IEEE Systems, Man and Cybernetics Magazine - July 2018 - 15

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Bridging a Gap Between Disciplines
Cyberspace is making available a vast and ever-increasing amount of data about the physical world, manmade
systems, human behavior, and much more. This is thanks
to the rapid advances in cloud computing, the Internet
and wireless communication, smart devices, mobile computing, and Internet of Things technology. Consequently,
data analytics has boomed into an active scientific/technical interdisciplinary research area. One compelling
challenge for researchers in this field is to use data to
obtain knowledge that is reusable, testable, verifiable,
and transferable, so that learning and reasoning can be
integrated effectively [1].

However, much of that extraction depends on machinelearning techniques, and these have many shortcomings.
The results of these techniques do not come with the
human-comprehensible explanations needed to validate,
verify, and test them. They also may be short-lived,
because they apply only to the currently available data set.
Thus, they are not as reusable for different users in varying
situations. Finally, the results are often in a tool-specific
format that is not transferable to other applications.
To overcome these problems, statistical learning and
symbolic reasoning need to be combined in an effective
manner, perhaps with knowledge representation playing a
crucial role, as Shoham argued [2]. However, in the past
three decades, these two fields have mostly been developed separately by distinct research communities. Those
working on the integration of symbolic and connectionist
paradigms of artificial intelligence are among the few
exceptions (see [3] for a recent survey).
The solution proposed by this article is a pattern-oriented approach that aims at bridging the gap between databased machine learning and reusable knowledge. It consists
of two parts:
◆ A formal theory of pattern: This is composed of a formalism of knowledge representation in the form of patterns, a set of operators that allow patterns to be
composed and instantiated, and a collection of algebraic laws on these operators to enable the reasoning
about and processing of knowledge. The theory of patterns is generalized from its original use in software
design to any subject domain, including the new area
of cyberspace.
◆ A pattern-oriented research methodology: This promotes the study of a subject domain by systematically
addressing a set of interrelated research questions with a
focus on patterns. As Cao pointed out recently [4], to
achieve the full potential of data science, a disciplinewide effort and corresponding methodology are required.
Pattern-Oriented Research Methodology
In general, a pattern represents a discernible regularity in
nature, manmade systems, and human behavior, among
others. It can be seen as either a template from which
instances can be created (the prescriptive view) or an
account of recurring observable phenomena (the descriptive view). The latter makes it possible to predict regularities in a subject domain and is analogous to a scientific
theory. When the subject domain is as complex as cyberspace, there may be a large number of interacting patterns,
each describing and predicting a subset of recurring phenomena. These patterns are all interrelated, and they can
be composed with each other.
The research questions for a pattern-oriented research
methodology include devising ways to do the following:
◆ identify the patterns manually, semiautomatically, or
even fully automatically, e.g., using data-mining and
machine-learning techniques
Ju ly 2018

IEEE SyStEmS, man, & CybErnEtICS magazInE

15


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Table of Contents for the Digital Edition of IEEE Systems, Man and Cybernetics Magazine - July 2018

Contents
IEEE Systems, Man and Cybernetics Magazine - July 2018 - Cover1
IEEE Systems, Man and Cybernetics Magazine - July 2018 - Cover2
IEEE Systems, Man and Cybernetics Magazine - July 2018 - Contents
IEEE Systems, Man and Cybernetics Magazine - July 2018 - 2
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IEEE Systems, Man and Cybernetics Magazine - July 2018 - Cover3
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