Lead Institution: AUTH

Location: Grand Serai Hotel, Ioannina

Program

The EDBT series of conferences is an established and prestigious forum for the exchange of the latest research results in data management. Held in an attractive European location, the conference provides unique opportunities for database researchers, practitioners, developers, and users to explore new ideas, techniques, and tools, and to exchange experiences. The conference took place from 28th March - 31st March 2023 in Ioannina, Greece, and ran in Hybrid mode so that participants having travel restrictions still had the opportunity to attend. All ESR RAIS fellows had the chance to attend the conference and further communicate their research and enhance their network. During the conference week, there were given the following keynote talks:

Keynote 1: “Centrality Fairness in Networks” by Panayiotis Tsaparas (University of Ioannina)

Abstract: Algorithmic systems that exploit large datasets are increasingly being used to assist or even replace human decision making, a fact that has raised concerns about the trustworthiness of such systems. Algorithmic fairness addresses such concerns. In this talk, we focus on centrality fairness in networks, and specifically on PageRank fairness. PageRank assigns a probability to each node in a network that signifies the importance of the node. Given a network where nodes belong to groups, we study fairness in terms of balance in the probability mass assigned to each group. We first present fairness-aware PageRank algorithms that achieve fairness with minimum utility loss with respect to the original PageRank algorithm. Then, we take a different approach where instead of modifying the PageRank algorithm, we modify the network to make the Pagerank algorithm more fair. Concretely, we derive analytical formulas for the contribution of edge additions and deletions to fairness, and we use them to design link recommendation algorithms for maximizing fairness. We also perform an experimental study of Pagerank fairness and our algorithms using real and synthetic networks.

Short Bio: Panayiotis Tsaparas is Associate Professor at the Department of Computer Science and Engineering at University of Ioannina. He received his Ph.D. from University of Toronto in 2003. Before joining University of Ioannina, he worked as a post-doctoral fellow at University of Rome, “La Sapienza” and at University of Helsinki, and as a researcher at Microsoft Research. His research interests include Data Mining & Machine Learning, Social Network Analysis, and Algorithmic Fairness. He is a Senior ACM member, and he has served several times as a PC and Senior PC member, and reviewer for premier Data Mining and Data Bases conferences and journals, while he was PC co-Chair for WSDM 2023. He is currently associate editor for the TKDE and OSNM journals. He has published 71 papers in peer-reviewed conferences and journals, and has filed for 12 patents, 8 of which have been awarded.

Keynote 2: “Using mobile network data to color epidemic risk maps” by Nikolaos Laoutaris (IMDEA, Spain)

Abstract:  In this talk we will describe a series of methods for using mobile network data to detect potential COVID-19 hospitalizations and derive corresponding epidemic risk maps. We have applied our methods to a dataset from more than 2 million cellphones, collected by a mobile network provider located in London, UK. The approach yields a 98.6% agreement with released public records of patients admitted to NHS hospitals. Analyzing the mobility pattern of these individuals prior to their potential hospitalization, we will present a series of risk maps. Compared with census-based maps, our risk maps indicate that the areas of highest risk are not necessarily the most densely populated ones and may change from day to day. Finally, we have observed that hospitalized individuals tended to have a higher average mobility than non-hospitalized ones. Overall, our work demonstrates that crucial epidemic risk maps can be extracted from data already available at mobile operators in a safe, privacy preserving manner, that does not require end user involvement. 

Short Bio: Nikolaos Laoutaris is a research professor at IMDEA Networks Institute in Madrid, and director of its Data Transparency Group (DTG). Prior to that he was director of data science at Eurecat and chief scientist of the Data Transparency Lab which he co-founded in 2014 during his 10 year tenure as a researcher in Telefonica. Before that, Nikolaos was a postdoc fellow at Harvard University, a Marie Curie postdoc fellow at Boston University, and a PhD student in computer science at the University of Athens. His main research interests include privacy, transparency, data protection, economics of networks and information, intelligent transportation, distributed systems, protocols, and network measurements. More information at: https://networks.imdea.org/team/research-groups/data-transparency-group/ and https://laoutaris.info/.

Three full papers and three short papers presentations were carried out reflecting emerging topics of great interest in Data Analytics and Ubiquitous Computing:  

• Full paper 1: Minimal wearable setup via sensor correlation: a case study of field hockey players, George Ioannou (ESR11), Andrei Kazlouski (ESR5), Thomas Marchioro (ESR6), Maarten Gijssel

• Full paper 2: Unveiling technology clusters and prominent investors of home automation networking through patent analysis, Konstantinos Charmanas, Konstantinos Georgiou, Nikolaos Mittas, Lefteris Angelis

• Full paper 3: Privacy-preserving Decentralized Learning of Knowledge Graph Embeddings, Anh-Tu Hoang, Ahmed Lekssays, Barbara Carminati, Elena Ferrari

• Short paper 1: Seek and Go: Data, Algorithms, and Interactive Tools for Pedestrian Navigation, Kyriakos Koritsoglou, Petros Laskas, Vaios Patras, Ioannis Fudos

• Short paper 2: ProximIoT: A Proximity-based Product Marketing Platform, Petros Manousis, Dimitris Louvaris, George Kelantonakis, Chris Zeginis, Kostas Magoutis, Konstantinos Lampropoulos, Konstantinos Kalampokis, Stelios Gkouskos

• Short paper 3: A Framework for Biodiversity Image Analysis using Machine Learning and Crowdsourcing Knowledge, Loukas Chatzivasili, Georgia Charalambous, Maria Papoutsoglou, Georgia Kapitsaki, Konstantinos Markakis, Ioustina Harasim, Kostas Magoutis, Eva Chatzinikolaou, Georgia Sarafidou, Ioannis Rallis, Markos Digenis

Photos