Plenary Lecture

Big Data Algebra: A Rigorous Approach to Big Data Analytics and Engineering

Professor Yingxu Wang
President, International Institute of Cognitive Informatics and Cognitive Computing (ICIC)
Director, Laboratory for Cognitive Informatics, Denotational Mathematics, and Software Science
Dept. of Electrical and Computer Engineering
Schulich School of Engineering and Hotchkiss Brain Institute
University of Calgary

Abstract: Data are an abstract representation of the quantity of real-world entities and mental objects. Big data are extremely large-scaled heterogeneous data in terms of quantity, complexity, semantics, distribution, and processing costs in computer science, information science, cognitive informatics, web-based computing, cloud computing, and computational intelligence. Big data science studies the properties, theories, mathematical means, and methodologies of big data. Big data engineering is systematical analytic technologies for efficiently dealing with the inherent complexity and exponentially increasing demands in big data representation, acquisition, storage, organization, manipulation, searching, retrieval, distribution, standardization, consistency, and security.
This keynote lecture presents a big data algebra as a novel denotational mathematics for formal big data analytics in big data science and engineering. The cognitive foundations of data, information, knowledge, and intelligence are explored. A mathematical model of big data is formally introduced. Based on it, a set of algebraic operators on formal big data models, such as the formal big data analysis, inference, mining, induction, and fusion operators, is rigorously elaborated. This leads to the algebra for big data modeling, analyses, mining, information elicitation, knowledge representation, and intelligence inference. A wide range of applications of big data algebra are identified in the contemporary fields of big data science/engineering, cognitive informatics, knowledge mining, neurocomputing, human memory mechanisms, cognitive computing, machine learning, semantic computing, cognitive linguistics, cognitive systems, computational intelligence, artificial intelligence, cloud computing, and intelligent systems.

Brief Biography of the Speaker: Yingxu Wang is professor of cognitive computing, brain science, and denotational mathematics, President of International Institute of Cognitive Informatics and Cognitive Computing (ICIC, at the University of Calgary. He is a Fellow of ICIC, a Fellow of WIF (UK), a P.Eng of Canada, and a Senior Member of IEEE and ACM. He received a PhD in computer science from the Nottingham Trent University, UK. He was visiting professors (on sabbatical leave) at Oxford University (1995), Stanford University (2008), UC Berkeley (2008), and MIT (2012), respectively. He is the founder and steering committee chair of the annual IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC) since 2002. He is founding Editor-in-Chief of Int. Journal of Cognitive Informatics & Natural Intelligence (IJCINI), founding Editor-in-Chief of Int. Journal of Software Science & Computational Intelligence (IJSSCI), Associate Editor of IEEE Trans. on SMC (Systems), and Editor-in-Chief of Journal of Advanced Mathematics & Applications (JAMA). Dr. Wang is the initiator of a few cutting-edge research fields such as cognitive informatics, denotational mathematics (concept algebra, process algebra, system algebra, semantic algebra, and inference algebra), abstract intelligence (αI), cognitive computing, cognitive learning engines, cognitive knowledge base theory, and basic studies in software science, neuroinformatics, fuzzy mathematics, cognitive linguistics, and computational intelligence. He has published 400+ peer reviewed papers and 28 books in cognitive informatics, denotational mathematics, cognitive computing, software science, and computational intelligence. He is the recipient of dozens international awards on academic leadership, outstanding contributions, best papers, and teaching in the last three decades.

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