• The 13th International ACM Conference on Management of Digital EcoSystems (MEDES'21) November 2021, Hammamet - Tunisia (Virtual Event)

  • Paper Submission Deadline: July 16th, 2021 (CLOSED)

  • Author Notification: August 16th, 2021 (SENT)

  • Hammamet, Tunisia (VIRTUAL EVENT)

Keynote Speaker
University of Bozen-Bolzano, Italy
Keynote Speaker
University of Malta, Malta
Keynote Speaker
Imperial College London, UK

Key Challenges in Processing Temporal and Time Series Data

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by Johann Gamper

University of Bozen-Bolzano, Italy

Abstract

Temporal data is ubiquitous, and the importance of considering time for data analysis has been recognized in different communities. The SQL:2011 standard introduced some temporal features (e.g., the possibility to associate a valid and/or transaction timestamp with a relation), and commercial DBMSs have started to offer temporal functionalities in a step-by-step manner. Another field with an inherent temporal dimension is the processing of time series data, which represent the most important data type for monitoring the dynamics of a changing world and occur in almost all application domains. This presentation aims at sheding some light on these two aspects of time in the context of data analysis. We begin with an overview about the first relational database management system that offers comprehensive support for sequenced temporal queries. The second part is dedicated to the processing and analysis of time series data, focusing on aspects that have not been in the mainstream of past research. The presentation concludes with an outlook on future challenges that need to be solved in order to exploit the full potential of temporal data analysis.

Biography

Johann Gamper is professor and head of the database systems group at the Free University of Bozen-Bolzano. Since 2018 he also serves as Vice-Rector for Research. He has a MSc degree in Computer Science from the TU Vienna and a PhD degree from the RWTH Aachen. His research concentrates on database technologies for advanced query processing and data analytics, with a specific focus on temporal data, time series, and graphs. Many of his research activities emerge from projects with different application domains. Johann Gamper is author of 120+ publications in international journals and conference proceedings, including the most prestigious outlets of the database community (TODS, VLDBJ, TKDE, SIGMOD, VLDB, ICDE). He is member of the ADBIS Steering Committee and regularly serves as reviewer, PC member, and conference organizer, e.g., Demo Co-chair ICDE 2021, General Co-chair SOFSEM 2021, PC Co-chair TIME 2019, General Chair SSDBM 2018.

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The Virtuous Loop of Artificial Intelligence and Games

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by Georgios N Yannakakis

University of Malta, Malta

Abstract

Ever since the birth of the idea of artificial intelligence (AI), games have been helping AI research to advance. Games not only pose interesting and complex problems for AI to solve, they also offer a rich canvas for creativity and expression. This rare domain where science meets art and interaction offers unique properties for the study of AI and is the key driver of technical progress and AI breakthroughs including deep learning and artificial general intelligence. It is not only AI that advances through games, however; AI has been helping games to advance across several fronts: in the way we play them, in the way we understand their inner functionalities, in the way we design them, and in the way we understand play, interaction and creativity. As games get increasingly richer and more complex through creative AI processes, AI advances further and in turn, it advances the environments it is trained in a continuous co-(r)evolutionary virtuous loop. Video games are arguably the most important domain to develop AI for, while AI is arguably the most important technological leap forward for games.

Biography

Georgios N. Yannakakis is a Professor and Director of the Institute of Digital Games, University of Malta, co-founder of modl.ai, and an Associate Professor at the Technical University of Crete. He does research at the crossroads of artificial intelligence, computational creativity, affective computing, advanced game technology, and human-computer interaction and he has published over 260 journal and conference papers in the aforementioned fields (h-index 53). His research has been supported by numerous national and European grants and has appeared in Science Magazine and New Scientist among other venues. He has been involved in a number of journal editorial boards and he is currently an Associate Editor of the IEEE Transactions on Games and the IEEE Transactions on Evolutionary Computation journals (among others). Prof. Yannakakis has been the General Chair of key conferences in the area of game artificial intelligence (IEEE CIG 2010) and games research (FDG 2013, FDG 2020). He is the co-author of the Artificial Intelligence and Games textbook and the co-organiser of the Artificial Intelligence and Games summer school series.

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Developing A Digital Platform for Remote Healthcare Monitoring

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by Payam Barnaghi

Imperial College London, UK

Abstract

Transforming the healthcare delivery and enabling preventative and predictive measures can enhance the quality of care services and reduce the cost of monitoring and management of different healthcare conditions. For example, in the UK, over 22% of all hospital admissions in people with dementia are due to preventable causes. The continuous analysis of remote monitoring data and digital biomarkers will allow for more rapid and accurate predictive risk analysis, condition and symptom management, and timely interventions based on personalised models. However, several engineering challenges need to be overcome to harness these new types of data for healthcare purposes. Real-world observations and measurements collected by IoT technologies and wearable devices come from uncontrolled and noisy environments. The sensory data is often less accurate than controlled, experimental measurements, more noisy, and incomplete because of missing data. Environmental and activity data is also dynamically changing due to variations in an individual's activity and changes in the environment, seasonal effects, and measurement conditions. It is crucial to develop new adaptive and robust methods for real-time healthcare data analysis and assessment that yield robust insights with acceptable accuracy. This talk will discuss developing a digital platform to integrate and evaluate the applicability of algorithmic and engineering developments to real-world healthcare problems. It will explore multimodal data integration, machine learning and analytical modelling, reproducibility, and interpretability as part of the healthcare digital platform design and development.

Biography

Payam Barnaghi is Chair of Machine Intelligence Applied to Medicine in the Department of Brain Sciences at Imperial College London. He is Co-PI and Deputy Head of Care Research and Technology Centre at the UK Dementia Research Institute (UK DRI). His main research goal is to develop AI and machine learning solutions for healthcare and create affordable and scalable digital systems than can be applied across a range of health conditions. He works on machine learning, Internet of Things (IoT), semantic computing, adaptive algorithms and computational neuroscience to solve problems and develop new technologies for the future healthcare systems.

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