DIGITAL ECOSYSTEM OF DATA VISUALIZATION

 

Data visualization tools reserved for experts, scientists, and professionals were open source, enabling the production and distribution of visualizations.

 

It quickly became more popular on the Web thanks to the release of free offers. Such is the case with the deployment of web services from Google. Data visualization is a visual data processing process in itself.

The process described according to the DataFlow reference model provides a methodological basis that illustrates each stage of data transformation. It applies from aggregation to visual configuration and final deployment of the visualization.

 

This nonlinear process is organized in four user-configurable steps that are subject to sequential iterations.

 

• Data collection: extraction of databases, data, browsers, etc.

• Transformation: consolidation of data in tables. It can add metadata from existing data, but format the raw data in certain formats.

• Visual configuration: the use of tables to match visual elements. This part is central to this model, called "mapping", which continues the transformation of data.

• Final views: conversion of visual representations for display in a final rendering that can be enhanced by more detailed graphic rendering.

 

Dataviz is based on a large ecosystem of tools that intervene at every stage of the data processing process.

 

Data preparation: collecting data, cleaning data, merging data

 

The preparation of the data is decisive. Conditions the meaning of dataviz for the end user. Data collection is traditionally derived from databases fed by business services.

 

It consists of extracting raw data as well as automatic retrieval of data through sensors distributed with the Internet of things.

 

More recently, scanning techniques have been developed to facilitate automatic use of data on the social network.

 

There are crawlers like Navicrawler that are used to map a domain's websites. For example, it makes it possible to collect and index all the links of a website community, thus detecting the relationships between them.

Preparing data also means cleaning it up. In this respect, we use tools such as Google Refine to sort, correct, even add metadata and apply new formats.

 

This data work is required for consolidation that can be used by visual tools. Although sometimes annoying, this step is still necessary preliminary work to ensure the quality of the data.

 

Data Analysis and Discovery

Tools devoted to visual configuration are Gephi, CartoDb, best known for visual network analysis. Geographical mapping, mathematical and statistical analysis etc. using for.

 

Visual structuring of data takes place from tables by applying algorithms (eg Force Atlas for Gephi) and special graphic parameters that make it possible to assign shapes and colors.

 

 

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In this category of tools, it allows to configure visualizations according to various visual processing criteria by loading datasets. It makes possible the appearance of many tools and services available online.

 

At this stage, the data may be the first problem they copy of the defined formatting. Or it will lead to analysis where it will cause new manipulations.

 

Interactions then change the zooms on the data to reveal patterns. It can be accomplished through filtering. Sometimes it causes unexpected discovery of new structures.

 

Herein lies one of the main contributions of visualization. It is an heuristic dimension that reveals a new meaning and leads to the emergence of new knowledge.