Boosting meterological statistics and analysis with big data assimilation: clusterization of a large amount of weather data

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dc.contributor.advisor Orsini, Renzo it_IT
dc.contributor.author Rachello, Fabio <1989> it_IT
dc.date.accessioned 2017-10-09 it_IT
dc.date.accessioned 2018-04-17T13:34:18Z
dc.date.available 2018-04-17T13:34:18Z
dc.date.issued 2017-10-26 it_IT
dc.identifier.uri http://hdl.handle.net/10579/11508
dc.description.abstract For a meterological monitoring center, it’s very important the presence of an archive with meteorological data of the past that is however continuously updated with new data added daily and hourly. This data is fundamentally used to make analysis and statistics about old events and not simply for meteorological forecasting. Storing a huge quantity of data with an usual relational database is obviously possible but more data is stored more time is necessary in order that the query, used to retrieve requested information, has been elaborated. We firstly present an analysis of the database used nowadays by a meterological monitoring center both the same database with more stored data in order to make a prediction of the future situation about the response time, secondly we consider a Big Data structure and we propose a solution to improve the response time, comparing the previous situation based on a relational database with the system based on NOSQL. it_IT
dc.language.iso en it_IT
dc.publisher Università Ca' Foscari Venezia it_IT
dc.rights © Fabio Rachello, 2017 it_IT
dc.title Boosting meterological statistics and analysis with big data assimilation: clusterization of a large amount of weather data it_IT
dc.title.alternative Boosting meteorological statistics and analysis with big data assimilation: clusterization of a large amount of weather data it_IT
dc.type Bachelor Thesis it_IT
dc.degree.name Informatica - computer science it_IT
dc.degree.level Laurea magistrale it_IT
dc.degree.grantor Dipartimento di Scienze Ambientali, Informatica e Statistica it_IT
dc.description.academicyear 2016/2017, sessione autunnale it_IT
dc.rights.accessrights openAccess it_IT
dc.thesis.matricno 832795 it_IT
dc.subject.miur INF/01 INFORMATICA it_IT
dc.description.note it_IT
dc.degree.discipline it_IT
dc.contributor.co-advisor it_IT
dc.date.embargoend it_IT
dc.provenance.upload Fabio Rachello (832795@stud.unive.it), 2017-10-09 it_IT
dc.provenance.plagiarycheck Renzo Orsini (orsini@unive.it), 2017-10-23 it_IT


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