Semantic Modeling for Engineering Data Analytics Solutions

Tracking #: 1992-3205

This paper is currently under review
Madhushi Bandara
Fethi A. Rabhi

Responsible editor: 
Oscar Corcho

Submission type: 
Survey Article
Data analytics solution engineering often involves multiple tasks from data exploration to result presentation which are applied in various contexts and on different datasets. Semantic modeling based on the open world assumption supports flexible modeling of linked knowledge. The objective of this paper is to review existing techniques that leverage semantic web technologies to tackle challenges such as heterogeneity and changing requirements in data analytics solution engineering. We explore the application scope of those techniques, the different types of semantic concepts they use and the role these concepts play during the analytics solution development process. To gather evidence for the study we performed a systematic mapping study by identifying and reviewing 82 papers that incorporate semantic models in engineering data analytics solutions. One of the paper's findings is that existing models can be classified within four types of knowledge spheres: domain knowledge, analytics knowledge, services and user intentions. Another finding is to show how this knowledge is used in literature to enhance different tasks within the analytics process. We conclude our study by discussing limitations of the existing body of research, showcasing the potential of semantic modeling to enhance data analytics solutions and discussing the possibility of leveraging ontologies for effective end-to-end data analytics solution engineering.
Under Review