Author : Ch.Rajya Lakshmi 1
Date of Publication :10th August 2017
Abstract: Now a days Big Data has defined very large amount of data, it includes both structured and unstructured format. The structured data analyzing is very easy task but an unstructured data analyzing is very difficult that can be produced by an individuals (eg. Twitter data)it also gathered by sensors(eg. satellites, videos) which can range from giga bytes, tera bytes and peta bytes. Big Data entitles more and more data that can be analyzed through various analyzing techniques. If the right analytic method is applied to unstructured datasets we can easily analyze and classifying various patterns, But at the same time will consider efficiency and scale of Data. In the real world the major issue of Big Data is early warning predictions is the use of Satellite imagery and Radar Sensor data. In the Satellite imagery data could reach a million derived spatial objects such data querying ,managing and various image patterns classification is very difficult task. So a proper architecture should be proposed to gain knowledge about Big Data for analyzing various Satellite imagery patterns classifications with hadoop technology. In the proposed architecture differentiate various classification methods for various satellite imagery pattern classification methods and also proposing Google’s Map reduce C4.5 Algorithm for effective classification to increase performance of patterns classification and increasingly large volume of Data sets to results both time efficiency and scalabilty. This research is carrying based on NASA Satellite data and Twitter data and also in weather forecasting.
Reference :
-
- I.Chebbi,w.boulila,I.R.Farah “Improvement of satellite image classification approach based on Hadoop/Map Reduce” in IJESRT ,Volume 3 , January 2017,pp 435-442.
- Ablimit Aji, Fusheng Wang, Hoang Vo, Rubao Lee, Qiaoling Liu, Xiaodong Zhang, andJoel Saltz “Hadoop-GIS: A High Performance Spatial Data WarehousingSystem over MapReduce”Proceedings VLDB Endowment. Author manuscript; available in PMC 2013 October 31.
- Yaman Kumar Gunturi*, K. Kishore Raju “RealBDA: A Real Time Big Data Analytics For Remote Sensing Data By Using Mapreduce Paradigam” in IJESRT ,Volume 3 , January 2017,pp. 435-442.
- .Sarade ShrikantD.,Ghule Nilkanth B.,Disale Swapnil P sane Sandip R. “Large Scale Satellite Image Processing Using Hadoop Distributed System” in IJARCET Volume 3 Issue 3, March 2014 pp. 731-735.
- Ismail Elkhrachy “Flash Flood Hazard Mapping Using Satellite Images and GIS Tools: A case study of Najran City, Kingdom of Saudi Arabia (KSA)” in The Egyptian Journal of Remote Sensing and Space Sciences 4 August 2015 pp 261–278
- Vaibhavi S. Shukla, Jay Vala in “A Survey on Image Mining, its Techniques and Application”in IJCATM Volume 133 – No.9, January 2016,PP.12-15.
- P. Chandarana and M. Vijayalakshmi, “Big Data analytics frameworks,” in Proc. Int. Conf. Circuits Syst.Commun. Inf. Technol. Appl. (CSCITA), 2014,pp 430–434.
- PThamilselvan, Dr. J. G. R. Sathiaseelan “A Comparative Study of Data Mining Algorithms for Image Classification” in I.J. Education and Management Engineering, 2015, 2, 1-9 DOI: 10.5815/ijeme.2015.02.01
- Yan, R., Fleury, M.-O., Merler, M., Natsev, A. and Smith, J.R. (2009) Large-Scale Multimedia Semantic ConceptModeling Using Robust Subspace Bagging and MapReduce. Proceedings of the 1st ACM Workshop on Large-ScaleMultimedia Retrieval and Mining, Beijing, 23 October 2009, 35-42.
- Almeer, M.H. (2012) Cloud Hadoop MapReduce For Remote Sensing Image Analysis. Journal of Emerging Trends in Computing and Information Sciences, 3, 637-644.
- Jiong Xie, Shu Yin, Xiaojun Ruan, Zhiyang Ding, Yun Tian,James Majors, Adam Manzanares, and Xiao Qin “Improving MapReduce Performance through Data Placement inHeterogeneous Hadoop Clusters” in Proc. 19th Int’l Heterogeneity in Computing Workshop, Atlanta, Georgia, April 2010.
- Cohen, S. E. 2013. Sandy Marked a Shift for Social Media Use in Disasters. Emergency Management http://www.emergencymgmt.com/disaster/Sandy-SocialMediaUs in Disasters.html#.UTkc9hoaKdo.facebook (last accessed 9 March 2013).
- Beth Tellman (1), Bessie Schwarz (2), Ryan Burns (3), Christiaan Adams (4) “Big Data in the Disaster Cycle: Overview of use of big data and satellite imaging in monitoring risk and impact of disasters” in UN Development Report 2015.
- Joyce,K.E., Belliss,S.E., Samsonov,S.V., McNeill,S.J., Glassey,P.J.(2009)”.A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters” in Progress in Physical Geography,33(2), 183207.doi: 10.1177/0309133309339563
- Er.Navjot Kaur, Er. Yadwinder Kaur “Object classification Techniques using Machine Learning Model In (IJCTT) – Volume 18 Number 4 – Dec 2014,pp 170- 174.
- Anil K Goswami, Swati Sharma, Praveen Kumar “Nearest Clustering Algorithm for Satellite ImageClassification in Remote Sensing Applications” in IJCSIT Vol. 5 (3) , 2014, 3768-3772,pp 3768-3772.