Advancing Big Data for Humanitarian Needs

Abstract At the present moment, almost every business is witnessing a bursting of data. Big data now available for analytics present complex, daunting challenges due to the vast number of digital data generated daily by different organizations. The public, government, and human rights organizations are adapting to this massively increasing amount of text, images, and video data available to analyze scientifically. For example, the social contribution of conflicts, political violence and disasters are all digitalized through the streaming of data. The vast amount of data has improved the global community's ability to defend and allow for progress of rights of vulnerable people around the globe. And, if big data processing is to improve lives, its existing data gathering methods should assist Humanitarian Affairs, and not replace them. Therefore, finding ways to increase humanitarian services with data, highlighting the importance of big data, are critically important. Numerous organizations have escaped the frustrations of their first-generation data warehouses by replacing older database technologies with significant data which is unstructured in a scalable, error tolerant and efficient way. The purpose of this paper is to propose a big data platform for large-scale data analysis by using the Map Reduce framework for unstructured data stored into integrating distributed-clustered systems such as NoSQL (Not Only SQL) and Hadoop Distributed File System (HDFS).