Ioannis Kotidis

Ioannis Kotidis is an Associate Professor in the Department of Informatics at the Athens University of Economics and Business (AUEB), School of Information Sciences and Technology. He holds a Diploma in Electrical and Mechanical Engineering from the National Technical University of Athens (1995), and Master's and Ph.D. degrees from the University of Maryland (1997, 2000). Prior to joining AUEB, he worked as a Senior Technical Specialist at AT&T Labs-Research in Florham Park, New Jersey until January 2006.

His research spans multiple areas of database systems with particular focus on On-Line Analytical Processing (OLAP) and data warehousing, data mining, sensor/P2P networks, mobile data management, data fusion & dissemination, data streams, RFID data management, approximate query answering, and database preservation. His work bridges theoretical foundations with practical implementations, as evidenced by numerous publications in top-tier database conferences and journals.

Professor Kotidis leads several significant research projects including RECOST (REal time management of COmplex STreams), DBSENSE (Information Management in Wireless Sensor Networks), INFORE (Interactive Extreme-Scale Analytics and Forecasting), and DeLorean (Storage, Indexing and Analysis Techniques for Time-Series Management). His recent work focuses on blockchain applications for decentralized OLAP processing, complex event processing frameworks, and advanced techniques for managing complex data streams.

Among his notable achievements is the best paper award at the ACM SIGMOD International Conference on Management of Data for his work on DynaMat: A Dynamic View Management System for Data Warehouses. His publications consistently address challenging problems in database systems with innovative approaches that have influenced both academic research and practical implementations.

He has supervised numerous undergraduate theses on topics including blockchain-based OLAP view management, complex event processing using FlinkCEP, and graph similarity learning. His research has attracted significant funding through various research programs at AUEB, including Basic Research Funding Programs 1 & 2.