BIGOWL: Knowledge centered Big Data analytics.

Highlights • A semantic approach to represent and validate Big Data analytics is proposed. • An OWL Ontology and SWRL rules are developed for reasoning in workflow design. • The proposal is validated with two real-world (traffic) and academic cases study. • Obtained semantized data successfully recommends and validate Big Data tasks. • We provide actual Big Data practitioners with software to enhance their analytics. Abstract Knowledge extraction and incorporation is currently considered to be beneficial for efficient Big Data analytics. Knowledge can take part in workflow design, constraint definition, parameter selection and configuration, human interactive and decision-making strategies. This paper proposes BIGOWL, an ontology to support knowledge management in Big Data analytics. BIGOWL is designed to cover a wide vocabulary of terms concerning Big Data analytics workflows, including their components and how they are connected, from data sources to the analytics visualization. It also takes into consideration aspects such as parameters, restrictions and formats. This ontology defines not only the taxonomic relationships between the different concepts, but also instances representing specific individuals to guide the users in the design of Big Data analytics workflows. For testing purposes, two case studies are developed, which consists in: first, real-world streaming processing with Spark of traffic Open Data, for route optimization in urban environment of New York city; and second, data mining classification of an academic dataset on local/cloud platforms. The analytics workflows resulting from the BIGOWL semantic model are validated and successfully evaluated. [ABSTRACT FROM AUTHOR]