Ανάλυση δεδομένων σε κατανεμημένα συστήματα
πραγματικού χρόνου

Τζίμα, Σοφία; Tzima, Sofia

dc.contributor.author	Τζίμα, Σοφία	el
dc.contributor.author	Tzima, Sofia	en
dc.date.accessioned	2016-01-27T10:24:40Z
dc.date.available	2016-01-27T10:24:40Z
dc.date.issued	2016-01-27
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/41875
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.11155
dc.rights	Αναφορά Δημιουργού-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.subject	Συστήματα πραγματικού χρόνου	el
dc.subject	Μοντέλο λανθάνουσας κατανομής Dirichlet	el
dc.subject	Πιθανολογικά θεματικά μοντέλα	el
dc.subject	Κοινωνικά δίκτυα	el
dc.subject	Real-time systems	en
dc.subject	Latent Dirichlet allocation	el
dc.subject	Social networks	el
dc.subject	Sentiment analysis	el
dc.subject	Probabilistic topic models	el
dc.subject	Ανάλυση συναισθήματος	el
dc.title	Ανάλυση δεδομένων σε κατανεμημένα συστήματα πραγματικού χρόνου	el
heal.type	bachelorThesis
heal.classification	Computer engineering	el
heal.language	el
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2015-03-27
heal.abstract	The scope of this thesis is the study and development of real-time systems, which process large collections of documents and draw conclusions about their content and emotion. We studied different nature algorithms in order to determine both the performance of the used tools and the real-time response of the algorithm, using metrics such as memory usage, the amount of data units processed per second, as well as the responsiveness of the system under severe time constraints. The usefulness of real-time systems that process document collections, in order to draw conclusions about their content, becomes obvious if we consider the raise of social networks as modern forms of communication and expression. The analysis of user created content in social networks, allows us to compute useful statistics about the feeling that prevails in public opinion around a particular theme. During the study, algorithms from the areas of probabilistic topic modelling and sentiment analysis were analyzed. We implemented those algorithms using Apache Storm, and created topologies that run endlessly in a Storm cluster. Those topologies accept data as a stream of events and export real-time information about them. Such systems are capable of monitoring events and making immediate decisions, based on them. (el)	en
heal.advisorName	Βαρβαρίγου, Θεοδώρα	el
heal.committeeMemberName	Λούμος, Βασίλειος	el
heal.committeeMemberName	Καγιάφας, Ελευθέριος	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Τεχνολογίας Πληροφορικής και Υπολογιστών. Εργαστήριο Συστημάτων Βάσεων Γνώσεων και Δεδομένων	el
heal.academicPublisherID	ntua
heal.numberOfPages	80 σ.	el
heal.fullTextAvailability	true