Hybrid Multidimensional Relational and Link Analytical Knowledge Discovery for Law Enforcement

Abstract

The challenges facing the Department of Homeland Security (DHS) require not only multi-dimensional, but also multi-scale data analysis. In particular, the ability to seamlessly move from summary information, such as trends, into detailed analysis of individual entities, while critical for law enforcement, typically requires manually transferring information among multiple tools. Such time-consuming and error prone processes significantly hamper the analysts' ability to quickly explore data and identify threats. As part of a DHS Science and Technology effort, we have been developing and deploying for Immigration and Customs Enforcement the CubeLink system integrating information between relational data cubes and link analytical semantic graphs. In this paper we describe CubeLink in terms of the underlying components, their integration, and the formal mapping from multidimensional data analysis into link analysis. In so doing, we provide a formal basis for one particular form of automatic schema-ontology mapping from OLAP data cubes to semantic graphs databases, and point the way towards future "intelligent" OLAP data cubes equipped with meta-data about their dimensional typing.