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Future Internet
Special Issues
Big Data Analytics and Artificial Intelligence
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Big Data Analytics and Artificial Intelligence

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: closed (25 July 2019) | Viewed by 14466

Special Issue Editors

Prof. Dr. Claudia Loebbecke

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Guest Editor
Dept. of Media and Technology Management, University of Cologne, Cologne, Germany
Interests: Big Data; Big Data Analytics; Artificial Intelligence; Media Management; Technology Management
Mr. Stefan Cremer

E-Mail Website
Guest Editor
Dept. of Media and Technology Management, University of Cologne, Cologne, Germany
Interests: Data Analytics; Artificial Intelligence; Information Goods; Cultural Impacts on Creativity and Innovation
Prof. Gun Woong Lee

E-Mail Website
Guest Editor
SungKyunKwan University (SKKU), Seoul 03063, Korea
Interests: Big Data & Mobile Analytics; Sharing Economy; FinTech; Social Media; ICT for Development (ICT4D)

Special Issue Information

Dear Colleagues,

With the increasing volume, velocity, and variety of data available, big data analytics and artificial intelligence have become central technology drivers and disruptors of entire industries. On a technological level, the enormous amount of new data generated every day poses a challenge for traditional database and information systems. To cope with this challenge, both research and practice are increasingly employing big data analytics and artificial intelligence, starting with determining the ways to store and share large amounts of data; dealing with data quality and uncertainty; asking the right data-focused question(s); employing state-of-the-art methods for data analysis; and, increasingly, deploying an array of artificial intelligence approaches and technologies, such as machine learning, natural language processing, and robotics.

Big data analytics and artificial intelligence challenge traditional expertise and governance mechanisms, thereby sometimes leading to reorganizing established value chains and altering business ecosystems. At the same time, they leverage new opportunities in areas as diverse as market intelligence, marketing and commerce, government, healthcare, and academic research—to name just a few.

The purpose of this future Internet Special Issue is to provide a forum for interdisciplinary research that builds on the latest advancements of big data and artificial intelligence. We very much welcome both empirical and theoretical contributions grounded on any established research paradigm.

Prof. Dr. Claudia Loebbecke
Mr. Stefan Cremer
Prof. Gun Woong Lee
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data
  • big data analytics
  • artificial intelligence

Published Papers (2 papers)

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Research

16 pages, 4006 KiB  
Article
Artificial Intelligence Imagery Analysis Fostering Big Data Analytics
by Stefan Cremer and Claudia Loebbecke
Future Internet 2019, 11(8), 178; https://doi.org/10.3390/fi11080178 - 15 Aug 2019
Cited by 4 | Viewed by 4893
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Big Data Analytics and Artificial Intelligence)
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16 pages, 994 KiB  
Article
Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2vec and WordNet
by Korawit Orkphol and Wu Yang
Future Internet 2019, 11(5), 114; https://doi.org/10.3390/fi11050114 - 12 May 2019
Cited by 60 | Viewed by 9083
Abstract
Words have different meanings (i.e., senses) depending on the context. Disambiguating the correct sense is important and a challenging task for natural language processing. An intuitive way is to select the highest similarity between the context and sense definitions provided by a large [...] Read more.
Words have different meanings (i.e., senses) depending on the context. Disambiguating the correct sense is important and a challenging task for natural language processing. An intuitive way is to select the highest similarity between the context and sense definitions provided by a large lexical database of English, WordNet. In this database, nouns, verbs, adjectives, and adverbs are grouped into sets of cognitive synonyms interlinked through conceptual semantics and lexicon relations. Traditional unsupervised approaches compute similarity by counting overlapping words between the context and sense definitions which must match exactly. Similarity should compute based on how words are related rather than overlapping by representing the context and sense definitions on a vector space model and analyzing distributional semantic relationships among them using latent semantic analysis (LSA). When a corpus of text becomes more massive, LSA consumes much more memory and is not flexible to train a huge corpus of text. A word-embedding approach has an advantage in this issue. Word2vec is a popular word-embedding approach that represents words on a fix-sized vector space model through either the skip-gram or continuous bag-of-words (CBOW) model. Word2vec is also effectively capturing semantic and syntactic word similarities from a huge corpus of text better than LSA. Our method used Word2vec to construct a context sentence vector, and sense definition vectors then give each word sense a score using cosine similarity to compute the similarity between those sentence vectors. The sense definition also expanded with sense relations retrieved from WordNet. If the score is not higher than a specific threshold, the score will be combined with the probability of that sense distribution learned from a large sense-tagged corpus, SEMCOR. The possible answer senses can be obtained from high scores. Our method shows that the result (50.9% or 48.7% without the probability of sense distribution) is higher than the baselines (i.e., original, simplified, adapted and LSA Lesk) and outperforms many unsupervised systems participating in the SENSEVAL-3 English lexical sample task. Full article
(This article belongs to the Special Issue Big Data Analytics and Artificial Intelligence)
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