Open Access   Article

Different Query Optimization Techniques (QOT) using Data Mining Technology

I. Shahina Begam1 , K. Tajudin2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-6 , Page no. 1479-1487, Jun-2018


Online published on Jun 30, 2018

Copyright © I. Shahina Begam, K. Tajudin . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: I. Shahina Begam, K. Tajudin, “Different Query Optimization Techniques (QOT) using Data Mining Technology”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1479-1487, 2018.

MLA Style Citation: I. Shahina Begam, K. Tajudin "Different Query Optimization Techniques (QOT) using Data Mining Technology." International Journal of Computer Sciences and Engineering 6.6 (2018): 1479-1487.

APA Style Citation: I. Shahina Begam, K. Tajudin, (2018). Different Query Optimization Techniques (QOT) using Data Mining Technology. International Journal of Computer Sciences and Engineering, 6(6), 1479-1487.

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Data mining is one of the main research areas to find particular data from the large set of data. The main aim of this paper is to give more knowledge about the agriculture sector. Agriculture is one of the main economic parts of growing country. Agricultural statistical data from India as been taken here the state of Kerala. To cover Kerala state 14 districts cropped data analysis, in the 12 years pattern of statistical dataset, start from 2005 to 2017. Utilization of Query Optimization Techniques (QOT), K-Means clustering and Filter Techniques (FT). The QOT analysation is to provide variety of query generation and reports. The K-Means clustering, is usage of spatio-temporal cluster data mining techniques. It also provides changes report of the dataset. Using clustering analysis is the process of discovering groups. The FT is used to filter the data season wise and found maximum production of rice occurrence district report.

Key-Words / Index Term

Cropped area-Grouping-Query optimization-Maximum Production


[1]Anusha A. Shettar , Shanmukhappa A. Angadi “Efficient Data Mining Algorithms For Agriculture Data”,International Journal of Recent Trends in Engineering & Reseach(IJRTER), Volume 02, Issue 09; September - 2016 [ISSN: 2455-1457] PP 141-149.
[2] Nirav Desai, GeetikaKalra, Amit Khandelwal, Shashank Mishra, TejaswiniApte “ A Design Of A Data Warehouse And Use Of Data Mining Techniques For Analysis Of Risk Factors Affecting Agriculture In India” IOSR Journal of Agriculture and Veterinary Science (IOSR-JAVS) e-ISSN: 2319-2380, p-ISSN: 2319-2372. Volume 8, Issue 10 Ver. II (Oct. 2015), PP 81-88.
[3] M. Kannan et. al. “Rainfall Forecasting Using Data Mining Technique”, International Journal of Engineering and Technology Vol.2 (6), 2010, 397-401.
[4] Sarisa Pinkham, Kanyarat Bussaban, “An Approximation of Daily Rainfall by Using a Pixel Value Data Approach”,World Academy of Science, Engineering and Technology International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering Vol:8, No:5, 2014.
[5] Narayanan Balakrishnan1 and Dr.Govindarajan Muthukumarasamy , “ Crop Production-Ensemble Machine Learning Model for Prediction”, International Journal of Computer Science and Software Engineering (IJCSSE), Volume 5, Issue 7, July 2016. ISSN (Online): 2409-4285, Page: 148-153.
[6] R. Lakshmi Priya and G. Manimannan, “ Rainfall Fluctuation and Region wise Classification in Tamilnadu: Using Geographical Information System”, IOSR Journal of Mathematics (IOSR-JM) e-ISSN: 2278-5728, p-ISSN: 2319-765X. Volume 10, Issue 5 Ver. IV (Sep-Oct. 2014), PP 05-12.
N. Appala Raju et al., “study on spatial distribution of groundwater quality in vizianagaram district of andhra pradesh, india”, International Journal of Science, Environment and Technology, ISSN 2278-3687 (O) , Vol. 3, No 1, 2014, 148 – 160.
[7]Durga Karthik and K.Vijayarekha “multivariate data mining techniques for assessing water potability”, Vol. 7 | No.3 |256 – 259 | July – September | 2014 ISSN: 0974-1496 | e-ISSN: 0976-0083 | CODEN: RJCABP .
[8]Faisal Aburub, Wael Hadi, “Predicting Groundwater Areas Using Data Mining Techniques: Groundwater in Jordan as Case Study”, World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:10, No:9, 2016.
[9]Faridi et al., “ Ground Water Quality Assessment using Data Mining Techniques”, Volume 5, Issue 6, June 2015 ISSN: 2277 128X.
[10]Himanshu Trivedi, Dr. Amit Dutta, “Parametric Analysis of Water Resource Data (E-Governance Projects) Using Data Mining Techniques”, International Journal of Advanced Trends in Computer Science and Engineering, Vol. 3 , No.2, Pages : 04 - 07 (2014) Special Issue of ICACE 2014 - Held on March 10, 2014 in Hotel Sandesh The Prince, Mysore, India.
[11]Jalna (Maharashtra-India)”, Int. Journal Of Engineering Research And Applications Issn : 2248-9622, Vol. 5, Issue 2, ( Part -5) February 2015, Pp.20-29.
[12]Kamakshaiah.Kolli and R. Seshadri, “ Ground Water Quality Assessment using Data Mining Techniques”, International Journal of Computer Applications (0975 – 8887) Volume 76– No.15, August 2013.
[13]K.Yoo et al., “Decision tree-based data mining and rule induction for identifying hydrogeological parameters that influence groundwater pollution sensitivity”, Journal of Cleaner Production 122 (2016) 277-286.
[14]L.Yeshodha et al., “ Comparative Analysis for Estimation of Groundwater Potential A-Case Study for Hosur Block, Krishnagiri District , Tamil Nadu”, international journal of innovative research in technology&science | volume 1, number5.
[15]Nallan, S. , & Armstrong, L. “ Assessment of Climate Change Effect on Water Harvesting Structures in Rainfed Regions: Geospatial Data Mining Approach” Proceedings of The Third National Conference on Agro-Informatics and Precision Agriculture 2012. (pp. 201-204). Hyderabad, India. Allied Publishers PVT LTD.
[16]M Waseem Ashfaque et al., “ Robust Strategies of GIS and Geospatial Data mining techniques for drinking ground water quality management, challenges and issues of Drought Case study
[17]Oliver López-Corona et al., “Data Mining of Historic Hydrogeological and Socioeconomic Data Bases of the Toluca Valley, Mexico”, Journal of Water Resource and Protection, 2016, 8, 522-533 Published Online April 2016 in SciRes.,
[18]Pratap Singh Solanki and R. S. Thakur , “ A Review of Literature on Water Resource Management Using Data Mining Techniques”, International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14 | Impact Factor (2015): 6.391.
[19] Rosaida Rosly et al., “ The Study on the Accuracy of Classifiers for Water Quality Application”, International Journal of u- and e- Service, Science and Technology Vol.8, No.3 (2015), pp.145-154
[22] Ramprasad Kundu et al., “Spatial Growth Pattern of Potato in West Bengal using Multi-temporal MODIS NDVI Data”,IJCSE., Vol.6 , Issue.6 , pp.55-61, Jun-2018.
[23] Kone Chaka et al., “performance comparison of the knn and svm classification algorithms in the emotion detection system emotica”, Int J Sens Netw Data Commun, an open access journal Volume 7 • Issue 1 • 1000153 ISSN: 2090-4886.