Unit:
Κατεύθυνση Μεγάλα Δεδομένα και Τεχνητή ΝοημοσύνηΠληροφορική
Author:
Mandamadiotis Antonios
Supervisors info:
Γεωργία Κούτρικα, Διευθύντρια Έρευνας, Ερευνητικό Κέντρο Αθηνά
Γιάννης Ιωαννίδης, Καθηγητής, Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών
Θοδωρής Δαλαμάγκας, Διευθυντής Ερευνών, Ερευνητικό Κέντρο Αθηνά
Original Title:
Interactive Recommendations in SQL Queries using Multi-Armed Bandits
Translated title:
Interactive Recommendations in SQL Queries using Multi-Armed Bandits
Summary:
SQL is the most popular and easy to use language for querying and analyzing large volumes of data in databases. Nowadays, databases are complex, containing several tables and attributes, making data exploration tasks difficult even for expert users. Our aim is to assist users in discovering the most interesting tables and attributes, while they are forming their queries. For that purpose, a recommendation system is proposed in the form of auto-completion. The proposed system uses Multi-Armed Bandits, a category of algorithms that balance exploration and exploitation, which are very suitable for interactive online learning. Furthermore, user information from past interactions can be used to create a context, making the recommendations personalized. Our system provides recommendations that speed up the process of writing SQL statements while learning from user feedback.
Main subject category:
Technology - Computer science
Keywords:
machine learning, reinforcement learning, multi-armed bandits, recommendations
File:
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Thesis_Antonis_Mandamadiotis.pdf
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File access is restricted only to the intranet of UoA.
