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From data Processing to Knowledge Processing: Working with Operational Schemas by Autopoietic Machines

1
Department of Mathematics, University of California, 520 Portola Plaza, Los Angeles, CA 90095, USA
2
Ageno School of Business, Golden Gate University, San Francisco, CA 94105, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Verena Kantere
Big Data Cogn. Comput. 2021, 5(1), 13; https://doi.org/10.3390/bdcc5010013
Received: 7 January 2021 / Revised: 26 February 2021 / Accepted: 2 March 2021 / Published: 10 March 2021
(This article belongs to the Special Issue Big Data Analytics and Cloud Data Management)
Knowledge processing is an important feature of intelligence in general and artificial intelligence in particular. To develop computing systems working with knowledge, it is necessary to elaborate the means of working with knowledge representations (as opposed to data), because knowledge is an abstract structure. There are different forms of knowledge representations derived from data. One of the basic forms is called a schema, which can belong to one of three classes: operational, descriptive, and representation schemas. The goal of this paper is the development of theoretical and practical tools for processing operational schemas. To achieve this goal, we use schema representations elaborated in the mathematical theory of schemas and use structural machines as a powerful theoretical tool for modeling parallel and concurrent computational processes. We describe the schema of autopoietic machines as physical realizations of structural machines. An autopoietic machine is a technical system capable of regenerating, reproducing, and maintaining itself by production, transformation, and destruction of its components and the networks of processes downstream contained in them. We present the theory and practice of designing and implementing autopoietic machines as information processing structures integrating both symbolic computing and neural networks. Autopoietic machines use knowledge structures containing the behavioral evolution of the system and its interactions with the environment to maintain stability by counteracting fluctuations. View Full-Text
Keywords: knowledge; information; schema; knowledge processing; automaton; autopoietic machines; structural machines knowledge; information; schema; knowledge processing; automaton; autopoietic machines; structural machines
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MDPI and ACS Style

Burgin, M.; Mikkilineni, R. From data Processing to Knowledge Processing: Working with Operational Schemas by Autopoietic Machines. Big Data Cogn. Comput. 2021, 5, 13. https://doi.org/10.3390/bdcc5010013

AMA Style

Burgin M, Mikkilineni R. From data Processing to Knowledge Processing: Working with Operational Schemas by Autopoietic Machines. Big Data and Cognitive Computing. 2021; 5(1):13. https://doi.org/10.3390/bdcc5010013

Chicago/Turabian Style

Burgin, Mark, and Rao Mikkilineni. 2021. "From data Processing to Knowledge Processing: Working with Operational Schemas by Autopoietic Machines" Big Data and Cognitive Computing 5, no. 1: 13. https://doi.org/10.3390/bdcc5010013

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