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| Content Provider | Springer Nature Link |
|---|---|
| Author | Cambria, Erik Chattopadhyay, Anupam Linn, Eike Mandal, Bappaditya White, Bebo |
| Copyright Year | 2017 |
| Abstract | Not unlike the concern over diminishing fossil fuel, information technology is bringing its own share of future worries. We chose to look closely into one concern in this paper, namely the limited amount of data storage. By a simple extrapolatory analysis, it is shown that we are on the way to exhaust our storage capacity in less than two centuries with current technology and no recycling. This can be taken as a note of caution to expand research initiative in several directions: firstly, bringing forth innovative data analysis techniques to represent, learn, and aggregate useful knowledge while filtering out noise from data; secondly, tap onto the interplay between storage and computing to minimize storage allocation; thirdly, explore ingenious solutions to expand storage capacity. Throughout this paper, we delve deeper into the state-of-the-art research and also put forth novel propositions in all of the abovementioned directions, including space- and time-efficient data representation, intelligent data aggregation, in-memory computing, extra-terrestrial storage, and data curation. The main aim of this paper is to raise awareness on the storage limitation we are about to face if current technology is adopted and the storage utilization growth rate persists. In the manuscript, we propose some storage solutions and a better utilization of storage capacity through a global DIKW hierarchy. |
| Starting Page | 646 |
| Ending Page | 658 |
| Page Count | 13 |
| File Format | |
| ISSN | 18669956 |
| Journal | Cognitive Computation |
| Volume Number | 9 |
| Issue Number | 5 |
| e-ISSN | 18669964 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2017-05-27 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Information technology Big Data analysis Data storage Data representation Data learning Data aggregation Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition |
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