Pairing conceptual modeling with machine learning
Wolfgang Maass,Wolfgang Maass,Veda C. Storey +2 more
- Vol. 134, pp 101909
TLDR
In this paper, the authors provide an overview of machine learning foundations and development cycle and examine how conceptual modeling can be applied to machine learning and propose a framework for incorporating conceptual modeling into data science projects.Abstract:
Both conceptual modeling and machine learning have long been recognized as important areas of research. With the increasing emphasis on digitizing and processing large amounts of data for business and other applications, it would be helpful to consider how these areas of research can complement each other. To understand how they can be paired, we provide an overview of machine learning foundations and development cycle. We then examine how conceptual modeling can be applied to machine learning and propose a framework for incorporating conceptual modeling into data science projects. The framework is illustrated by applying it to a healthcare application. For the inverse pairing, machine learning can impact conceptual modeling through text and rule mining, as well as knowledge graphs. The pairing of conceptual modeling and machine learning in this way should help lay the foundations for future research.read more
Citations
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Journal ArticleDOI
Conceptual modeling in the era of Big Data and Artificial Intelligence: Research topics and introduction to the special issue
TL;DR: In 2018, the 37th International Conference on Conceptual Modeling (ER'18) as mentioned in this paper was held in Xi'an, China and the best papers from this conference were presented.
Journal ArticleDOI
The central role of data repositories and data models in Data Science and Advanced Analytics
TL;DR: In the age of Data Science and Advanced Analytics, we are witnessing a race for developing data-driven smart systems in various domains such as business, finance, healthcare, environment, cybersecurity, etc. as discussed by the authors.
References
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Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.