Special Issue "Big Data"
A special issue of Entropy (ISSN 1099-4300).
Deadline for manuscript submissions: closed (30 October 2013)
Dr. Nikunj C. Oza
NASA Ames Research Center, NASA, Moffett Field, CA 94035, USA
Interests: data mining; machine learning; ensemble learning methods; online learning; anomaly detection; applications of machine learning and data mining
"Big data" refers to datasets that are so large that conventional database management and data analysis tools are insufficient to work with them. Big data has become a bigger-than-ever problem for many reasons. Data storage is rapidly becoming cheaper in terms of cost per unit of storage, thereby making appealing the prospect of saving all collected data. Computer processing is becoming more powerful and cheaper, and computer memory is also becoming cheaper, thereby making processing such data increasingly practical. The number of deployed sensors is growing rapidly. For example, there are a greater number of Earth-Observing Satellites than ever before, collecting many terabytes of data per day. Engineered systems have increasing sensing of their environment as well as of the systems themselves for integrated vehicle health management. The internet has greatly added to the volume and heterogeneity of data available---the world-wide web contains an enormous volume of text, images, videos, and connections between these. Many complex processes that we desire to understand generate these data. We desire methods that go in the reverse direction---from big data to an understanding of these complex processes---how they work, when and how they display anomalous behavior, and other insights. Data mining is a field---brought about through the combination of machine learning, statistics, and database management---that seeks to develop such methods. This special issue seeks comprehensive reviews or research articles in the area of entropy and information theory methods for big data. Research articles may describe theoretical and/or algorithmic developments.
Dr. Nikunj C. Oza
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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed Open Access monthly journal published by MDPI.
- big data
- data mining
- predictive analytics
- knowledge discovery
- anomaly detection
Entropy 2014, 16(2), 854-869; doi:10.3390/e16020854
Received: 13 October 2013; in revised form: 10 January 2014 / Accepted: 28 January 2014 / Published: 13 February 2014| PDF Full-text (325 KB) | HTML Full-text | XML Full-text
Entropy 2013, 15(12), 5510-5535; doi:10.3390/e15125510
Received: 9 August 2013; in revised form: 4 December 2013 / Accepted: 9 December 2013 / Published: 13 December 2013| PDF Full-text (728 KB)
Entropy 2013, 15(11), 4782-4801; doi:10.3390/e15114782
Received: 26 August 2013; in revised form: 27 September 2013 / Accepted: 29 October 2013 / Published: 4 November 2013| Cited by 1 | PDF Full-text (2532 KB)
Entropy 2013, 15(5), 1567-1586; doi:10.3390/e15051567
Received: 1 March 2013; in revised form: 25 April 2013 / Accepted: 29 April 2013 / Published: 3 May 2013| Cited by 1 | PDF Full-text (2683 KB)
Entropy 2013, 15(5), 1486-1502; doi:10.3390/e15051486
Received: 28 February 2013; in revised form: 16 April 2013 / Accepted: 18 April 2013 / Published: 25 April 2013| PDF Full-text (221 KB)
Last update: 12 March 2013