Databases and Information Systems in the AI Era: Contributions from ADBIS, TPDL and EDA 2020 Workshops and Doctoral Consortium

Research on database and information technologies has been rapidly evolving over the last couple of years. This evolution was lead by three major forces: Big Data, AI and Connected World that open the door to innovative research directions and challenges, yet exploiting four main areas: (i) computational and storage resource modeling and organization; (ii) new programming models, (iii) processing power and (iv) new applications that emerge related to health, environment, education, Cultural Heritage, Banking, etc. The 24th East-European Conference on Advances in Databases and Information Systems (ADBIS 2020), the 24th International Conference on Theory and Practice of Digital Libraries (TPDL 2020) and the 16th Workshop on Business Intelligence and Big Data (EDA 2020), held during August 25–27, 2020, at Lyon, France, and associated satellite events aimed at covering some emerging issues related to database and information system research in these areas. The aim of this paper is to present such events, their motivations, and topics of interest, as well as briefly outline the papers selected for presentations. The selected papers will then be included in the remainder of this volume

Inference Rules for Nested Functional Dependencies

Functional dependencies add semantics to a database schema# and are useful for studying vari# ous problems# such as database design# query optimization and how dependencies are carried into a view. In the context of a nested relational model# these dependencies can be extended by using path expressions instead of attribute names# resulting in a class of dependencies that we call nested functional dependencies #NFDs#. NFDs de#ne a natural class of dependencies in complex data struc# tures# in particular they allow the speci#cation of many useful intra# and inter#set dependencies #i.e.# dependencies that are local to a set and dependencies that require consistency between sets#. Such constraints cannot be captured by existing notions of functional# multi#valued# or join dependencies. This paper presents the de#nition of NFDs and gives their meaning by translation to logic. It then presents a sound and complete set of eight inference rules for NFDs# and discusses approaches to handling th..

DOING : Intelligent Data – from data to knowledge (workshop of ADBIS 2021): Springer Communications in Computer and Information Science 1450

International audienceTexts are important sources of information and communication in diverse domains. The intelligent, efficient, and secure use of this information requires, in most cases, the transformation of unstructured textual data into data sets with some structure, organized according to an appropriate schema that follows the semantics of an application domain. Indeed, solving the problems of modern society requires interdisci-plinary research and information cross-referencing, thus surpassing the simple provision of unstructured data. There is a need for representations that are more flexible, subtle, and context-sensitive, which can also be easily accessible via consultation tools and evolve according to these principles. In this context, consultation requires robust and efficient processing of queries, which may involve information analysis, quality assessments, consistency checking, and privacy preservation. Knowledge bases can be built as new generation infrastructures to support data science queries with a user-friendly framework. They can provide the required machinery for advised decision-making.The 2nd Workshop on Intelligent Data - From Data to Knowledge (DOING 2021) focused on transforming data into information and then into knowledge. The workshop gathers researchers from natural language processing (NLP), databases (DB), and artificial intelligence (AI). This edition featured works in two main areas: (1) information extraction from textual data and its representation on knowledge bases and (2) intelligent methods for handling and maintaining these databases. Overall, the purpose of the workshop was to focus on all aspects concerning modern infrastructures to support these areas, giving particular, but not sole, attention to data on health and environmental domains.DOING 2021 received nine submissions, out of which three were accepted as full papers and three as short papers, resulting in an acceptance rate of 50%. Each paper received three reviews from members of the Program Committee.This workshop is an event supported by the French network MADICS1. More specifically, it is an event of the action DOING2 within MADICS and of the DOING working group in the regional network DIAMS3

DOING : Intelligent Data – From Data to Knowledge WORKSHOP in ADBIS, TPDL & EDA 2020 joint conferences: Springer - Communications in Computer and Information Science 1260 - ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium

International audienceThe First Workshop on Intelligent Data - From Data to Knowledge (DOING 2020) focuses on transforming data into information and then into knowledge. It gathered researchers from natural language processing (NLP), databases, and AI. DOING 2020 focuses on all aspects concerning modern infrastructures that support these areas, giving particular attention, but not limited to, data on health and environmental domains. The DOING workshop received 17 submissions, out of which 8 were accepted as full papers and 1 as a short paper, resulting in an acceptance rate of 50%. The workshop program also featured an invited keynote talk by Professor Marie- Christine Rousset, from the Laboratoire d’Informatique de Grenoble (LIG), France

Databases and Information Systems: Contributions from ADBIS 2023 Workshops and Doctoral Consortium

International audienc