From: A bibliometric approach to tracking big data research trends
Author keyword | 1980–2015 TP | 1980–1999 TP (%) | 2000–2009 TP (%) | 2010–2015 TP (%) |
---|---|---|---|---|
Machine learning | 757 | 48 (0.06) | 304 (0.40) | 405 (0.53) |
MapReduce | 514 | N/A | 24 (0.04) | 490 (0.95) |
Data warehouse(s)/warehousing | 353 | 11 (0.03) | 215 (0.60) | 127 (0.35) |
Big data | 292 | N/A | N/A | 292 (1) |
Hadoop | 236 | N/A | 5 (0.02) | 231 (0.97) |
Cloud computing | 232 | N/A | 4 (0.01) | 228 (0.98) |
Data center(s) | 232 | N/A | 40 (0.17) | 192 (0.82) |
Data mining | 181 | 4 (0.02) | 80 (0.44) | 97 (0.53) |
Support vector machine(s) | 180 | N/A | 64 (0.35) | 116 (0.64) |
Sentiment analysis | 147 | N/A | 6 (0.04) | 141 (0.95) |
Classification(s)/classifier(s) | 112 | 4 (0.03) | 53 (0.47) | 55 (0.49) |
Neural network(s) | 85 | 9 (0.10) | 41 (0.48) | 35 (0.41) |
Performance | 84 | N/A | 14 (0.16) | 70 (0.83) |
Energy efficiency | 84 | N/A | 4 (0.04) | 80 (0.95) |
Online analytic(al) processing (OLAP) | 77 | N/A | 47 (0.61) | 30 (0.38) |
Virtualization | 64 | N/A | 14 (0.21) | 50 (0.78) |
Feature selection | 57 | N/A | 28 (0.49) | 29 (0.50) |
Cluster/clustering | 54 | 2 (0.03) | 16 (0.29) | 36 (0.66) |
Opinion mining | 59 | N/A | 5 (0.10) | 44 (0.89) |
Scheduling | 47 | N/A | 5 (0.10) | 42 (0.89) |