From data point timelines to a well curated data set, data mining of experimental data and chemical structure data from scientific articles, problems and possible solutions

2013 ◽  
Vol 27 (7) ◽  
pp. 583-603 ◽  
Author(s):  
Villu Ruusmann ◽  
Uko Maran
Keyword(s):  
Experimental Data ◽  
Data Mining ◽  
Structure Data ◽  
Chemical Structure ◽  
Data Set ◽  
Data Point

Privacy Preserving Data Mining on Unstructured Data

2017 ◽  
pp. 167-190
Author(s):  
Trupti Vishwambhar Kenekar ◽  
Ajay R. Dani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Structure Data ◽  
Data Privacy ◽  
Differential Privacy ◽  
Unstructured Data ◽  
Map Reduce ◽  
Individual Data ◽  
Data Set ◽  
Privacy Preserving Data Mining

As Big Data is group of structured, unstructured and semi-structure data collected from various sources, it is important to mine and provide privacy to individual data. Differential Privacy is one the best measure which provides strong privacy guarantee. The chapter proposed differentially private frequent item set mining using map reduce requires less time for privately mining large dataset. The chapter discussed problem of preserving data privacy, different challenges to preserving data privacy in big data environment, Data privacy techniques and their applications to unstructured data. The analyses of experimental results on structured and unstructured data set are also presented.

EXPERIMENTAL DATA SET NO. 24: PHASE DISTRIBUTION IN BUBBLY FLOW

1992 ◽  
Vol 6 (1-4) ◽  
pp. 257-301 ◽  
Author(s):  
Akimi Serizawa ◽  
Isao Kataoka ◽  
Itaru Michiyoshi
Keyword(s):  
Experimental Data ◽  
Phase Distribution ◽  
Bubbly Flow ◽  
Data Set

COMPARISONS WITH PREDICTIONS WITH EXPERIMENTAL DATA SET NO. 9: DIVIDING FLOW IN A T-JUNCTION

1992 ◽  
Vol 6 (1-4) ◽  
pp. 39-49
Author(s):  
Theo G. Theofanous ◽  
W. H. Amarasooriya
Keyword(s):  
Experimental Data ◽  
Data Set

Validating Numerical CFD Simulations With Experimental Data for Turbulence Phenomena in Axial Flow Gas Turbine Diffusers

2009 ◽  
Author(s):  
Farrokh Zarifi-Rad ◽  
Hamid Vajihollahi ◽  
James O’Brien
Keyword(s):  
Experimental Data ◽  
Gas Turbine ◽  
Turbine Blades ◽  
Axial Flow ◽  
Experimental Results ◽  
Scale Model ◽  
Data Set ◽  
Pressure Profiles ◽  
Scale Models ◽  
Inlet Conditions

Scale models give engineers an excellent understanding of the aerodynamic behavior behind their design; nevertheless, scale models are time consuming and expensive. Therefore computer simulations such as Computational Fluid Dynamics (CFD) are an excellent alternative to scale models. One must ask the question, how close are the CFD results to the actual fluid behavior of the scale model? In order to answer this question the engineering team investigated the performance of a large industrial Gas Turbine (GT) exhaust diffuser scale model with performance predicted by commercially available CFD software. The experimental results were obtained from a 1:12 scale model of a GT exhaust diffuser with a fixed row of blades to simulate the swirl generated by the last row of turbine blades five blade configurations. This work is to validate the effect of the turbulent inlet conditions on an axial diffuser, both on the experimental front and on the numerical analysis approach. The object of this work is to bring forward a better understanding of velocity and static pressure profiles along the gas turbine diffusers and to provide an accurate experimental data set to validate the CFD prediction. For the CFD aspect, ANSYS CFX software was chosen as the solver. Two different types of mesh (hexagonal and tetrahedral) will be compared to the experimental results. It is understood that hexagonal (HEX) meshes are more time consuming and more computationally demanding, they are less prone to mesh sensitivity and have the tendancy to converge at a faster rate than the tetrahedral (TET) mesh. It was found that the HEX mesh was able to generate more consistent results and had less error than TET mesh.

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