Human, Model and Machine: A Complementary Approach to Big Data
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Authors
Thomson, Robert
Lebiere, Christian
Bennati, Stefano
Issue Date
2014-04-01
Type
Conference presentations, papers, posters
Language
Keywords
big data , cognitive modeling , cognitive architecture , DIKW , knowledge formatione , expertise
Alternative Title
Abstract
In this paper, we describe a framework for processing big data that maximizing the efficiency of human data scientists by having them primarily operate over information that is best structured to human processing demands. We accomplish this through the use of cognitive models as an intermediary between machine learning algorithms and human data scientists. The ACT-R cognitive architecture is a computational implementation of a unified theory of cognition. ACT-R cognitive models can take weakly structured data and learn to filter information and make accurate inferences orders of magnitude faster than machine learning, and then present these well-structured inferences to human data scientists. The role for human data scientists is both oversight and feedback; one complementary piece of a hierarchy of cognitive and machine learning techniques that are computationally appropriate for their level of information complexity.
Description
Citation
Thomson, Robert, Christian Lebiere, and Stefano Bennati. "Human, model and machine: a complementary approach to big data." In Proceedings of the 2014 Workshop on Human Centered Big Data Research, pp. 27-31. 2014.
Publisher
ACM