Table of Contents

Big Data Cogn. Comput., Volume 1, Issue 1 (December 2017)

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
View options order results:
result details:
Displaying articles 1-3
Export citation of selected articles as:

Editorial

Jump to: Research

Open AccessEditorial Welcome to the New Interdisciplinary Journal Combining Big Data and Cognitive Computing
Big Data Cogn. Comput. 2017, 1(1), 1; doi:10.3390/bdcc1010001
Received: 30 November 2016 / Accepted: 30 November 2016 / Published: 8 December 2016
Cited by 2 | PDF Full-text (142 KB) | HTML Full-text | XML Full-text
Abstract
Welcome to Big Data and Cognitive Computing (BDCC). Full article

Research

Jump to: Editorial

Open AccessArticle A 5G Cognitive System for Healthcare
Big Data Cogn. Comput. 2017, 1(1), 2; doi:10.3390/bdcc1010002
Received: 7 January 2017 / Revised: 22 March 2017 / Accepted: 27 March 2017 / Published: 30 March 2017
Cited by 5 | PDF Full-text (1261 KB) | HTML Full-text | XML Full-text
Abstract
Developments and new advances in medical technology and the improvement of people’s living standards have helped to make many people healthier. However, there are still large design deficiencies due to the imbalanced distribution of medical resources, especially in developing countries. To address this
[...] Read more.
Developments and new advances in medical technology and the improvement of people’s living standards have helped to make many people healthier. However, there are still large design deficiencies due to the imbalanced distribution of medical resources, especially in developing countries. To address this issue, a video conference-based telemedicine system is deployed to break the limitations of medical resources in terms of time and space. By outsourcing medical resources from big hospitals to rural and remote ones, centralized and high quality medical resources can be shared to achieve a higher salvage rate while improving the utilization of medical resources. Though effective, existing telemedicine systems only treat patients’ physiological diseases, leaving another challenging problem unsolved: How to remotely detect patients’ emotional state to diagnose psychological diseases. In this paper, we propose a novel healthcare system based on a 5G Cognitive System (5G-Csys). The 5G-Csys consists of a resource cognitive engine and a data cognitive engine. Resource cognitive intelligence, based on the learning of network contexts, aims at ultra-low latency and ultra-high reliability for cognitive applications. Data cognitive intelligence, based on the analysis of healthcare big data, is used to handle a patient’s health status physiologically and psychologically. In this paper, the architecture of 5G-Csys is first presented, and then the key technologies and application scenarios are discussed. To verify our proposal, we develop a prototype platform of 5G-Csys, incorporating speech emotion recognition. We present our experimental results to demonstrate the effectiveness of the proposed system. We hope this paper will attract further research in the field of healthcare based on 5G cognitive systems. Full article
Figures

Open AccessArticle Function Modeling Improves the Efficiency of Spatial Modeling Using Big Data from Remote Sensing
Big Data Cogn. Comput. 2017, 1(1), 3; doi:10.3390/bdcc1010003
Received: 26 June 2017 / Revised: 10 July 2017 / Accepted: 10 July 2017 / Published: 13 July 2017
PDF Full-text (3598 KB) | HTML Full-text | XML Full-text
Abstract
Spatial modeling is an integral component of most geographic information systems (GISs). However, conventional GIS modeling techniques can require substantial processing time and storage space and have limited statistical and machine learning functionality. To address these limitations, many have parallelized spatial models using
[...] Read more.
Spatial modeling is an integral component of most geographic information systems (GISs). However, conventional GIS modeling techniques can require substantial processing time and storage space and have limited statistical and machine learning functionality. To address these limitations, many have parallelized spatial models using multiple coding libraries and have applied those models in a multiprocessor environment. Few, however, have recognized the inefficiencies associated with the underlying spatial modeling framework used to implement such analyses. In this paper, we identify a common inefficiency in processing spatial models and demonstrate a novel approach to address it using lazy evaluation techniques. Furthermore, we introduce a new coding library that integrates Accord.NET and ALGLIB numeric libraries and uses lazy evaluation to facilitate a wide range of spatial, statistical, and machine learning procedures within a new GIS modeling framework called function modeling. Results from simulations show a 64.3% reduction in processing time and an 84.4% reduction in storage space attributable to function modeling. In an applied case study, this translated to a reduction in processing time from 2247 h to 488 h and a reduction is storage space from 152 terabytes to 913 gigabytes. Full article
Figures

Journal Contact

MDPI AG
BDCC Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
E-Mail: 
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to BDCC Edit a special issue Review for BDCC
logo
loading...
Back to Top