BDA 2021

37ème Conférence sur la Gestion de Données – Principes, Technologies et Applications

25-28 octobre 2021, en ligne et Ecole Normale Superieure de Paris

Les 37èmes journées de la conférence BDA « Gestion de Données – Principes, Technologies et Applications », rendez-vous incontournable de la communauté gestion de données en France, auront lieu cette année en ligne et à Paris, du 25 au 28 octobre 2021, dans le campus de l’Ecole normale supérieure, au cœur du Quartier latin (45, rue d’Ulm Paris). La conférence se déroulera entièrement en ligne le 25, 26 et 27 octobre, et physiquement à l’ENS Paris,  le 28 octobre 2021.

La recherche en gestion de données n’a jamais été aussi active, variée, ouverte sur d’autres champs de l’informatique et, au-delà, sur les grands défis des applications modernes. Poursuivant la tradition de rencontres annuelles de la communauté de gestion de données francophone, BDA 2021 invite académiques et industriels à soumettre leurs travaux récents pour rendre compte des défis et des avancées scientifiques et industrielles dans ce domaine en pleine effervescence.

Conférences invitées

Anastasia Ailamaki Nothing is for granted: Making wise decisions using real-time intelligence

In today’s ever-growing demand for fast, data-driven decisions, heterogeneity severely undermines performance and fragments efforts for building unified data exploration tools. The variety in data formats and workloads forces data pipelines to be manually split across a variety of task-specialized systems and combined through expensive ETL and orchestration processes, or to adapt both the data and the workloads to match the requirements of a single-system, sacrificing expressiveness and structural information. Furthermore, the ever-increasing hardware heterogeneity causes task-based specialization of the tools to specific hardware such as CPUs or GPUs, forcing a trade-off: designing optimized hardware often means wasting accelerator-level parallelism (ALP) opportunities or tolerating slow and unnecessary communication between devices. In general, data processing is adapted to the pre-determined data processing system architecture, losing valuable information in the translation.

Real-time intelligence means to make all decisions during execution, when all relevant information is available for optimal utilisation of resources, while it also learns and extracts information about the query requests, instead of depending on pre-determined workload expectations. I will show how designing top-down the system architecture to allow a data- and workload-driven just-in-time specialization enables fast query execution over unprepared, potentially dirty data without time consuming preparation, as well as efficient orchestration and utilization of heterogeneous hardware devices.
Anastasia Ailamaki is a Professor of Computer and Communication Sciences at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland and the co-founder of RAW Labs SA, a Swiss company developing real-time analytics infrastructures for heterogeneous big data from multiple sources. She earned a Ph.D. in Computer Science from the University of Wisconsin-Madison in 2000. She received the 2019 ACM SIGMOD Edgar F. Codd Innovations and the 2020 VLDB Women in Database Research Award. She is also the recipient of an ERC Consolidator Award (2013), the Finmeccanica endowed chair from the Computer Science Department at Carnegie Mellon (2007), a European Young Investigator Award from the European Science Foundation (2007), an Alfred P. Sloan Research Fellowship (2005), an NSF CAREER award (2002), and ten best-paper awards in database, storage, and computer architecture conferences. She is an ACM fellow, an IEEE fellow, the Laureate for the 2018 Nemitsas Prize in Computer Science, and an elected member of the Swiss, the Belgian, the Greek, and the Cypriot National Research Councils. She is a member of the Academia Europaea and of the World Economic Forum Expert Network.
Georgia Koutrika The Rise of Intelligent Data Assistants: Democratizing Data Access


For many, data is considered the 21st century’s most valuable commodity growing at an exponential rate – but is it for everyone? Analysts exploring data sets for insight, scientists looking for patterns, and consumers looking for information are just a few examples of user groups that need to access and dig into data. However, existing data exploration tools are falling behind in bridging the chasm between data and users, making data exploration intended only for the few. In this talk, we will discuss about what it takes to bridge this chasm and the new generation of intelligent data exploration tools that are emerging at the intersection of data management, natural language processing, machine learning and visualization. The talk will end with a summary of open questions on intelligent data assistants.


Georgia Koutrika is a Research Director at Athena Research Center in Greece. She has more than 15 years of experience in multiple roles at HP Labs, IBM Almaden, and Stanford. She has received a PhD and a diploma in Computer Science from the Department of Informatics and Telecommunications, University of Athens, Greece. Her work focuses on data exploration, recommendations, and data analytics, and has been incorporated in commercial products, described in 14 granted patents and 26 patent applications in the US and worldwide, and published in more than 90 papers in top-tier conferences and journals. Georgia is an ACM Senior Member, IEEE Senior Member, and ACM Distinguished Speaker. Her recent academic activities include: Editor-in-chief for VLDB Journal, PC co-chair for VLDB 2023, Co-EiC of Proceedings of the VLDB (PVLDB) Vol 16, and associate editor for TKDE, SIGMOD2022 and VLDB2022.

Benjamin Nguyen Privacy Preserving Contact Tracing : a case of privacy driven data analysis


Classical contact-tracing is a technique during an epidemy to slow down contamination. Classical contact tracing is organized by medical personnel during an (intrusive) interview with a patient suffering from the disease, in order to trace back all the other people whom the patient was in contact with, in order to informe them of the possibility of contamination.

During the Covid19 pandemic, contact tracing has been pushed to a new level, via automation. However, due to the sensitive nature of the information that is manipulated in order to achieve good tracing, building a privacy preserving solution, while still maintaining the quality of the results proved to be a compelling research challenge. During this presentation, we will present the data management and privacy issues around privacy preserving contact tracing, and discuss in more detail the two main solutions deployed in Europe, the French lead ROBERT (aka StopCovid/TousAntiCovid) proposal, and the Swiss lead DP3T, later adapted by Apple and Google.

Bio :

Benjamin Nguyen graduated from ENS Cachan in 2000. He obtained his Ph.D from University of Paris Sud in 2003 on data warehousing, and his HDR from University of Versailles in 2013 on privacy preserving data mangement. Since 2014, he holds a professor position in the Systems and Data Security team of the Laboratoire d’Informatique Fondamentale d’Orléans (LIFO), at INSA Centre Val de Loire and is an external collaborator of the Inria Private and Trusted Cloud (PETRUS) team. His current research topics cover anonymization techniques, models and methods to represent, quantify and enforce privacy models (such as logics, secure hardware and cryptography), and the design and implementation of large scale privacy-by-design information management systems. Pr. Nguyen is head of LIFO since 2016, and co-chair of the Privacy Working group of the CNRS research group on Computer Security since 2016.

Autres Liens

École d’été Masses de données 2022

Édition précedente: BDA’2020

Site de la communauté BDA

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