Big data and advanced spatial analytics

Abstract

Today's business and government organizations are challenged when trying to manage and analyze information from enterprise databases, streaming servers, social media and open source. This is compounded by the complexity of integrating diverse data types (relational, text, spatial, images, spreadsheets) and their representations (customers, products, suppliers, events, and locations) - all of which need to be understood and re-purposed in different contexts. Identifying meaningful patterns across these different information sources is non-trivial. Moreover, conventional IT tools, such as conventional data warehousing and business intelligence alone, are insufficient at handling the volumes, velocity and variety of content at hand. A new framework and associated tools are needed. Dr. Lopez outlines how data scientists and analysts are applying Spatial and Semantic Web concepts to make sense of this Big Data stream. He will describe new approaches oriented toward search, discovery, linking, and analyzing information on the Web, and throughout the enterprise. The role of Map Reduce is described, as is importance of engineered systems to simplify the creation and configuration of Big Data environments. The key take away is use of spatial and linked open data concepts to enhance content alignment, interoperability, discovery and analytics in the Big Data stream.