Abstract
Data collected now-a-days is quite huge in size. Also in the future, data will continue to grow at a much higher rate. The survey highlights the basic concepts of big data analytics and its application in the domain of weather prediction. More the data available to us, more accurate will be the results. Relatively small change in the accuracy of models benefits a lot to society. Huge number of statistical and predictive models for weather prediction exists in the literature but the methods are too time consuming and cannot handle unstructured as well as huge datasets. To overcome this problem, various authors have explored the Apache Hadoop Map Reduce framework for processing and storing Big Data. In this paper, we have discussed and analysed the work done by various researchers on weather prediction using big data analytics.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Evert, F., Fountas, S., Jakovetic, D., Cnnojevic, V., Travlos, I., Kempenaar, C.: Big data for weed control and crop protection. In: Big Data for Weed Control and Crop Protection. Weed Research (2017). https://doi.org/10.1111/wre.12255
Assuncao, M., Calheiros, R., Bianchi, S., Netto, M., Buyya, R.: Big data computing and clouds: trends and future directions. J. Parallel Distrib. Comput. 79–80, 3–15 (2015). https://doi.org/10.1016/j.jpdc.2014.08.003
Giacalone, M., Cusatelli, C., Santarcangelo, V.: Big data compliance for innovative clinical models. Big Data Res. 12, 35–40 (2018). https://doi.org/10.1016/j.bdr.2018.02.001
Dang, Z., Zhu, X., Cheng, D., Zong, M., Zhang, S.: Efficient KNN classification algorithm for big data. J. Neurocomput. 195, 143–148 (2016). https://doi.org/10.1016/j.neucom.2015.08.112
Zhao, W., Ma, H., He, Q.: Parallel K-Means clustering based on MapReduce. In: Jaatun, M.G., Zhao, G., Rong, C. (eds.) CloudCom 2009. LNCS, vol. 5931, pp. 674–679. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10665-1_71
Maillo, J., Triguero, I., Herrera, F.: A MapReduce-based k-Nearest neighbor approach for big data classification. In: IEEE Trustcom/BigDataSE/ISPA (2015). https://doi.org/10.1109/trustcom.2015.577
Tsai, C., Lai, C., Chao, H., Vasilakos, A.: Big data analytics: a survey. J. Big Data 2, 21 (2015). https://doi.org/10.1186/s40537-015-0030-3
Majumdar, J., Naraseeyappa, S., Ankalaki, S.: Analysis of agriculture data using data mining techniques: application of big data. Open J. Big Data 4, 20 (2017). https://doi.org/10.1186/s40537-017-0077-4
Fan, W., Chong, C., Xiaoling, G., Hua, Y.: Prediction of crop yield using big data. In: 8th International Symposium on Computational Intelligence and Design. IEEE (2015). https://doi.org/10.1109/iscid.2015.191
Bendre, M.R., Thool, R.C., Thool, V.R.: Big data in precision agriculture: weather forecasting for future farming. In: 1st International Conference on Next Generation Computing Technologies. IEEE (2015). https://doi.org/10.1109/ngct.2015.7375220
Sneha, N., Majumdar, J.: Big data application in agriculture to maximize the rice yield crop production using data mining techniques. Int. J. Innov. Res. Comput. Commun. Eng. (2017). https://doi.org/10.15680/ijircce.2017.0505045
Kushwaha, A., Bhattachrya, S.: Crop yield prediction using agro algorithm in hadoop. Int. J. Comput. Sci. Inf. Technol. Secur. (IJCSITS) 5(2), 271–274 (2015)
Nguyen, V., Nguyen, S., Kim, K.: Design of a platform for collecting and analyzing agricultural big data. J. Digit. Contents Soc. 18(1), 149–158 (2017). https://doi.org/10.9728/dcs.2017.18.1.149
Reddy, P., Babu, A.: Survey on weather prediction using big data analystics. In: Second International Conference on Electrical, Computer and Communication Technologies. IEEE (2017). https://doi.org/10.1109/icecct.2017.8117883
Shobha, N., Asha, T.: Monitoring weather based meteorological data: clustering approach for analysis. In: International Conference on Innovative Mechanisms for Industry Applications. IEEE (2017). https://doi.org/10.1109/icimia.2017.7975575
Nikam, V., Meshram, B.: Modeling rainfall prediction using data mining method. In: Fifth International Conference on Computational Intelligence, Modelling and Simulation. IEEE (2013). https://doi.org/10.1109/cimsim.2013.29
Tsagalidis, E., Evangelidis, G.: The effect of training set selection in meteorological data mining. In: Fourteenth Panhellenic Conference on Informatics. IEEE (2010). https://doi.org/10.1109/pci.2010.37
Navadia, S., Yadav, P., Thomas, J., Shaikh, S.: Weather prediction: a novel approach for measuring and analyzing weather data. In: International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud). IEEE (2017). https://doi.org/10.1109/i-smac.2017.8058382
Anchalia, P., Roy, K.: The k-Nearest neighbor algorithm using map reduce paradigm. In: Fifth International Conference on Intelligent Systems, Modelling and Simulation. IEEE (2014). https://doi.org/10.1109/isms.2014.94
Fang, W., Sheng, V.S., Wen, X., Pan, W.: Meteorological data analysis using MapReduce. Sci. World J. (2014). https://doi.org/10.1155/2014/646497
Ismail, K., Majid, M., Zain, J., Abu Bakar, N.: Big data prediction framework for weather temperature based on MapReduce algorithm. In: Conference on Open Systems. IEEE (2016). https://doi.org/10.1109/icos.2016.7881981
Riyaz, P.A., Varghese, S.: Leveraging map reduce with hadoop for weather data analytics. IOSR J. Comput. Eng. 17(3), 6–12 (2015). https://doi.org/10.9790/0661-17320612
Mazhar, A., Ikram, M.T., Butt, N.A., Butt, A.J.: Do we really have to consider data mining techniques for meteorological data. In: Fourth International Conference on Aerospace Science and Engineering. IEEE (2015). https://doi.org/10.1109/icase.2015.7489525
Marjani, M., et al.: Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access 5, 5247–5261 (2017). https://doi.org/10.1109/ACCESS.2017.2689040
Alves, G.M., Cruvinel, P.E.: Big data environment for agricultural soil analysis from CT digital images. In: Tenth International Conference on Semantic Computing. IEEE (2016). https://doi.org/10.1109/icsc.2016.80
Mohapatra, S., Upadhyay, A., Gola, C.: Rainfall prediction based on 100 years of meteorological data. In: International Conference on Computing and Communication Technologies for Smart Nation. IEEE (2017). https://doi.org/10.1109/ic3tsn.2017.8284469
Pandey, A., Agrawal, C., Agrawal, M.: A Hadoop based weather prediction model for classification of weather data. In: Second International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE (2017). https://doi.org/10.1109/icecct.2017.8117862
Kumar, M., Nagar, M.: Big data analytics in agriculture and distribution channel. In: Proceedings of the IEEE, International Conference on Computing Methodologies and Communication (2017). https://doi.org/10.1109/iccmc.2017.8282714
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mittal, S., Sangwan, O.P. (2019). Big Data Analytics Using Data Mining Techniques: A Survey. In: Luhach, A., Singh, D., Hsiung, PA., Hawari, K., Lingras, P., Singh, P. (eds) Advanced Informatics for Computing Research. ICAICR 2018. Communications in Computer and Information Science, vol 955. Springer, Singapore. https://doi.org/10.1007/978-981-13-3140-4_24
Download citation
DOI: https://doi.org/10.1007/978-981-13-3140-4_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3139-8
Online ISBN: 978-981-13-3140-4
eBook Packages: Computer ScienceComputer Science (R0)