BIGOWL: Knowledge centered Big Data 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.

Orchestrating Distributed Computing and Web Technology with Semantic Web and Big Data

“…In accordance with the assertion that was stated by Barba-González et al (2019) in reference [27], the BIGOWL solution makes it possible to carry out studies on huge datasets. At the present, a model for an RDF database setup will include an RDF/programmatic semantic query engine as a component of its overall structure.…”

Big Data Integration of Smart Grid Consumption Data

“…Knowledge extraction and incorporation as part of a workflow design is becoming an important component of Big Data analytics as it favors data and algorithm integration. Barba-Gonzalez [4] use both well-known big data products and frameworks including Spark, Kafka, Weka and less-known solutions such as jMetalSP (previously developed by the same authors [5]) for task optimizing or BigML, for Cloud-based Machine Learning processing to demonstrate that annoting big data sources and algorithms can act as a catalyzer for incorporating domain knowledge and improve the analytical process. The algorithms available to data scientist can be classified in several categories, depending on the data set characteristics as well as the type of problem that needs to be solved, as can be seen in Figure 4.…”

Knowledge discovery using SPARQL property path: The case of disease data set

“…The reason might refer to the fact that most of BioPortal ontologies include hierarchical and taxonomic relationships rather than a graph-based links. In terms of leveraging ontology-driven approach in Semantic Web, Barba-González et al [17] proposed an ontology, BIGOWL (BIG data analytics OWL2 ontology), to represent and consolidate knowledge in a big data analytics workflow. They generated a semantic model using the designed ontology in an RDF repository, which was queried by high-level algorithms using SPARQL.…”