Extracting Knowledge from Stream Behavioural Patterns

Ricardo Jesus, Mário Antunes, Diogo Gomes, Rui Aguiar

2017

Abstract

The increasing number of small, cheap devices full of sensing capabilities lead to an untapped source of information that can be explored to improve and optimize several systems. Yet, as this number grows it becomes increasingly difficult to manage and organize all this new information. The lack of a standard context representation scheme is one of the main difficulties in this research area (Antunes et al., 2016b). With this in mind we propose a stream characterization model which aims to provide the foundations of a new stream similarity metric. Complementing previous work on context organization, we aim to provide an automatic organizational model without enforcing specific representations.

References

  1. Abowd, G. D., Dey, A. K., Brown, P. J., Davies, N., Smith, M., and Steggles, P. (1999). Towards a better understanding of context and context-awareness. In Proc. of the 1st international symposium on Handheld and Ubiquitous Computing, pages 304-307.
  2. Antunes, M., Gomes, D., and Aguiar, R. (2015). Semantic features for context organization. In Future Internet of Things and Cloud (FiCloud), 2015 3rd International Conference on, pages 87-92. IEEE.
  3. Antunes, M., Gomes, D., and Aguiar, R. (2016a). Learning semantic features from web services. In Future Internet of Things and Cloud (FiCloud), 2016 4rd International Conference on. IEEE.
  4. Antunes, M., Gomes, D., and Aguiar, R. L. (2016b). Scalable semantic aware context storage. Future Generation Computer Systems, 56:675-683.
  5. Bacon, J., Bejan, A., Beresford, A., Evans, D., Gibbens, R., and Moody, K. (2011). Using real-time road traffic data to evaluate congestion. In Jones, C. and Lloyd, J., editors, Dependable and Historic Computing, volume 6875 of Lecture Notes in Computer Science, pages 93-117. Springer Berlin Heidelberg.
  6. Chen, K.-C. and Lien, S.-Y. (2014). Machine-to-machine communications: Technologies and challenges. Ad Hoc Networks, 18:3-23.
  7. Dey, A. K. (2001). Understanding and using context. Personal and ubiquitous computing, 5(1):4-7.
  8. Eriksson, J., Girod, L., Hull, B., Newton, R., Madden, S., and Balakrishnan, H. (2008). The pothole patrol: Using a mobile sensor network for road surface monitoring. In Proceedings of the 6th International Conference on Mobile Systems,Applications, and Services, pages 29-39.
  9. Krempl, G., Z?liobaite, I., BrzeziÁski, D., Hüllermeier, E., Last, M., Lemaire, V., Noack, T., Shaker, A., Sievi, S., Spiliopoulou, M., and Stefanowski, J. (2014). Open challenges for data stream mining research. SIGKDD Explor. Newsl., 16(1):1-10.
  10. Mohan, P., Padmanabhan, V. N., and Ramjee, R. (2008). Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In Proc. of the 6th ACM conference on Embedded network sensor systems, pages 323-336.
  11. Perera, C., Zaslavsky, A., Christen, P., and Georgakopoulos, D. (2014). Context aware computing for the internet of things: A survey. IEEE Communications Surveys Tutorials, 16(1):414-454.
  12. Suhr, J. K. and Jung, H. G. (2014). Sensor fusion-based vacant parking slot detection and tracking. Intelligent Transportation Systems, IEEE Transactions on, 15(1):21-36.
  13. Winograd, T. (2001). Architectures for context. Hum.- Comput. Interact., 16(2):401-419.
  14. Wortmann, F., Flüchter, K., et al. (2015). Internet of things. Business & Information Systems Engineering, 57(3):221-224.
Download


Paper Citation


in Harvard Style

Jesus R., Antunes M., Gomes D. and Aguiar R. (2017). Extracting Knowledge from Stream Behavioural Patterns . In Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-245-5, pages 419-423. DOI: 10.5220/0006373804190423


in Bibtex Style

@conference{iotbds17,
author={Ricardo Jesus and Mário Antunes and Diogo Gomes and Rui Aguiar},
title={Extracting Knowledge from Stream Behavioural Patterns},
booktitle={Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2017},
pages={419-423},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006373804190423},
isbn={978-989-758-245-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - Extracting Knowledge from Stream Behavioural Patterns
SN - 978-989-758-245-5
AU - Jesus R.
AU - Antunes M.
AU - Gomes D.
AU - Aguiar R.
PY - 2017
SP - 419
EP - 423
DO - 10.5220/0006373804190423