Nonparametric Transformations for Data Correlation and Integration

1999 ◽  
pp. 381-396 ◽  
Keyword(s):  
Data Correlation

Solubility Measurement and Data Correlation of Pentaerythritol in Four Binary Solvent Systems at Temperatures from 283.15 to 323.15 K

2021 ◽  
Author(s):  
Shaonan Zhang ◽  
Ningning Tian ◽  
Shengzhe Jia ◽  
Jing Yang ◽  
Yu Zhou ◽  
...  
Keyword(s):  
Binary Solvent ◽  
Data Correlation ◽  
Solubility Measurement ◽  
Binary Solvent Systems ◽  
Solvent Systems

Exploring Data Correlation between Feature Pairs for Generating Constraint-based Adversarial Examples

2020 ◽  
Author(s):  
Yunzhe Tian ◽  
Yingdi Wang ◽  
Endong Tong ◽  
Wenjia Niu ◽  
Liang Chang ◽  
...  
Keyword(s):  
Data Correlation ◽  
Adversarial Examples

scholarly journals Imaging Biomarkers and Gene Expression Data Correlation Framework for Lung Cancer Radiogenomics Analysis Based on Deep Learning

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Dong Sui ◽  
Maozu Guo ◽  
Xiaoxuan Ma ◽  
Julian Baptiste ◽  
Lei Zhang
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Deep Learning ◽  
Gene Expression Data ◽  
Imaging Biomarkers ◽  
Expression Data ◽  
Data Correlation

Demonstration of QPSK data correlation and equalization using a tunable optical tapped delay line based on orbital angular momentum mode delays

2022 ◽  
Vol 503 ◽  
pp. 127438
Author(s):  
Ahmed Almaiman ◽  
Hao Song ◽  
Amir Minoofar ◽  
Haoqian Song ◽  
Runzhou Zhang ◽  
...  
Keyword(s):  
Angular Momentum ◽  
Orbital Angular Momentum ◽  
Delay Line ◽  
Data Correlation ◽  
Momentum Mode

scholarly journals CoPart: a context-based partitioning technique for big data

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sara Migliorini ◽  
Alberto Belussi ◽  
Elisa Quintarelli ◽  
Damiano Carra
Keyword(s):  
Big Data ◽  
Quality Criteria ◽  
Query Execution ◽  
Data Correlation ◽  
Huge Amount ◽  
Programming Paradigm ◽  
Computation Node ◽  
Contextual Dimension ◽  
Partitioning Technique ◽  
The Way

AbstractThe MapReduce programming paradigm is frequently used in order to process and analyse a huge amount of data. This paradigm relies on the ability to apply the same operation in parallel on independent chunks of data. The consequence is that the overall performances greatly depend on the way data are partitioned among the various computation nodes. The default partitioning technique, provided by systems like Hadoop or Spark, basically performs a random subdivision of the input records, without considering the nature and correlation between them. Even if such approach can be appropriate in the simplest case where all the input records have to be always analyzed, it becomes a limit for sophisticated analyses, in which correlations between records can be exploited to preliminarily prune unnecessary computations. In this paper we design a context-based multi-dimensional partitioning technique, called CoPart, which takes care of data correlation in order to determine how records are subdivided between splits (i.e., units of work assigned to a computation node). More specifically, it considers not only the correlation of data w.r.t. contextual attributes, but also the distribution of each contextual dimension in the dataset. We experimentally compare our approach with existing ones, considering both quality criteria and the query execution times.

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