A comprehensive review of tools for exploratory analysis of tabular industrial datasets

Ghosh, Aindrila; Nashaat, Mona; Miller, James; Quader, Shaikh; Marston, Chad

doi:10.1016/j.visinf.2018.12.004

Cited by 38 publications

(21 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…EDA has been used in multiple sectors, also in construction for many years, relying on a wide range of techniques and tools [17], from basic statistical exploration and visualization to more sophisticated attribute transformations such as Principal Component Analysis (PCA), Factor Analysis (FA) and Canonical Correspondence Analysis (CCA). In waste management practice, PCA, because of its power and simplicity, is preferentially used over FA, ([18] reports little difference between PCA and FA).…”

Section: State Of the Art In Exploratory Data Analysis And Data Envelmentioning

confidence: 99%

“…(ii) univariate data analysis to characterize the data in the dataset; (iii) detect interactions among attributes by performing bivariate and multivariate analysis; (iv) detect and minimize impact of missing and aberrant values; (v) detect outliers (further analysis or errors); and, finally, (vi) feature engineering, where features are transformed or combined to generate new features. There is a large number of tools for performing EDA (50 of them are analyzed in [17]) with different functionalities to assist both with the identification of hidden patterns and correlations among attributes, but also with the formulation of hypotheses from the data and their validation. EDA can also be performed using R (used in our research work, together with Datawrapper visualization), python or any other programming language oriented to data preparation, exploration, and visualization.…”

Section: Exploratory Data Analysis (Eda) For Assessing the Efficiencymentioning

confidence: 99%

“…In the CE waste management is the last solution, and, as for recycling, it is usually measured using ratios, both total waste and of the recyclable part. There is a large number of tools for performing EDA (50 of them are analyzed in [17]) with different functionalities to assist both with the identification of hidden patterns and correlations among attributes, but also with the formulation of hypotheses from the data and their validation. EDA can also be performed using R (used in our research work, together with Datawrapper visualization), python or any other programming language oriented to data preparation, exploration, and visualization.…”

Section: New Indicators For Assessing the Efficiency And Sustainabilimentioning

confidence: 99%

See 2 more Smart Citations

Exploratory Data Analysis and Data Envelopment Analysis of Construction and Demolition Waste Management in the European Economic Area

et al. 2020

View full text Add to dashboard Cite

This paper deals with the efficiency and sustainability of Construction and Demolition Waste (CDW) management in 30 Member States of the European Economic Area (EEA) (the 28 European Union countries plus Norway and Iceland) for the period 2010–2016 using Exploratory Data Analytics (EDA) and Data Envelopment Analysis (DEA). The first stage of the proposed methodology is EDA with already available (the CDW recovery rate) and suggested indicators (e.g., building stock characterization, dwelling occupancy ratio, macroeconomic ratios and CDW breakdown) to characterize the efficiency and sustainability of CDW management. The second stage is to assess the efficiency of countries using DEA through two original CDW production models, one for sustainability, measuring the efficiency of the construction sector for reducing its CDW, and the second a model to score the efficiency of maximizing the CDW recovery rate. The main outcome of the paper is the proposed methodology, which is a candidate for replacing current indicators in order to evaluate the performance of CDW policy, due to is adaptive nature, promoting the continuous improvement and overcoming the limitations of the poor quality of metrics, data and parametric indicators. The methodology has been experimentally validated using Eurostat data for 30 Member States of EEA, ranking them according to the two DEA model scores, to point out the countries considered efficient among those of their scale, as a reference for sustainable and efficient practices.

show abstract

Section: State Of the Art In Exploratory Data Analysis And Data Envelmentioning

confidence: 99%

Section: Exploratory Data Analysis (Eda) For Assessing the Efficiencymentioning

confidence: 99%

Section: New Indicators For Assessing the Efficiency And Sustainabilimentioning

confidence: 99%

See 1 more Smart Citation

Exploratory Data Analysis and Data Envelopment Analysis of Construction and Demolition Waste Management in the European Economic Area

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Aindrila Ghosha et al [1] have examined the different data exploration tool for exploratory analysis. They have described some of the data exploration tool.…”

Section: Literature Surveymentioning

confidence: 99%

Exploratory Data Analysis using Python

Sahoo*¹,

Samal²,

Pramanik³

et al. 2019

IJITEE

View full text Add to dashboard Cite

Abstract: Data need to be analyzed so as to produce good result. Using the result decision can be taken. For example recommendation system, ranking of the page, demand fore casting, prediction of purchase of the product. There are some leading companies where the review of the customer plays a great role to analyze the factor which influences the review rating. We have used exploratory data analysis (EDA) where data interpretations can be done in row and column format. We have used python for data analysis. it is object oriented ,interpreted and interactive programming language. it is open source with rich sets of libraries like pandas, MATplotlib, seaborn etc. We have used different types of charts and various types of parameter to analyzeAmazon review data sets which contains the reviews of electronic data items. We have used python programming for the data analysis.

show abstract

“…Big data visualization is a broad area; we give an overview of the closest related work below. For more information, we refer the reader to several surveys in the area [82,42,41,8,9]. Compared to published systems, Hillview achieves the best scalability for the amount of resources: we are not aware of any system that can handle a trillion cells with only 8 servers.…”

Section: Related Workmentioning

confidence: 99%

Hillview

et al. 2019

View full text Add to dashboard Cite

Hillview is a distributed spreadsheet for browsing very large datasets that cannot be handled by a single machine. As a spreadsheet, Hillview provides a high degree of interactivity that permits data analysts to explore information quickly along many dimensions while switching visualizations on a whim. To provide the required responsiveness, Hillview introduces visualization sketches, or vizketches, as a simple idea to produce compact data visualizations. Vizketches combine algorithmic techniques for data summarization with computer graphics principles for efficient rendering. While simple, vizketches are effective at scaling the spreadsheet by parallelizing computation, reducing communication, providing progressive visualizations, and offering precise accuracy guarantees. Using Hillview running on eight servers, we can navigate and visualize datasets of tens of billions of rows and trillions of cells, much beyond the published capabilities of competing systems. PVLDB Reference Format:

show abstract

A comprehensive review of tools for exploratory analysis of tabular industrial datasets

Cited by 38 publications

References 37 publications

Exploratory Data Analysis and Data Envelopment Analysis of Construction and Demolition Waste Management in the European Economic Area

Exploratory Data Analysis and Data Envelopment Analysis of Construction and Demolition Waste Management in the European Economic Area

Exploratory Data Analysis using Python

Hillview

Contact Info

Product

Resources

About