business intelligence to increase their competitiveness.  The phenomenon of overflowing data that occurs in this digital era must be able to be converted into knowledge that is useful for organizational progress.  Therefore, organizations must be able to apply big data technology to be able to manage large, diverse, and very fast amounts of data.  One of the big data technology ecosystems that plays an important role in data management is the NoSQL database.  This engine has very different characteristics compared to the RDBMS which has been used to manage internal organizational data.  Broadly speaking, NoSQL databases are divided into four categories.  Two of the most popular are document-oriented NoSQL databases and column-oriented NoSQL databases. In order for big data technology to process internal organizational data, a good database transformation method is needed to migrate data from RDBMS to NoSQL databases.  Several studies have been conducted for this.  Graph Transforming V2 Algorithm has been successfully developed to effectively transform data from RDBMS to document-oriented NoSQL databases.  Multiple Nested Schema has also been developed to transform data from an RDBMS to a column-oriented NoSQL database.  However, some special conditions in RDBMS cannot be handled properly.  This study proposes the development of adjacency metrics to support the development of Multiple Nested Schema in order to transform data from an RDBMS that contains multiple relationships and recursive relationships into an effective CoNoSQLDB schema.

Keywords: Transformation; RDBMS; CoNoSQLDB; Multiple Relationships; Recursive Relationships; Business Intelligence.