Today we find ourselves in a situation where there is an overabundance of data in information networks. This being the case, there is an overwhelming need to identify pieces of data that could be critical in decision making. Time and time again, graph theory has played a critical role in facilitating this decision making. Here, the semantics of data are considered pivotal when compared to the content.

In this paper, the authors illustrate a unique method for identifying correlations between data elements contained on the Internet. The uniqueness of their approach lies in the fact that they use social and spatiotemporal graphs to perform the required analysis. The authors use a real-life situation to illustrate their technique, along with the associated algorithm and mathematics. The real-life situation considered here is that of various electronic items located in the kitchen and their usage. The items are tracked using radio-frequency identification (RFID)-based mobility detection.

This is a very interesting research topic and the associated experiment is well illustrated. The application of this presented method may be multi-fold and can further the science described in the paper.