Semantic Modeling for Engineering Data Analytic Solutions

Tracking #: 1823-3036

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

Responsible editor: 
Oscar Corcho

Submission type: 
Survey Article
Abstract: 
Data analytic solutions often are a composition of multiple tasks from data exploration to result presentation that are applied in various contexts and on different data sets. Semantic modeling based on open world assumption support flexible modeling of linked knowledge and in turn may help to tackle heterogeneity and continuous changing requirements in data analytic solutions. Hence the objective of this paper is to review existing techniques that leverage semantic web technologies to facilitate data analytic solution engineering. We explore the application scope of those techniques, the different classes of semantic concepts they use and the role these concepts play during the analytic solution development process. To gather evidence for the study we performed a systematic mapping study by identifying and reviewing 49 papers that incorporate semantic models in engineering data analytic solutions. One of the paper's findings is that existing models represent four classes of knowledge: domain knowledge, analytics knowledge, services and user intentions. Another finding is how this knowledge is used 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 analytic systems and discussing the possibility of leveraging ontologies for effective end-to-end data analytic solution engineering.
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