Semantic modeling for engineering data analytics solutions

Abstract

Data Analytics Solution (DAS) 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 DAS engineering. We explore the application scope of those techniques, the different types of semantic concepts they use and the role these concepts play during DAS 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 DASs. 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 the limitations of the existing body of research, showcasing the potential of semantic modeling to enhance DASs and presenting the possibility of leveraging ontologies for effective end-to-end DAS engineering.

Big Data Analytics Has Little to Do with Analytics

“…Then we study how this knowledge (semantic concepts) is applied in analytic process development process, related to different development tasks such as business understanding, data extraction, model selection and analytic process composition. Based on the limitations we found form the literature survey [6] we suggest future research directions in knowledge enabled analytics. Mainly, the analysts should consider leveraging intent related models that represent business requirements and goals, as only then the solution can address the core problem.…”

“…To explore the state-of-art that use semantic technology for data analytic solution engineering and identify its potential, we conducted a systematic literature review that explores literature spread over three spheres: software engineering, semantic modelling and data analytics. A detailed discussion of this review findings is presented in [6].…”

“…The survey [6] provides evidence to the importance of service based approaches in analytic solution engineering and the SOA community has multitude of research regarding the service modelling, selection etc. which are useful for realizing the Agile based big data analytics development cycle.…”

A knowledge-driven approach for designing data analytics platforms

“…A recent systematic literature review [4] has studied existing research efforts related to the use of semantic technology in data analytics platform design and implementation. The majority of identified studies use semantic models to support isolated activities such as model generation [32] or data source selection [18].…”

An Overview of Ontologies and Tool Support for COVID-19 Analytics

“…In general, ontology-based systems that utilise knowledge graphs and linked data are being used for data integration, information retrieval, recommender systems as well as explainable machine learning [20]. Such approaches have the feasibility to be adopted in solving analytics challenges associated with pandemic [21]- [23].…”