BIGOWL: Knowledge centered Big Data analytics

Abstract

Knowledge extraction and incorporation is currently considered to be beneficial for efficient Big Data an- alytics. Knowledge can take part in workflow design, constraint definition, parameter selection and con- figuration, human interactive and decision-making strategies. This paper proposes BIGOWL, an ontologyto support knowledge management in Big Data analytics. BIGOWL is designed to cover a wide vocab- ulary of terms concerning Big Data analytics workflows, including their components and how they areconnected, from data sources to the analytics visualization. It also takes into consideration aspects suchas parameters, restrictions and formats. This ontology defines not only the taxonomic relationships be- tween the different concepts, but also instances representing specific individuals to guide the users inthe design of Big Data analytics workflows. For testing purposes, two case studies are developed, whichconsists in: first, real-world streaming processing with Spark of traffic Open Data, for route optimizationin urban environment of New York city; and second, data mining classification of an academic dataset onlocal/cloud platforms. The analytics workflows resulting from the BIGOWL semantic model are validatedand successfully evaluated.