Exploratory data analysis on temperature data of Indian states from 1800–2013 (Analysis of trends in temperature data for prediction modelling)

2016 ◽  
Author(s):  
Aditya Agrawal ◽  
Dharvi Verma ◽  
Shilpa Gupta
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Temperature Data ◽  
Indian States ◽  
Exploratory Data ◽  
Prediction Modelling

Application of Exploratory Data Analysis (EDA) Techniques to Temperature Data in a Conventional Data Center

2007 ◽  
Author(s):  
Ratnesh Sharma ◽  
Rocky Shih ◽  
Chandrakant Patel ◽  
John Sontag
Keyword(s):  
Data Analysis ◽  
Thermal Management ◽  
High Power ◽  
Data Center ◽  
Data Centers ◽  
Exploratory Data Analysis ◽  
Temperature Data ◽  
High Density ◽  
Exploratory Data ◽  
Mission Critical

Data centers are the computational hub of the next generation. Rise in demand for computing has driven the emergence of high density datacenters. With the advent of high density, mission-critical datacenters, demand for electrical power for compute and cooling has grown. Deployment of a large number of high powered computer systems in very dense configurations in racks within data centers will result in very high power densities at room level. Hosting business and mission-critical applications also demand a high degree of reliability and flexibility. Managing such high power levels in the data center with cost effective reliable cooling solutions is essential to feasibility of pervasive compute infrastructure. Energy consumption of data centers can also be severely increased by over-designed air handling systems and rack layouts that allow the hot and cold air streams to mix. Absence of rack level temperature monitoring has contributed to lack of knowledge of air flow patterns and thermal management issues in conventional data centers. In this paper, we present results from exploratory data analysis (EDA) of rack-level temperature data collected over a period of several months from a conventional production datacenter. Typical datacenters experience surges in power consumption due to rise and fall in compute demand. These surges can be long term, short term or periodic, leading to associated thermal management challenges. Some variations may also be machine-dependent and vary across the datacenter. Yet other thermal perturbations may be localized and momentary. Random variations due to sensor response and calibration, if not identified, may lead to erroneous conclusions and expensive faults. Among other indicators, EDA techniques also reveal relationships among sensors and deployed hardware in space and time. Identification of such patterns can provide significant insight into data center dynamics for future forecasting purposes. Knowledge of such metrics enables energy-efficient thermal management by helping to create strategies for normal operation and disaster recovery for use with techniques like dynamic smart cooling.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Covid-19 Cases in India: A Visual Exploratory Data Analysis Model (Preprint)

2020 ◽  
Author(s):  
Jayesh S
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Case Fatality ◽  
Public Health Emergency ◽  
Virus Spread ◽  
Analysis Model ◽  
Case Fatality Ratio ◽  
First Case ◽  
Exploratory Data ◽  
The Government

UNSTRUCTURED Covid-19 outbreak was first reported in Wuhan, China. The deadly virus spread not just the disease, but fear around the globe. On January 2020, WHO declared COVID-19 as a Public Health Emergency of International Concern (PHEIC). First case of Covid-19 in India was reported on January 30, 2020. By the time, India was prepared in fighting against the virus. India has taken various measures to tackle the situation. In this paper, an exploratory data analysis of Covid-19 cases in India is carried out. Data namely number of cases, testing done, Case Fatality ratio, Number of deaths, change in visits stringency index and measures taken by the government is used for modelling and visual exploratory data analysis.

scholarly journals Multivariate Statistical Approach for Nephrines in Women with Obesity

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1393
Author(s):  
Ralitsa Robeva ◽  
Miroslava Nedyalkova ◽  
Georgi Kirilov ◽  
Atanaska Elenkova ◽  
Sabina Zacharieva ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Metabolic Syndrome ◽  
Data Analysis ◽  
Stress Response ◽  
Exploratory Data Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Obese Women ◽  
Exploratory Data

Catecholamines are physiological regulators of carbohydrate and lipid metabolism during stress, but their chronic influence on metabolic changes in obese patients is still not clarified. The present study aimed to establish the associations between the catecholamine metabolites and metabolic syndrome (MS) components in obese women as well as to reveal the possible hidden subgroups of patients through hierarchical cluster analysis and principal component analysis. The 24-h urine excretion of metanephrine and normetanephrine was investigated in 150 obese women (54 non diabetic without MS, 70 non-diabetic with MS and 26 with type 2 diabetes). The interrelations between carbohydrate disturbances, metabolic syndrome components and stress response hormones were studied. Exploratory data analysis was used to determine different patterns of similarities among the patients. Normetanephrine concentrations were significantly increased in postmenopausal patients and in women with morbid obesity, type 2 diabetes, and hypertension but not with prediabetes. Both metanephrine and normetanephrine levels were positively associated with glucose concentrations one hour after glucose load irrespectively of the insulin levels. The exploratory data analysis showed different risk subgroups among the investigated obese women. The development of predictive tools that include not only traditional metabolic risk factors, but also markers of stress response systems might help for specific risk estimation in obesity patients.

Sign in / Sign up

Export Citation Format

Share Document