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Identifier 000441078
Title Interoperability over meteorological and spatial big data warehouse
Alternative Title Διαλειτουργικότητα σε αποθήκες μεγάλου όγκου μετεωρολογικών και χωρικών δεδομένων
Author Μπαριτάκης, Παύλος Δ.
Thesis advisor Πλεξουσάκης, Δημήτριος
Reviewer Τζίτζικας, Ιωάννης
Μαγκούτης, Κωνσταντίνος
Abstract Nowadays with climate change being a major issue in Scientific Community and to citizens too, the scientific community struggles to find causes, results and make predictions about future impacts to our lives. Having Internet Of Things, like Meteorological Sensors, like an arrow in the quiver, helps the Scientific Community collect important meteorological and spatial data in a manner of using this kind of data to build statistical and mathematical models to guide them to more accurate results, plus faster than the existing models. The frequent collection of the meteorological and spatial data from het- erogenous sources of information, drives to huge portions of data that have to be stored and managed efficiently in a sense of being useful to users by converting raw data format into knowledge. The solution to the efficient storage and management of these big portions of data was given firstly by building a data warehouse with a collection of different databases, regarding the sources of information and secondly by using a Knowledge Base Layer over the data warehouse. With this approach, we create interoperability over the data warehouse. The approach of the thesis is a Web-Based Management Information Sys- tem that uses NoSQL databases to build the Storage Layer so as the Knowledge Base Representation Layer. The Apache Cassandra DB is used as the Storage Layer and the Knowledge Base Layer implemented with the usage of Neo4j Graph DB. The combination of these two NoSQL Databases leads to a dynamic M.I.S. Web-Based Application that can handle the load of data from sensors. The Web App can be used easily from novice to more advanced users to gather and manage the data, create statistics, views and execute dynamic queries to the database warehouse to have results on demand.
Language English
Subject Μεγάλος όγκος δεδομένων
Issue date 2021-07-30
Collection   School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work--Post-graduate theses
Permanent Link https://elocus.lib.uoc.gr//dlib/b/a/e/metadata-dlib-1625732169-317679-25262.tkl Bookmark and Share
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