The Detection of Emerging Trends Using Wikipedia Traffic Data and Context Networks - Fig 3

<p>Relative attraction and trends based on Google Trends data (a, b, c) and on access-rate time series from Wikipedia (d) for names of NoSQL databases as search terms (see legends) versus time in months since (a, b) 2010-01-01, (c) 2004-01-01, and (d) 2009-01-01. The approximately linear slopes in (a) differ, and very strong fluctuations are found for Hadoop (black) and SOLR (green), so that the different curves can hardly be compared. In (b) the Google Trends data from (a) have been normalized for zero average and unit standard deviation to facilitate a comparison. However, ambiguous keywords can strongly influence the results as shown in in (c), where raw Google Trends data acquired simultaneously for eight different keywords (using an unofficial API software [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141892#pone.0141892.ref031" target="_blank">31</a>]) is shown. Here, the maximum at interest level 100 (arbitrary units from Google Trends) occurs only for one of the curves in 2015 and is not shown in the plot. Wikipedia access-rate data in (d), corresponding to the time range of the yellow box in (c), indicate a jump in user interest for pages about Apache Hadoop, Apache Zookeeper, and Apache SOLR, which was not visible in the Google Trends data. The gray boxes in (d) indicate times for which no Wikipedia access data is available for technical reasons.