Scoring a Data Warehouse Model for Homeland Security Applications

The increasing growth of the amount of noisy and unorganized data on the Internet made the processing of such data a challenging research issue. Data Warehouses (DW) and online analytical processing (OLAP) are some of the widely recognized and used technologies to process and analyze structured data. Providing an accurate DW model with the appropriate facts, measures and dimensions in the context of Homeland Security is a key issue for Intelligence agencies and homeland security tasks. In this paper, we propose a DW model for Online Social Networks (OSN) to assist Security Intelligence agencies in retrieving sensitive information. This model would have a great impact in assisting cyber security specialists in identifying terrorist and crime trends online. The proposed model will be enriched using the Global Terrorism Database (GTD) that reports worldwide events related to terrorism. Besides, these two models will be enriched using a scoring capability to put forward the most imminent information.