Conceptual Analysis of Big Data Using Ontologies and EER

Large amounts of "big data" are generated every day, many in a "raw" format that is difficult to analyze and mine. This data contains potential hidden meaningful concepts, but much of the data is superfluous and not of interest to the domain experts. Thus, dealing with big raw data solely by applying a set of distributed computing technologies e.g., MapReduce, BSP [Bulk Synchronous Parallel], and Spark and/or distributed storage systems, namely NoSQL, is generally not sufficient. Extracting the full knowledge that is hidden in the raw data is necessary to efficiently enable analysis and mining. The data needs to be processed to remove the superfluous parts and generate the meaningful domain-specific concepts. In this paper, we propose a framework that incorporates conceptual modeling and EER principle to effectively extract conceptual knowledge from the raw data so that mining and analysis can be applied to the extracted conceptual data.