FEDSA: A Data Federation Platform for Law Enforcement Management

Abstract

In the era of big data, new challenges occur in the field of data federation research. New types of data sources with new formats of data have emerged, and end users need to conduct complex search and data analytical tasks, which impose requirements such flexible data federation, customized security mechanism and high-performance processing (for example, near real time query). To address these challenges, this paper proposes a data federation platform named FEDSA and reports on an initial implementation. Distinctive features of the platform include process-driven data federation, Data Federation as a Service, a simple query language over a high-level common data model, data security protection over all federation services, query re-writing and full distribution. We demonstrate how these features address the challenges, discuss the performance of the current implementation, and outline future extensions.