Abstract
Due to the wide use of low-cost sensors in environmental monitoring, there is an increasing concern on the stability of marine sensor network (MSN) and reliability of data collected. With the dramatic growth of data collected with high sampling frequency from MSN, the query answering for environment phenomenon at a specific time is inevitably compromised. This study proposes a simple approximate query answering system to improve query answering service, which is motivated by sea water temperature data collected in Tasmania Marine Analysis and Network (TasMAN). The paper first analyses the problems of special interest in missing readings in time series of sea water temperature. Some current practices on approximate query answering and forecasting are reviewed, and after that some methods of gap filling and forecasting (e.g. Linear Regression (LR), Quadratic Polynomial Regression (QPR), Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA)) are introduced in designing the simple approximate query answering system. It is followed by experiments on gap filling of time series with artificial noise made in the original time series. Finally, the comparison of different algorithms in terms of accuracy, computation time, extensibility (i.e. scalability) is presented with recommendations. The significance of this research lies in the evaluation of different simple methods in forecasting and gap filling in real time series, which may contribute to studies in time series analysis and knowledge discovery, especially in marine science domain.
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References
Shahriar, M.S., de Souza Jr., P.A., Timms, G.P.: Smart query answering for marine sensor data. Sensors 11(3), 2885–2897 (2011)
Timms, G.P., McCulloch, J.W., McCarthy, P., Howell, B., de Souza Jr., P.A., Dunbabin, M.D., Hartmann, K.: The Tasmanian marine analysis network (TasMAN). In: IEEE OCeans, pp. 1–6 (2009)
Acharya, S., Gibbons, P.B., Poosala, V., Ramaswamy, S.: The Aqua approximate query answering system. SIGMOD Rec. 28(2), 574–576 (1999)
Mestekemper, T., Windmann, M., Kauermann, G.: Functional hourly forecasting of water temperature. International Journal of Forecasting 26, 684–699 (2010)
Timms, G.P., de Souza, P.A., Reznik, L.: Automated assessment of data quality in marine sensor networks. In: Proceedings of Oceans 2010 IEEE-Sydney, pp. 1–5 (2010)
Deng, F.: Approximation algorithms for frequency related query processing on streaming data. PhD thesis (2007) ISBN: 978-0-494-32950-4
Madden, M.R., Franklin, M.J., Hellerstein, J.M., Hong, W.: TinyDB: an acquisitional query processing system for sensor networks. ACM Trans. Database Syst. 30(1), 122–173 (2005)
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Chen, Z., Shahriar, M.S., Kang, B.H. (2012). User-Centric Recommendation-Based Approximate Information Retrieval from Marine Sensor Data. In: Richards, D., Kang, B.H. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2012. Lecture Notes in Computer Science(), vol 7457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32541-0_5
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DOI: https://doi.org/10.1007/978-3-642-32541-0_5
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