Toward Stopping Spread Of Coronavirus With The Help Of Big Data Density Management

at this hour, coronavirus is a threat to our life and especially to our close family and the elderly person, who have more or less poor health, it will harm human beings and economics, so we have to find a method to limit the risk of this devastating virus and to discover a way to decrease its propagation. Our concern is to compute people density on many different locations of interest in the whole city with intention of dispatching people to different areas by avoiding as much as possible congestions. The support of using big data technologies is very crucial to process data with a fast manner and in real time. Today traditional database management tools is unable to manage the voluminous data generated by different system sources, that's why big data technologies give us its support to manage this mass of data to extract crucial information from this enormous data, and without big data technologies, the process turn out very difficult to control. In the present paper, rye first propose a method that will calculate densities of people in different city's places in real time, this will allow us to prevent people locations to avoid where there are congestions and reorient them to another lightened safe locations. And then we will store in a database all human contacts that have taken place to warn people who have been in contact or who have been close to the contaminated ones.