Beyond Databases Architectures And Structures Paving The Road To Smart Data Processing And Analysis
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Beyond Databases, Architectures and Structures. Paving the Road to Smart Data Processing and Analysis
This book constitutes the refereed proceedings of the 15th International Conference entitled Beyond Databases, Architectures and Structures, BDAS 2019, held in Ustroń, Poland, in May 2019. It consists of 26 carefully reviewed papers selected from 69 submissions. The papers are organized in topical sections, namely big data and cloud computing; architectures, structures and algorithms for efficient data processing and analysis; artificial intelligence, data mining and knowledge discovery; image analysis and multimedia mining; bioinformatics and biomedical data analysis; industrial applications; networks and security.
Man-Machine Interactions 6
This book includes a selection papers describing the latest advances and discoveries in the field of human-computer interactions, which were presented at the 6th International Conference on Man-Machine Interactions, ICMMI 2019, held in Cracow, Poland, in October 2019. Human-computer interaction is a multidisciplinary field concerned with the design of computer technology and, in particular, the interaction between humans (the users) and computers. Over recent decades, this field has expanded from its initial focus on individual and generic user behavior to the widest possible spectrum of human experiences and activities. The book features papers covering a variety of topics, which are divided into five sections: ‘human-computer interfaces,’ ‘artificial intelligence and knowledge discovery,’ ‘pattern recognition,’ ‘bio-data and bio-signal analysis,’ and ‘algorithms, optimization and signal processing.’ Presenting the latest research in the field, this book provides a valuable reference resource for academics, industry practitioners and students.
Knowledge Graphs
This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.