Analyzing Movement Object Databases

by Tyler K
This article discusses the usage of Movement Object Databases (MOD), which provides info on objects that move over time – two examples given in the article include humans and vehicles, but could extend to animals and aircraft, and other such things. The authors of the chosen article suggest that there are techniques which could allow for further analysis of movement based objects, rather than just basic storage of information concerning them. A toolkit of sorts is proposed, which can analyze all sorts of motion trajectories, from time/space to acceleration and direction – all of which can be utilized for data-mining of trajectories. This analysis would be useful for many professionals who has a field that focuses on moving objects; the example given in the document is a large-scale traffic manager (for instance, a city) that wishes to observe the movement patterns of large vehicle traffic throughout the city. The data focuses upon the distance of two data subjects that is stored, and the information can be clustered so that the extremely large scope of the data storage can be analyzed by an individual or smaller groups.
This article has multiple points that are relevant to what has been recently learned in class: for example, the Movement Object Database analysis techniques being discussed hinge upon similarity searches, which are more evolved versions of the join and search operations we have been discussing in class, and the data-mining is a far more advanced than what we covered in class – yet still requires the same essential structure we have learned so far. Finally, the usage of clustered data has been brought up in our class before, and it is intriguing to see a real-life application of data clustering, with a real reason for its usage (reducing the amount of time to cope with all of the stored data/information”
I find this article fascinating as it shows that databases and data storage isn’t just limited to linking names and employee ID numbers to health benefits and whatnot – the future of databases goes far beyond that. This is just one example of intriguing data storage, where motion-based objects are being stored, analyzed, and utilized. It also brings up how powerful data-mining and analysis techniques are becoming – while not as shocking as my article on Target, it is still rather daunting to consider how the motion of vehicles, or humans, could be analyzed, stored, and put to use. Hopefully, the power of this information shall be used for good, rather than for bad.

Works Cited
Pelekis, N., Andrienko, G., Andrienko, N., Kopanakis, I., Marketos, G., & Theodoridis, Y. (2012). Visually exploring movement data via similarity-based analysis. Journal of Intelligent Information Systems, 38(2), 343-391.