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

Intelligent Support for Information Retrieval of Web Documents

The main goal of this research was to investigate the means of intelligent support for retrieval of web documents. We have proposed the architecture of the web tool system --- Trillian, which discovers the interests of users without their interaction and uses them for autonomous searching of related web content. Discovered pages are suggested to the user. The discovery of user interests is based on analysis of documents visited by the users previously. We have created a module for completely transparent tracking of the user's movement on the web, which logs both visited URLs and contents of web pages. The post analysis step is based on a variant of the suffix tree clustering algorithm. We primarily focus on overall Trillian architecture design and the process of discovering topics of interests. We have implemented an experimental prototype of Trillian and evaluated the quality, speed and usefulness of the proposed system. We have shown that clustering is a feasible technique for extraction of interests from web documents. We consider the proposed architecture to be quite promising and suitable for future extensions

Knowledge and Metadata Integration for Warehousing Complex Data

With the ever-growing availability of so-called complex data, especially on the Web, decision-support systems such as data warehouses must store and process data that are not only numerical or symbolic. Warehousing and analyzing such data requires the joint exploitation of metadata and domain-related knowledge, which must thereby be integrated. In this paper, we survey the types of knowledge and metadata that are needed for managing complex data, discuss the issue of knowledge and metadata integration, and propose a CWM-compliant integration solution that we incorporate into an XML complex data warehousing framework we previously designed.Comment: 6th International Conference on Information Systems Technology and its Applications (ISTA 07), Kharkiv : Ukraine (2007

Mobile Databases: a Selection of Open Issues and Research Directions

International audienceThis paper reports on the main results of a specific action on mobile databases conducted by CNRS in France from October 2001 to December 2002. The objective of this action was to review the state of progress in mobile databases and identify major research directions for the French database community. Rather than provide a survey of all important issues in mobile databases, this paper gives an outline of the directions in which the action participants are now engaged, namely: copy synchronization in disconnected computing, mobile transactions, database embedded in ultra-light devices, data confidentiality, P2P dissemination models and middleware adaptability

Conceptual modeling in the era of Big Data and Artificial Intelligence: Research topics and introduction to the special issue

Since the first version of the Entity–Relationship (ER) model proposed by Peter Chen over forty years ago, both the ER model and conceptual modeling activities have been key success factors for modeling computer-based systems. During the last decade, conceptual modeling has been recognized as an important research topic in academia, as well as a necessity for practitioners. However, there are many research challenges for conceptual modeling in contemporary applications such as Big Data, data-intensive applications, decision support systems, e-health applications, and ontologies. In addition, there remain challenges related to the traditional efforts associated with methodologies, tools, and theory development. Recently, novel research is uniting contributions from both the conceptual modeling area and the Artificial Intelligence discipline in two directions. The first one is efforts related to how conceptual modeling can aid in the design of Artificial Intelligence (AI) and Machine Learning (ML) algorithms. The second one is how Artificial Intelligence and Machine Learning can be applied in model-based solutions, such as model-based engineering, to infer and improve the generated models. For the first time in the history of Conceptual Modeling (ER) conferences, we encouraged the submission of papers based on AI and ML solutions in an attempt to highlight research from both communities. In this paper, we present some of important topics in current research in conceptual modeling. We introduce the selected best papers from the 37th International Conference on Conceptual Modeling (ER’18) held in Xi’an, China and summarize some of the valuable contributions made based on the discussions of these papers. We conclude with suggestions for continued research.The research reported in this paper was partially funded by the ECLIPSE-UA (RTI2018-094283-B-C32) and the AETHER-UA (PID2020-112540RB-C43) Projects from the Spanish Ministry of Science and Innovation