Universität zu Köln

Cremer, Stefan and Loebbecke, Claudia (2019). Artificial Intelligence Imagery Analysis Fostering Big Data Analytics. Future Internet, 11 (8). BASEL: MDPI. ISSN 1999-5903

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Abstract

In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Cremer, Stefan UNSPECIFIED UNSPECIFIED UNSPECIFIED Loebbecke, Claudia UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-145320
DOI:	10.3390/fi11080178
Journal or Publication Title:	Future Internet
Volume:	11
Number:	8
Date:	2019
Publisher:	MDPI
Place of Publication:	BASEL
ISSN:	1999-5903
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language PRESENTATION FORMATS Multiple languages Computer Science, Information Systems Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/14532