Proposal Title

Visual Analytics of Enron Corpus with JavaScript vs Fusion Tables+Electron

Primary Faculty Mentor’s Name

Angkul Kongmunvattana

Session Format

Oral (max. 15 minutes)

Abstract

The Enron data corpus has been analyzed numerous times by various organizations and individual researchers due to its sheer volume and intrinsic uniqueness. In particular, the corpus has about 500,000 emails from 150 users, who worked as senior management of Enron. Many visual analysis methods have been utilized in order to display this data in an easy to grasp fashion. In this work, a simple Python code was implemented to process the email corpus to highlight key figures in the hidden social networks within the data. JavaScript codes and Fusion Tables were used individually to make the data readily and visually accessible on a wide variety of platforms. Finally, Electron, a simple web-enabled app, is created for showcasing the visual outputs from Fusion Tables on desktop.

Keywords

Enron, emails, data corpus, hidden data, Electron, Python, JavaScript, Fusion Tables

Presentation Year

2017

Publication Type and Release Option

Event

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Visual Analytics of Enron Corpus with JavaScript vs Fusion Tables+Electron

The Enron data corpus has been analyzed numerous times by various organizations and individual researchers due to its sheer volume and intrinsic uniqueness. In particular, the corpus has about 500,000 emails from 150 users, who worked as senior management of Enron. Many visual analysis methods have been utilized in order to display this data in an easy to grasp fashion. In this work, a simple Python code was implemented to process the email corpus to highlight key figures in the hidden social networks within the data. JavaScript codes and Fusion Tables were used individually to make the data readily and visually accessible on a wide variety of platforms. Finally, Electron, a simple web-enabled app, is created for showcasing the visual outputs from Fusion Tables on desktop.