Detection of Semantic Events Using Changepoint and Ontology

References

[1] Al-Baltah, I.A., Ghani, A.A.A., Al-Gomaei, G.M., Abdulrazzak, F.A.H. and Kharusi, A.A.A., 2023. A scalable semantic data fusion framework for heterogeneous sensors data. Journal of Ambient Intelligence and Humanized Computing, 14(5), pp.5047–5066.

[2] Atemezing, G., Corcho, O., Garijo, D., Mora, J., Poveda-Villalón, M., Rozas, P., Vila-Suero, D. and Villazón-Terrazas, B., 2013. Transforming meteorological data into linked data. Semantic Web, 4(3), pp.285–290.

[3] Al-Baltah, I.A., Ghani, A.A.A., Al-Gomaei, G.M., Abdulrazzak, F.A.H. and Kharusi, A.A.A., 2023. A scalable semantic data fusion framework for heterogeneous sensors data. Journal of Ambient Intelligence and Humanized Computing, 14(5), pp.5047–5066.

[4] Atemezing, G., Corcho, O., Garijo, D., Mora, J., Poveda-Villalón, M., Rozas, P., Vila-Suero, D. and Villazón-Terrazas, B., 2013. Transforming meteorological data into linked data. Semantic Web, 4(3), pp.285–290.

[5] Basseville, M. and Nikiforov, I.V., 1993. Detection of abrupt changes: Theory and application. Prentice Hall.

[6] Lavielle, M., 2005. Using penalized contrasts for the change-point problem. Signal Processing, 85(8), pp.1501–1510.

[7] Truong, C., Oudre, L. and Vayatis, N., 2020. Selective review of offline change point detection methods. Signal Processing, 167, p.107299.

[8] Killick, R. and Eckley, I.A., 2014. changepoint: An R package for change point analysis. Journal of Statistical Software, 58(3), pp.1–19.

[9] Aminikhanghahi, S. and Cook, D.J., 2017. A survey of methods for time series change point detection. Knowledge and Information Systems, 51(2), pp.339–367.

[10] Cristani, M. and Cuel, R., 2005. A survey on ontology creation methodologies. International Journal on Semantic Web and Information Systems, 1(2), pp.49–69.

[11] Devaraju, A. and Kauppinen, T., 2012. Sensors tell more than they sense: Modeling and reasoning about sensor observations for understanding weather events. International Journal of Sensors Wireless Communications and Control, 2(1), pp.14–26.

[12] Velu, A. and Thangavelu, M., 2021. Hetero-GCD2RDF: Interoperable solution for climatic data. Wireless Personal Communications, 117(4), pp.3527–3551.

[13] Basseville, M. and Nikiforov, I.V., 1993. Detection of abrupt changes: Theory and application. Prentice Hall.

[14] Garrido, Á.L., Buey, M.G., Munoz, G. and Casado Rubio, J.L., 2016. AEMIX: Semantic verification of weather forecasts on the web.

[15] Gupta, S., Jain, I., Johari, P. and Lal, M., 2018. Impact of climate change on tropical cyclones frequency and intensity on Indian coasts. In: Springer Conference Proceedings, pp.359–365.

[16] Kaladevi, R., Revathi, A. and Sridhar, S., 2020. Development of background ontology for weather systems through ontology learning. International Journal of Recent Technology and Engineering, 8, pp.168–172.

[17] Kalibatiene, D. and Vasilecas, O., 2011. Survey on ontology languages. In: International Conference on Business Informatics Research, pp.124–141.

[18] Kambli, K., 2020. Throwback to a stormy year: A look at the 8 North Indian Ocean cyclones of 2019. The Times of India.

[19] Al-Baltah, I.A., Ghani, A.A.A., Al-Gomaei, G.M., Abdulrazzak, F.A.H. and Kharusi, A.A.A., 2023. A scalable semantic data fusion framework for heterogeneous sensors data. Journal of Ambient Intelligence and Humanized Computing, 14(5), pp.5047–5066.

[20] Atemezing, G., Corcho, O., Garijo, D., Mora, J., Poveda-Villalón, M., Rozas, P., Vila-Suero, D. and Villazón-Terrazas, B., 2013. Transforming meteorological data into linked data. Semantic Web, 4(3), pp.285–290.

@article{1719157,
      author = {Dr. V. Seshasrinivas, V. Thanuja},
      title = {Detection of Semantic Events Using Changepoint and Ontology},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {12},
      pages = {2482-2492},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1719157.pdf},
      abstract = {With increased availability of time series data of oceanographic and environmental variables, there is a necessity for developing a way to recognize events out of continuous observation data. To solve the problem described above, this research offers a two-stages framework to recognize semantic events utilizing change point detection and ontology-based reasoning approach. At the first stage, change point detection techniques are used to identify significant events out of raw time series data. Pruned Exact Linear Time (PELT) algorithm is applied to find points of changes in statistical characteristics of data, like mean or variance. Detected change points divide time series into homogeneous parts, each of which can be considered as an event. At the second stage, semantic interpretation of detected parts is carried out based on ontology. Knowledge about domain is represented in the form of ontology that describes relationships between parameters of the system, such as threshold value, trend and environmental conditions. In accordance with described characteristics, each part is classified to meaningful groups of normal and risk conditions. For this project, I am using the Pelt approach.},
      month = {June},
      doi = {https://doi.org/10.64388/IREV9I12-1719157}
  }