Application of multidimensional data analysis methods for modeling of the construction site

2019 ◽  
Vol 0 (28 (1353)) ◽  
Author(s):  
Tetyana Honcharenko ◽  
Victor Mihaylenko
Keyword(s):  
Data Analysis ◽  
Multidimensional Data ◽  
Construction Site ◽  
Multidimensional Data Analysis ◽  
Analysis Methods ◽  
Data Analysis Methods

scholarly journals Proceedings of the 11th International Workshop "Data analysis methods for software systems"

2019 ◽  
Vol 8 ◽  
pp. 1-97
Author(s):  
Jolita Bernatavičienė
Keyword(s):  
Data Analysis ◽  
Software Engineering ◽  
Data Science ◽  
International Workshop ◽  
Digital Technologies ◽  
Research Community ◽  
Multidimensional Data ◽  
Software Systems ◽  
Analysis Methods ◽  
Data Analysis Methods

DAMSS-2019 is the 11th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics that now is the Institute of Data Science and Digital Technologies of Vilnius University. The Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 77. The number of registered participants is 127 from 9 countries. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 9 companies and institutions supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover Artificial Intelligence, Big data, Bioinformatics, Blockchain technologies, Business Rules Software Engineering, Data Science, Deep Learning, Digital Technologies, High-Performance Computing, Machine Learning, Medical Informatics, Modelling Educational Data, Ontological Engineering, Optimization in Data Science, Signal Processing, Visualization Methods for Multidimensional Data. A special session and discussions are organized about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2019.  

scholarly journals Objective assessment of SERS thin films: comparison of silver on copper via galvanic displacement with commercially available fabricated substrates

2017 ◽  
Vol 9 (33) ◽  
pp. 4783-4789 ◽  
Author(s):  
Samuel Mabbott ◽  
Yun Xu ◽  
Royston Goodacre
Keyword(s):  
Thin Films ◽  
Data Analysis ◽  
Objective Assessment ◽  
Galvanic Displacement ◽  
Sers Signal ◽  
Analysis Methods ◽  
Data Analysis Methods

Reproducibility of SERS signal acquired from thin films developed in-house and commercially has been assessed using seven data analysis methods.

Omaha System Public Health Data: Analysis Methods

2010 ◽  
Vol 58 (2) ◽  
pp. e22-e23
Author(s):  
Karen A. Monsen ◽  
Karen S. Martin ◽  
Bonnie L Westra
Keyword(s):  
Public Health ◽  
Data Analysis ◽  
Health Data ◽  
Public Health Data ◽  
Analysis Methods ◽  
Omaha System ◽  
Data Analysis Methods

Peak detection in sediment - charcoal records: impacts of alternative data analysis methods on fire-history interpretations

2010 ◽  
Vol 19 (8) ◽  
pp. 996 ◽  
Author(s):  
Philip E. Higuera ◽  
Daniel G. Gavin ◽  
Patrick J. Bartlein ◽  
Douglas J. Hallett
Keyword(s):  
Data Analysis ◽  
Fire History ◽  
Threshold Value ◽  
Decomposition Methods ◽  
Peak Detection ◽  
Detection Methods ◽  
Statistical Framework ◽  
Analysis Methods ◽  
Decomposition Models ◽  
Data Analysis Methods

Over the past several decades, high-resolution sediment–charcoal records have been increasingly used to reconstruct local fire history. Data analysis methods usually involve a decomposition that detrends a charcoal series and then applies a threshold value to isolate individual peaks, which are interpreted as fire episodes. Despite the proliferation of these studies, methods have evolved largely in the absence of a thorough statistical framework. We describe eight alternative decomposition models (four detrending methods used with two threshold-determination methods) and evaluate their sensitivity to a set of known parameters integrated into simulated charcoal records. Results indicate that the combination of a globally defined threshold with specific detrending methods can produce strongly biased results, depending on whether or not variance in a charcoal record is stationary through time. These biases are largely eliminated by using a locally defined threshold, which adapts to changes in variability throughout a charcoal record. Applying the alternative decomposition methods on three previously published charcoal records largely supports our conclusions from simulated records. We also present a minimum-count test for empirical records, which reduces the likelihood of false positives when charcoal counts are low. We conclude by discussing how to evaluate when peak detection methods are warranted with a given sediment–charcoal record.

scholarly journals Weighing the Giants – I. Weak-lensing masses for 51 massive galaxy clusters: project overview, data analysis methods and cluster images

2014 ◽  
Vol 439 (1) ◽  
pp. 2-27 ◽  
Author(s):  
Anja von der Linden ◽  
Mark T. Allen ◽  
Douglas E. Applegate ◽  
Patrick L. Kelly ◽  
Steven W. Allen ◽  
...  
Keyword(s):  
Data Analysis ◽  
Galaxy Clusters ◽  
Weak Lensing ◽  
Analysis Methods ◽  
Massive Galaxy ◽  
Data Analysis Methods

A Survey of FMRI Data Analysis Methods

2018 ◽  
Author(s):  
Anahid Ehtemami ◽  
Rollin Scott ◽  
Shonda Bernadin
Keyword(s):  
Data Analysis ◽  
Fmri Data ◽  
Analysis Methods ◽  
Fmri Data Analysis ◽  
Data Analysis Methods
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