Application of big data analytics to support homeland security investigations targeting human smuggling networks

Abstract

Human smuggling organizations facilitating the smuggling of aliens into the United States have an unlawful network supporting their illicit transnational activities. Identifying those networks and the key facilitators is challenging due to high volumes of disparate data. This research focuses on how big data analytics can improve the effectiveness and efficiency of Homeland Security Investigations (HSI) targeting human smuggling networks. The purpose of this thesis is to determine whether applying big data analytics to data associated with human smuggling will make network identification of illegal aliens more efficient while producing the necessary articulable facts to substantiate enough probable cause for subsequent investigative actions. An experimental data analytics application called Citrus is used to examine the efficiency and effectiveness of data analytics supporting criminal investigations. Citrus revealed that big data analytics can effectively produce knowledge, including probable cause, more efficiently for HSI in targeting criminal networks. The implications are significant, as the application of data analytics may reshape analytical tradecraft, and compel HSI to revamp data systems. Increases in efficiencies through data analytics may be limited without changes in judicial processes. Upgrading processing capacities for obtaining warrants will become vital as analytics becomes more prevalent.