Spark-based loyalty reward system for an energy company

Καρποντίνης, Δημήτρης; Karpontinis, Dimitris

dc.contributor.author	Καρποντίνης, Δημήτρης	el
dc.contributor.author	Karpontinis, Dimitris	el
dc.date.accessioned	2025-01-09T08:39:51Z
dc.date.available	2025-01-09T08:39:51Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/60670
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.28366
dc.description	Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) "Επιστήμη Δεδομένων και Μηχανική Μάθηση"	el
dc.rights	Αναφορά Δημιουργού 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/gr/	*
dc.subject	Data Mining	en
dc.subject	Cloud Computing	en
dc.subject	Machine Learning	en
dc.subject	Big Data	en
dc.title	Spark-based loyalty reward system for an energy company	en
heal.type	masterThesis
heal.classification	Data Science	en
heal.language	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2024-07-23
heal.abstract	Providing rewards to loyal customers is a common technique that companies employ in order to maintain and boost their clientele. In this work a system for providing certified green energy to a selection of clients has been implemented. This selection process relies on the client’s needs, their loyalty as well as their attitude towards ecology, with an emphasis on transparency and adaptability to include as many clients as possible. This system was implemented using cloud computing services and big data frameworks. The data utilized are based on the customer consumption, the available produced energy and other client features with were identified using data extraction techniques. Finally, components of the algorithm were also implemented locally, in order to be later deployed on edge devices.	el
heal.advisorName	Κόλλιας, Στέφανος	el
heal.committeeMemberName	Βουλόδημος, Αθανάσιος	el
heal.committeeMemberName	Στάμου, Γιώργος	el
heal.committeeMemberName	Κόλλιας, Στέφανος	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	56 σ.	el
heal.fullTextAvailability	false