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From Data to Information to Knowledge: Temporal and Structural Information

Shannon's information theory has served as a bedrock for critical advances in communication and storage systems over the past five decades. However, this theory does not handle well higher order structures (e.g., graphs, geometric structures), temporal aspects (e.g., real-time considerations),or semantics. We argue that these are essential aspects of data and information that underly a broad class of current and emerging data science applications.In this talk, we present some recent results on structural and temporal information. We start with a motivating example, where we show how to extract temporal information in dynamic networks (arrival of nodes) from its structure (unlabelled graphs). We then proceed to establish some fundamental limits on information content for a wide range of data structure, and present asymptotically optimal lossless compression algorithms achieving these limits for various graph models. We use these examples to motivate a novel view on data science. Our approach brings information to the front and centre of the theoretical foundations of information and data science.We pose a basic question in data science: how much learnable information exists in a particular dataset. In sum, we argue that the unifying theme of data science should be the triad: from data to information to knowledge. We Propose the novel concept of information-efficient computation as a framework for this triad, in which we first establish fundamental limits on information, and then seek efficient algorithms that achieve this limit.

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Wojciech Szpankowski is Saul Rosen Distinguished Professor of Computer Science at Purdue University where he teaches and conducts research in analysis of algorithms,information theory, analytic combinatorics, data science, random structures, and stability problems of distributed systems.He held several Visiting Professor/Scholar positions, includingMcGill University, INRIA, France, Stanford, Hewlett-Packard Labs, Universite de Versailles, University of Canterbury, New Zealand, Ecole Polytechnique, France, the Newton Institute, Cambridge, UK, ETH, Zurich, and Gdansk University of Technology, Poland."lancaster18" 53 lines, 3082 characters Canterbury, New Zealand, Ecole Polytechnique, France, the Newton Institute, Cambridge, UK, ETH, Zurich, and Gdansk University of Technology, Poland.He is a Fellow of IEEE, and the Erskine Fellow.In 2010 he received the Humboldt Research Award and in 2015the Inaugural Arden L. Bement Jr. Award.He published two books: "Average Case Analysis of Algorithms on Sequences", John Wiley & Sons, 2001, and "Analytic Pattern Matching: From DNA to Twitter", Cambridge, 2015.In 2008 he launched the interdisciplinary Institute for Science of Information, and in 2010 he became the Director of the newly established NSi Science and Technology Centre for Science of Information. --------------------------------------------------------------------------