Desain Model Integrasi Data Terstruktur (Heterogen) untuk Mendukung Analisis Big Data Kesehatan

Abstract

Big Data is triggered by several studies and prospects on the concepts and processes related to the data warehouse or data warehouse (DW) field. Some conclude that such Warehouse Data will disappear, then Big Data will appear as the natural evolution of the Data Warehouse. The focus of most of Big Data (BD) is very large data integration as large data source processing. The data source is viewed as a convergence area so the data warehouse is part of big data if it can be integrated. There are problems that are just as difficult as data warehouse and big data about how to normalize, integrate, and convert data from many sources into the format needed to carry out large-scale analysis and visualization tools. In previous research it has developed an approach to semi-automatically mapping multiple sources into shared domain ontologies so that they can be quickly combined. In this paper describes the approach to building and implementing integration and restructuring plans to support analysis and visualization tools in very large and diverse datasets. In writing this revises the technology features that underlie Big Data and Data Warehouse, highlighting their differences and convergence areas. Even when some differences exist, both technologies can (and should) be integrated because they aim at the same goal, namely data in exploration to support decision making. This study will explore several convergence strategies, based on common elements in both technologies. In presenting revisions of state-of-the-art in integration proposals from the point of view of the underlying objectives, methodology, architecture and technology, highlighting common elements that support both technologies that can serve as starting points for full integration and we propose a data integration model between two technologies.