Πλοήγηση ανά Επιβλέποντα "Chatziantoniou, Damianos"

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Α Β Γ Δ Ε Ζ Η Θ Ι Κ Λ Μ Ν Ξ Ο Π Ρ Σ Τ Υ Φ Χ Ψ Ω

Τώρα δείχνει 1 - 2 από 2

Data migration from On-Premise to Cloud: a strategic overview of methods, providers, and AI-driven optimization
(2025-05-08) Avgoustinos, Leonidas; Αυγουστίνος, Λεωνίδας; Louridas, Panagiotis; Karlis, Dimitrios; Chatziantoniou, Damianos
Η μελέτη αυτή δίνει προσοχή στον χρηματοπιστωτικό κλάδο με ιδιαίτερη έμφαση στον τρόπο με τον οποίο οι τράπεζες διαχειρίζονται τη μετάβαση από την αποθήκευση On Premise σε λύσεις Cloud. Η μελέτη θα εξετάσει παραδείγματα του πραγματικού κόσμου και θα συλλέξει πληροφορίες από επαγγελματίες του κλάδου για να δείξει τις βέλτιστες πρακτικές, τις προκλήσεις που αντιμετωπίζουν και το ρόλο των σύγχρονων τεχνολογιών στην επίλυση αυτών των προκλήσεων. Τα ευρήματα είναι σημαντικά για τους οργανισμούς στην Ελλάδα, καθώς προσπαθούν να να υιοθετήσουν και να προωθήσουν τις υπηρεσίες που βασίζονται στο νέφος.
Introducing a novel model for data portability in heterogeneous data environments
(2025-04-09) Vratsanou, Lida; Βρατσάνου, Λήδα; Karlis, Dimitrios; Louridas, Panagiotis; Chatziantoniou, Damianos
Modern data ecosystems are characterized by high complexity, rendering data portability difficult, a foundational principle of regulations such as the GDPR. Traditional data management approaches like data warehousing and ETL pipelines cannot provide the flexibility required for frictionless data transfers, user control, and interoperability. This thesis presents Data Virtual Machines (DVMs) as a new graph-based conceptual model that enables efficient, scalable, and user-oriented data portability for heterogeneous data. The research begins with the exploration of the technical and non-technical data portability challenges such as usability, interoperability, extensibility, scalability, regulatory compliance, and data security. It then examines related work on personal data control, integration of data, virtualization, and sector-specific frameworks and identifies gaps in aspects covered by DVMs. The contribution of the thesis is the presentation of DVMs, with their structure, query language, and key ideas as a practicable solution for data portability. DVMs follow a data-driven and flexible modeling approach compared to traditional rigid schemas. They allow schema reorientation and query optimization in order to achieve any-entity view and model polymorphism. The DataMingler tool is a significant practical contribution and through a real-life use case we will illustrate its feasibility for data portability for the financial sector. Its multiple strengths include the ability to derive the schema from the data, visually represent queries and effectively integrate data, through a simple interface suitable for both technical and non-technical users. The thesis thus illustrates how DVMs simplify data extraction, transformation, and transfer, thus making data more accessible, governed, and compliant. This thesis positions DVMs as a scalable, flexible, and regulatory compliant paradigm for future-proof data portability solutions, bridging the gap between technical sophistication and end-user empowerment.