Reconstructing Neoproterozoic palaeoclimates using a combined data/modelling approach

2018 ◽  
pp. 61-80 ◽  
Author(s):  
L.E. Sohl ◽  
M.A. Chandler
Keyword(s):  
Data Modelling ◽  
Combined Data ◽  
Modelling Approach

Improved air valve design using evolutionary polynomial regression

2019 ◽  
Vol 19 (7) ◽  
pp. 2036-2043
Author(s):  
G. Balacco ◽  
D. Laucelli
Keyword(s):  
Polynomial Regression ◽  
Pipeline System ◽  
Heuristic Methods ◽  
Data Modelling ◽  
Valve Design ◽  
Technical Literature ◽  
Evolutionary Polynomial Regression ◽  
Physically Based ◽  
Air Valve ◽  
Modelling Approach

Abstract Air valves are usually sized by heuristic methods or, sometimes, even oversized. Although the technical literature has long focused on the correct sizing of air valves to reduce the overpressure generated by the filling of a pipe, the phenomenon is complex and does not seem to be representable by physically based equations in an easy way, to be of practical use for technicians and designers. In this paper, air valve design is approached through an alternative data-modelling approach, based on evolutionary polynomial regression, with the aim to provide symbolic formulas of variable complexity and accuracy, suitable for physical interpretation, and at the same time easy to be used and applied for design purposes. The present investigation suggests a design formula that, given the geometric parameters of the pipeline system where the air valve is installed, provides the maximum tolerable overpressure, thus allowing the optimal air valve orifice size to be identified.

scholarly journals Prioritized Property-Value based Data Modelling for Big Data

2019 ◽  
Vol 8 (9S2) ◽  
pp. 701-705
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Structured Data ◽  
Unstructured Data ◽  
Innovative Approach ◽  
Data Modelling ◽  
Property Value ◽  
Modelling Approach

In the present era, as technology is emerging widely data storage is also increasing its volume or space of storage enormously; which is the current buzz defined as Big Data. Existing Big Data modelling includes mostly in handling structured data but no defined approach was designed for modelling Big Data which includes structured, semi-structured and unstructured data. Among the existing challenges on Big Data, the most imperative challenge is modelling Big Data. This paper proposes a generic modelling approach for modelling Big Data. The effectiveness of this innovative approach is sensed by modelling oncology data using MongoDB. This modelling facilitates ease analytics and is independent of context.

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