Real Time Analytics for Characterizing the Computer User's State

Article Sidebar

PDF

ISSN online: 2255-2863

Prefijo Doi: 10.14201

Main Article Content

Davide Carneiro
Algoritmi Center/Department of Informatics, Minho University, Braga, Portugal
Portugal
Daniel Araújo
Algoritmi Center/Department of Informatics, Minho University, Braga, Portugal
Portugal
André Pimenta
Algoritmi Center/Department of Informatics, Minho University, Braga, Portugal
Portugal
Paulo Novais
Algoritmi Center/Department of Informatics, Minho University, Braga, Portugal
Portugal
Vol. 5 No. 4 (2016), Articles, pages 01-18
DOI: https://doi.org/10.14201/ADCAIJ201654118
Accepted: Nov 15, 2016
Copyright

Abstract

In the last years, the amount of devices that can be connected to a network grew significantly allowing to, among other tasks, collect data about the environment or the people in it in a non-intrusive way. This generated nowadays well-known topics such as Big Data or the Internet of Things. This also opened the door to the development of novel and interesting applications. In this paper we propose a distributed system for acquiring data about the users of technological devices in a non-intrusive way. We describe how this data can be collected and transformed to produce meaningful interaction features, that reveal the state of the individuals. We analyse the requirements of such a system, namely in terms of storage and speed, and describe three prototypes currently being used in three different domains of application.

Keywords:

Big Data, Distributed Computing, Behavioural Analysis, Keystroke Dynamics, Mouse Dynamics

Downloads

Download data is not yet available.

Article Details

References

Aiello, J. R. and Kolb, K. J., 1995. Electronic performance monitoring and social context: impact on productivity and stress. Journal of Applied Psychology, 80(3):339. https://doi.org/10.1037/0021-9010.80.3.339

Bertino, E., Bernstein, P., Agrawal, D., Davidson, S., Dayal, U., Franklin, M., Gehrke, J., Haas, L., Halevy, A., Han, J. et al., 2011. Challenges and Opportunities with Big Data. https://doi.org/10.14778/2367502.2367572

Cattell, R., 2011. Scalable SQL and NoSQL data stores. ACM SIGMOD Record, 39(4):12–27. https://doi.org/1978915.1978919.

Chang, F., Dean, J., Ghemawat, S., Hsieh, W. C., Wallach, D. A., Burrows, M., Chandra, T., Fikes, A., and Gruber, R. E., 2006. Bigtable: A Distributed Storage System for Structured Data. In Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation - Volume 7, OSDI '06, pages 15–15. USENIX Association, Berkeley, CA, USA.

Chaudhuri, S. and Dayal, U., 1997. An overview of data warehousing and OLAP technology. ACM Sigmod record, 26(1):65–74. https://doi.org/10.1145/248603.248616

Dyrbye, L. N., Thomas, M. R., and Shanafelt, T. D., 2006. Systematic review of depression, anxiety, and other indicators of psychological distress among US and Canadian medical students. Academic Medicine, 81(4):354–373. https://doi.org/10.1097/00001888-200604000-00009

Gantz, J. and Reinsel, D., 2011. Extracting value from chaos. IDC iview, (1142):9–10.

Goebert, D., Thompson, D., Takeshita, J., Beach, C., Bryson, P., Ephgrave, K., Kent, A., Kunkel, M., Schechter, J., and Tate, J., 2009. Depressive symptoms in medical students and residents: a multischool study. Academic Medicine, 84(2):236–241. https://doi.org/10.1097/ACM.0b013e31819391bb

Hwang, K.-A. and Yang, C.-H., 2009. Automated Inattention and Fatigue Detection System in Distance Education for Elementary School Students. Educational Technology & Society, 12(2):22–35.

Kejariwal, A., Kulkarni, S., and Ramasamy, K., 2015. Real time analytics: algorithms and systems. Proceedings of the VLDB Endowment, 8(12):2040–2041. https://doi.org/10.14778/2824032.2824132.

Khazaei, H., Fokaefs, M., Zareian, S., Beigi-Mohammadi, N., Ramprasad, B., Shtern, M., Gaikwad, P., and Litoiu, M., 2015. How do I choose the right NoSQL solution? A comprehensive theoretical and experimental survey. Submitted to Journal of Big Data and Information Analytics (BDIA). https://doi.org/10.3934/bdia.2016004

Lourenço, J. R., Cabral, B., Carreiro, P., Vieira, M., and Bernardino, J., 2015. Choosing the right NoSQL database for the job: a quality attribute evaluation. Journal of Big Data, 2(1):1–26. https://doi.org/10.1186/s40537-015-0025-0

Mayer-Schönberger, V. and Cukier, K., 2013. Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt. ISBN 978-0544227750.

McEwen, B. S., 2012. Brain on stress: how the social environment gets under the skin. Proceedings of the National Academy of Sciences, 109(Supplement 2):17180–17185. https://doi.org/10.1073/pnas.1121254109

Pritchett, D., 2008. Base: An acid alternative. Queue, 6(3):48–55. https://doi.org/10.1145/1394127.1394128

Soares, J. M., Sampaio, A., Ferreira, L. M., Santos, N., Marques, F., Palha, J. A., Cerqueira, J., and Sousa, N., 2012. Stress-induced changes in human decision-making are reversible. Translational psychiatry, 2(7):e131. https://doi.org/10.1038/tp.2012.59

Stonebraker, M., 2010. SQL databases v. NoSQL databases. Communications of the ACM, 53(4):10–11. https://doi.org/10.1145/1721654.1721659

Strous, R. D., Shoenfeld, N., Lehman, A., Wolf, A., Snyder, L., and Barzilai, O., 2012. Medical students' self-report of mental health conditions. International journal of medical education, 3:1. doi:10.5116/ijme.4ed1.d1e0.