Name
Abstract | ||
|---|---|---|
Over the past decade, several sophisticated analytic techniques such as machine learning, neural networks, and predictive modelling have evolved to enable scientists to derive insights from data. Data Science is characterised by a cycle of model selection, customization and testing, as scientists often do not know the exact goal or expected results beforehand. Existing research efforts which explore maximising automation, reproducibility and interoperability are quite mature and fail to address a third criterion, usability. The main contribution of this paper is to explore the development of more complex semantic data models linked with existing ontologies (e.g. FIBO) that enable the standardisation of data formats as well as meaning and interpretation of data in automated data analysis. A model-driven architecture with the reference model that capture statistical learning requirement is proposed together with a prototype based around a case study in commodity pricing. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-52764-2_2 | Lecture Notes in Business Information Processing |
Keywords | Field | DocType |
Ontologies,Semantic,Analytics,Commodity,Statistical learning,FIBO,Architecture,ADAGE,Model-driven engineering,Big data,Data science | Data science,Ontology (information science),Model-driven architecture,Interoperability,Computer science,Usability,Microeconomics,Analytics,Big data,Personalization,Semantic data model | Conference |
Volume | ISSN | Citations |
276 | 1865-1348 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Behnaz | 1 | 0 | 1.35 |
| Aarthi Natarajan | 2 | 0 | 0.68 |
| Fethi Rabhi | 3 | 427 | 50.68 |
| Maurice Peat | 4 | 2 | 2.74 |