Special Issue "Learning with Big Data: Scalable Algorithms and Novel Applications"

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (31 January 2018)

Special Issue Editors

Guest Editor
Prof. Dr. Suely Oliveira

Department of Computer Science, 14 MLH, The University of Iowa, Iowa City, IA 52242-1419, USA
Website | E-Mail
Interests: big data applications; parallel algorithms; clustering; machine learning algorithms
Guest Editor
Dr. Tianbao Yang

Department of Computer Science, 14 MLH, The University of Iowa, Iowa City, IA 52242-1419, USA
Website | E-Mail
Interests: stochastic optimization; online optimization; deep learning; big data analytics
Guest Editor
Dr. Xun Zhou

Department of Management Sciences, W252 PBB, The University of Iowa, Iowa City, IA 52242-1994, USA
Website | E-Mail
Interests: spatial and spatio-temporal data mining; spatial databases; big data analytics

Special Issue Information

Dear Colleagues,

Recent years have witnessed unprecedented growth in the scale, dimensionality and complexities of data in various areas, spurring BIG DATA research and development. Big data research has empowered the success of many applications in urban computing, social science, e-commerce, computer vision, natural language processing, speech recognition, bioinformatics, education, physics, chemistry, biology, and engineering. On the other hand, in order to enable learning with big data, scalable algorithms have attracted much attention in machine learning and data mining. Numerous computational techniques for Big Data have been proposed, including stochastic optimization, parallel and distributed optimization, randomization, and GPU computing. This Special Issue addresses the emerging topic of learning with big data, with an emphasis on novel applications and scalable algorithms. Papers may choose to mainly focus on one aspect (novel applications or scalable algorithms) but also provide sufficient background or discussion on the other.

Topics of Interest

Topics of interest include but not limited to:

  • Novel Applications of Machine Learning and Data Mining on Big Data. In particular, we welcome novel applications in spatial and temporal data mining, urban and mobile computing, smart city and smart community, e-commerce, and computer vision.
  • Big Data Learning Techniques and their applications. These include stochastic optimization, online optimization, parallel and distributed optimization, randomized dimensionality reduction, GPU computing, etc. The employment of these techniques for solving machine learning and data mining problems on big datasets are particularly welcome.
  • Novel Machine Learning Methods such as Deep Learning and their applications to big datasets.

Prof. Dr. Suely Oliveira
Dr. Tianbao Yang
Dr. Xun Zhou
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 papers will be 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. Big Data and Cognitive Computing is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. 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
  • novel applications
  • scalable algorithms
  • deep learning
  • stochastic optimization
  • spatio-temporal data mining

Published Papers (3 papers)

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Research

Open AccessArticle A Deep Learning Model of Perception in Color-Letter Synesthesia
Big Data Cogn. Comput. 2018, 2(1), 8; https://doi.org/10.3390/bdcc2010008
Received: 12 December 2017 / Revised: 4 March 2018 / Accepted: 8 March 2018 / Published: 13 March 2018
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Abstract
Synesthesia is a psychological phenomenon where sensory signals become mixed. Input to one sensory modality produces an experience in a second, unstimulated modality. In “grapheme-color synesthesia”, viewed letters and numbers evoke mental imagery of colors. The study of this condition has implications for
[...] Read more.
Synesthesia is a psychological phenomenon where sensory signals become mixed. Input to one sensory modality produces an experience in a second, unstimulated modality. In “grapheme-color synesthesia”, viewed letters and numbers evoke mental imagery of colors. The study of this condition has implications for increasing our understanding of brain architecture and function, language, memory and semantics, and the nature of consciousness. In this work, we propose a novel application of deep learning to model perception in grapheme-color synesthesia. Achromatic letter images, taken from database of handwritten characters, are used to train the model, and to induce computational synesthesia. Results show the model learns to accurately create a colored version of the inducing stimulus, according to a statistical distribution from experiments on a sample population of grapheme-color synesthetes. To the author’s knowledge, this work represents the first model that accurately produces spontaneous, creative mental imagery characteristic of the synesthetic perceptual experience. Experiments in cognitive science have contributed to our understanding of some of the observable behavioral effects of synesthesia, and previous models have outlined neural mechanisms that may account for these observations. A model of synesthesia that generates testable predictions on brain activity and behavior is needed to complement large scale data collection efforts in neuroscience, especially when articulating simple descriptions of cause (stimulus) and effect (behavior). The research and modeling approach reported here provides a framework that begins to address this need. Full article
(This article belongs to the Special Issue Learning with Big Data: Scalable Algorithms and Novel Applications)
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Open AccessArticle A Multi-Modality Deep Network for Cold-Start Recommendation
Big Data Cogn. Comput. 2018, 2(1), 7; https://doi.org/10.3390/bdcc2010007
Received: 1 February 2018 / Revised: 18 February 2018 / Accepted: 27 February 2018 / Published: 5 March 2018
PDF Full-text (523 KB) | HTML Full-text | XML Full-text
Abstract
Collaborative filtering (CF) approaches, which provide recommendations based on ratings or purchase history, perform well for users and items with sufficient interactions. However, CF approaches suffer from the cold-start problem for users and items with few ratings. Hybrid recommender systems that combine collaborative
[...] Read more.
Collaborative filtering (CF) approaches, which provide recommendations based on ratings or purchase history, perform well for users and items with sufficient interactions. However, CF approaches suffer from the cold-start problem for users and items with few ratings. Hybrid recommender systems that combine collaborative filtering and content-based approaches have been proved as an effective way to alleviate the cold-start issue. Integrating contents from multiple heterogeneous data sources such as reviews and product images is challenging for two reasons. Firstly, mapping contents in different modalities from the original feature space to a joint lower-dimensional space is difficult since they have intrinsically different characteristics and statistical properties, such as sparse texts and dense images. Secondly, most algorithms only use content features as the prior knowledge to improve the estimation of user and item profiles but the ratings do not directly provide feedback to guide feature extraction. To tackle these challenges, we propose a tightly-coupled deep network model for fusing heterogeneous modalities, to avoid tedious feature extraction in specific domains, and to enable two-way information propagation from both content and rating information. Experiments on large-scale Amazon product data in book and movie domains demonstrate the effectiveness of the proposed model for cold-start recommendation. Full article
(This article belongs to the Special Issue Learning with Big Data: Scalable Algorithms and Novel Applications)
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Open AccessArticle A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization
Big Data Cogn. Comput. 2018, 2(1), 5; https://doi.org/10.3390/bdcc2010005
Received: 28 December 2017 / Revised: 16 February 2018 / Accepted: 19 February 2018 / Published: 24 February 2018
Cited by 2 | PDF Full-text (595 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., ozone, particle matter (PM2.5) and sulfur dioxide). Machine learning, as one of the most popular techniques, is able
[...] Read more.
In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., ozone, particle matter ( PM 2.5 ) and sulfur dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exist some works applying machine learning to air quality prediction, most of the prior studies are restricted to several-year data and simply train standard regression models (linear or nonlinear) to predict the hourly air pollution concentration. In this work, we propose refined models to predict the hourly air pollution concentration on the basis of meteorological data of previous days by formulating the prediction over 24 h as a multi-task learning (MTL) problem. This enables us to select a good model with different regularization techniques. We propose a useful regularization by enforcing the prediction models of consecutive hours to be close to each other and compare it with several typical regularizations for MTL, including standard Frobenius norm regularization, nuclear norm regularization, and 2 , 1 -norm regularization. Our experiments have showed that the proposed parameter-reducing formulations and consecutive-hour-related regularizations achieve better performance than existing standard regression models and existing regularizations. Full article
(This article belongs to the Special Issue Learning with Big Data: Scalable Algorithms and Novel Applications)
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