DOING’21: Intelligent Data – from data to knowledge

Text 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, and organized according to an appropriate schema that follows the semantics of an application domain. Indeed, solving the problems of modern society requires interdisciplinary 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 requests, which may involve information analysis, with quality, consistency, and privacy preservation guarantees. Knowledge bases can be built as these new generation infrastructures which support data science queries on a user-friendly framework and are capable of providing the required machinery for advised decision-making.

This workshop focuses on all aspects concerning these modern infrastructures, giving particular attention (but not limited to) to data related to health and environmental domains.


Mírian Halfeld Ferrari – Université d’Orléans, INSA CVL, LIFO EA, France
Carmem H. Hara – Universidade Federal do Paraná, Curitiba, Brazil

SIMPDA’21 Data-Driven Process Discovery and Analysis

The increasing automation of business processes and the growing amounts of process data become available opens new research opportunities for business process data analysis, mining, and modeling. The aim of the IFIP W.G. 2.6 International Symposium on Data-Driven Process Discovery and Analysis is to offer a forum where researchers from different communities and the industry can share their insight into this hot new field. The Symposium will feature a number of advanced research papers, shorter presentations on recent research, a competitive Ph.D. seminar, and selected research and industrial demonstrations. 


Paolo Ceravolo, Università degli Studi di Milano, Italy
Maurice van Keulen, University of Twente, The Netherlands
Maria Teresa Gomez Lopez, University of Seville, Spain

MADEISD’21: Modern Approaches in Data Engineering and Information System Design

The main goal of the MADEISD workshop is to address open questions and real potentials for various applications of modern approaches and technologies in data engineering and information system design so as to develop and implement effective software services in a support of information management in various organization systems. The workshop addresses interdisciplinary character of a set of theories, methodologies, processes, architectures, and technologies in disciplines such as Data Engineering, Information System Design, Big Data, NoSQL Systems, and Model Driven Approaches in a development of effective software services. Researchers from all over the world are invited to present their contributions, interdisciplinary approaches or case studies related to modern approaches in Data Engineering and Information System Design. Experts from all sectors are welcomed.


Ivan Luković – University of Novi Sad, Faculty of Technical Sciences, Serbia
Slavica Kordić – University of Novi Sad, Faculty of Technical Sciences, Serbia
Sonja Ristić – University of Novi Sad, Faculty of Technical Sciences, Serbia

MegaData’21: Advances in Data Systems Management, Engineering, and Analytics

The MegaData workshop aims to bring together researchers and practitioners from the data science and data engineering communities to share their latest studies in Big Data practices. It will be an opportunity for novice and experienced Big Data users to learn, get help and have exchanges between them and the system administrators, programmers, and others interested in sharing and presenting their perspectives on Big Data systems. The workshop theme focuses on Data systems effective management, engineering, and analytics of different Big Data models ranging from databases to file systems and from data streaming to batch processing.  MegaData offers an opportunity to showcase the latest advances in this area and discuss and identify future directions and challenges in all aspects of Big Data systems’ management and engineering.


Yaser Jararweh – Duquesne University, USA
Tomás F. Pena – University of Santiago de Compostela, CiTIUS research center, Spain
Feras M. Awaysheh – University of Tartu, Estonia

CAoNS’21: Computational Aspects of Network Science

The 1st International Workshop on Computational Aspects of Networks Science (CAoNS) provides opportunities for researchers and practitioners to share their original research results, and practical development products on Network Science. CAoNS aims at bringing together researchers and practitioners working on areas related to any kind of network, such as networks of devices, World Wide Web, social, bibliographic, biological networks, complex systems, and so on, with main focus on the management of networked data (retrieval, evolution) and on the use of analytics for predictive purposes and/or on the analysis of the networks themselves. Due to numerous applications, there is a growing interest in such systems from the point of view of modeling, capturing, storing and management. This is where IT comes into play, e.g. graph databases, machine learning, distributed and parallel processing. We invite the submission of articles that address various computational aspects of network science. The workshop welcomes submissions of theoretical, technical, experimental, methodological papers, application papers.


Dimitrios Katsaros – University of Thessaly (Greece)
Yannis Manolopoulos – Open University of Cyprus and Aristotle University of Thessaloniki (Greece)