Complex Data Analytics with Formal Concept Analysis

FCA is an important formalism that is associated with a variety of research areas such as lattice theory, knowledge representation, data mining, machine learning, and semantic Web. It is successfully exploited in an increasing number of application domains such as software engineering, information retrieval, social network analysis, and bioinformatics. Its mathematical power comes from its concept lattice formalization in which each element in the lattice captures a formal concept while the whole structure represents a conceptual hierarchy that offers browsing, clustering and association rule mining.

Complex data analytics refers to advanced methods and tools for mining and analyzing data with complex structures such as XML/Json data, text and image data, multidimensional data, graphs, sequences and streaming data. It also covers visualization mechanisms used to highlight the discovered knowledge.

This edited book examines a set of important and relevant research directions in complex data management, and updates the  contribution of the FCA community in analyzing complex and large data such as knowledge graphs and interlinked contexts.  For example, Formal Concept Analysis and some of its extensions are exploited, revisited and coupled with recent processing parallel and distributed paradigms to maximize the benefits in analyzing large data.

Léonard Kwuida graduated in mathematics at Université de Yaoundé I. He hold a PhD in algebra and logic from TU Dresden. He currently works at the Department of Business, Bern University of Applied Sciences, where he teaches mathematics and statistics. He does research in Formal Concept Analysis and related algebraic structures. He has (co-)authored more than thirty papers published in peer-reviewed journals, conferences and workshops proceedings. Dr. Kwuida has co-edited four proceedings and published one book. He also serves in the Program Committee of major conferences in FCA.

Talel Abdessalem is a Professor at Télécom Paris (Institut Polytechnique de Paris). He held the Big Data and Market Insights Chair at Télécom ParisTech from 2013 to 2017. He is heading the Information and Communication (LTCI) research center since 2017 and Dean of Research at Télécom Paris since 2018. His main research achievements are on version control, change detection and querying of XML documents, information extraction from the structured web, and social networks analysis. His current research interests are in recommender systems and predictive analytics. He participated in several national and European research projects. He co-supervised a dozen of Ph. D. students. He received a Ph. D. in Computer Science from the University Paris-Dauphine and a Habilitation degree (HDR) from the University Pierre & Marie Curie. He spent two years as an assistant professor at the University Paris-Dauphine (PSL), before joining Telecom Paris (formerly ENST) in 1998.