Graphics on demand: The automatic data visualization on the WEB

Abstract

Data visualization is an effective tool for communicating the results of opinion surveys, epidemiological studies, statistics on consumer habits, etc. The graphical representation of data usually assists human information processing by reducing demands on attention, working memory, and long-term memory. It allows, among other things, a faster reading of the information (by acting on the forms, directions, colors...), the independence of the language (or culture), a better capture the attention of the audience, etc. Data that could be graphically represented may be structured or unstructured. The unstructured data, whose volume grows exponentially, often hide important and even vital information for society and companies. It, therefore, takes a lot of work to extract valuable information from unstructured data. If it is easier to understand a message through structured data, such as a table, than through a long narrative text, it is even easier to convey a message through a graphic than a table. In our opinion, it is often very useful to synthesize the unstructured data in the form of graphical representations. In this paper, we present an approach for processing unstructured data containing statistics in order to represent them graphically. This approach allows transforming the unstructured data into structured one which globally conveys the same countable information. The graphical representation of such a structured data is then obvious. This approach deals with both quantitative and qualitative data. It is based on Natural Language Processing Techniques and Text Mining. An application that implements this process is also presented in this paper.