# DVP: Data Visualization Platform

arXiv: https://arxiv.org/abs/1906.11738 · HTML: https://mlsanity.com/paper/1906.11738/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [UN-AVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring](https://mlsanity.com/paper/2111.10010/) (2021)

## Abstract (arXiv)

We identify two major steps in data analysis, data exploration for understanding and observing patterns/relationships in data; and construction, design and assessment of various models to formalize these relationships. For each step, there exists a large set of tools and software. For the first step, many visualization tools exist, such as, GGobi, Parallax, and Crystal Vision, and most recently tableau and plottly. For the second step, many Scientific Computing Environments (SCEs) exist, such as, Matlab, Mathematica, R and Python. However, there does not exist a tool which allows for seamless two-way interaction between visualization tools and SCEs. We have designed and implemented a data visualization platform (DVP) with an architecture and design that attempts to bridge this gap. DVP connects seamlessly to SCEs to bring the computational capabilities to the visualization methods in a single coherent platform. DVP is designed with two interfaces, the desktop stand alone version and the online interface. To illustrate the power of DVP design, a free demo for the online interface of DVP is available citepDVP and very low-level design details are explained in this article. Since DVP was launched, circa 2012, the present manuscript was not published since today for commercialization and patent considerations.