Search algorithm of Google

·         Good morning Mrs.Gallego

- Good morning.

·         ¿ Are you graduated in industrial engineer right?

- Yes it is, I finished my studies two years ago, so we can say that I’m a novel in this theme

·         ¿ And are you making any superior study in this moment?

- Yes I’m making a master in engineer in electronic systems at the same time that I’m working in the CastFlowValves.

·         ¿ So we can say that your live turn around the mathematics world ?

 -Yes the sciences of mathematics have been part of my life for many.

·         ¿Could you told me about some application of the mathematics that we use every day in our life ?

 - Of course I still remember a project of investigation very interesting about the algorithm of searching of Google

·         ¿Could you told me a little bit about this?

- Sure, It’s about the most powerful searcher in the world and make millions of searches every day, being the 5ª web side most visited in the world.

·         ¿And how the searcher can realize all this operations?

- This is possible because of the algorithm of PageRank that it using in nowadays, even if was blurred inside a system much bigger of evaluation. Now also of this parameter it keep in main another more like the models of langue that search sentences the synonymous and It keep in main how old are the webs sides being better the information from the newer  webs .

-Here you have a little draw to show you better the algorithm

·         ¿And could you give me an easier example to understand it better?, because if wasn’t so clear for me.

-Imagine that a surest is surfing in the red,  in a moment is in page (P1), in the next moment it something bored from the context of P1  and goes to jump to a page that is connecting to P1 and lets tell that there are N1 numbers of them ¿ how exactly wich one ? So to decide which one of them goes it’s going to make a raffle, and it make it in most simple way using a regular dice (we spouse that is virtual) and it have as many face as the connections have P1 in more professional way the election of the next page follow a distribution of uniform probability. Because our model is not determinist if not probabilistic, so that we don’t now where is in the next moment but we now the probability of being in one of possible destiny, and we could use this for the next movement and the next successively.

-I hope this explanation make you understand all

·         Yes so much better, thank you very much for your time bye

- Was a pleasure for me to help you bye.