RT Journal Article
T1 BIGOWL: Knowledge centered Big Data analytics
A1 Barba-González, Cristóbal
A1 García-Nieto, José Manuel
A1 Roldán-García, María del Mar
A1 Navas-Delgado, Ismael
A1 Nebro-Urbaneja, Antonio Jesús
A1 Aldana-Montes, José Francisco
K1 Ontología
AB 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.
PB Elsevier
YR 2018
FD 2018-08-23
LK https://hdl.handle.net/10630/36436
UL https://hdl.handle.net/10630/36436
LA eng
NO Barba-González, C., García-Nieto, J., del Mar Roldán-García, M., Navas-Delgado, I., Nebro, A. J., & Aldana-Montes, J. F. (2019). BIGOWL: Knowledge centered big data analytics. Expert Systems with Applications, 115, 543-556.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026