Open Access Open Access  Restricted Access Subscription or Fee Access

Scalable Data Analysis and Query Processing


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irea.v7i3.17012

Abstract


Scalable data is the demand of many of the emerging technologies. In order to target scalability, query processing plays an important role. The target is to achieve the maximum performance in terms of less execution time and more output. This could be achieved with any selected data and implementing advanced algorithms in any platform. This paper has worked in ArcMap in order to handle data of maps through different layers. Map reduction in size eases the process of query processing and generates the resultant records much faster. In addition, query category contributes to scalability as well. Classifying a compound query into a simple one adds positive impact on the results. Query payload and cost are controlled by maintaining execution time of the query and enhancing retuned records per command. This is helpful in analyzing different map layers for selected area of interest. Scalable map data is selected, analyzed with different map layers and results are obtained of processed queries that clearly indicates the successful achievement of scalability of the data through controlled process of query handling in smart and efficient way.
Copyright © 2019 Praise Worthy Prize - All rights reserved.

Keywords


ArcGIS; ArcMap; Scalable Data; Query Processing; Query Execution

Full Text:

PDF


References


Muhammad Haroon, Query Processing and Optimization in Distributed Database Systems, International Journal of Modern Computation, Information and Communication Technology, 2018; 1(4):83-87.
ISSN: 2581-5954 http://ijmcict.gjpublications.com

Hasan W. Optimization of SQL Queries for Parallel Machines. LNCS 1182, Springer-Verlag, 1996.

S. Agarwal, H. Milner, A. Kleiner, A. Talwalkar, M. I. Jordan, S. Madden, B. Mozafari, and I. Stoica. Knowing when you’re Wrong: building fast and reliable approximate query processing Systems. In SIGMOD, pages 481–492, 2014.
https://doi.org/10.1145/2588555.2593667

MySQL, A. B. Mysql 5.1 reference manual, 2009 Accessible in URL: http://dev.mysql.com/doc, last Access: 10/10/2017.

Abhaya Kumar Sahoo1, Sundar Sourav Sarangi2, Rachita Misra, A Comparison Study among GPU and Map Reduce Approach for Searching Operation on Index file in Database Query Processing, 2015 International Conference on Man and Machine Interfacing (MAMI), 978-1-5090-0225-2/15©2015 IEEE.
https://doi.org/10.1109/mami.2015.7456608

B. Cao, J. Wang, J. Fan, J. Yin, and T. Dong, Querying similar Process models based on the Hungarian algorithm, IEEE Transactions on Services Computing, Vol. 14, No.8, pp.1-14, December 2015.
https://doi.org/10.1109/tsc.2016.2597143

Srinivas Karthik , Jayant R. Haritsa, Fellow, IEEE, Sreyash Kenkre, Vinayaka Pandit, and Lohit Krishnan , Platform-Independent Robust Query Processing, IEEE Transactions On Knowledge And Data Engineering, Vol. 31, No. 1, January 2019.
https://doi.org/10.1109/tkde.2017.2664827

Sinnott, R.O., Chhetri, P., Gong, Y., Macaulay, A., Voorsluys, W.: Privacy-preserving Data Linkage through Blind Geo-spatial, Data Aggregation, The IEEE International Symposium on Big Data Security on Cloud (BigDataSecurity 2015), New York, USA, (August 2015).
https://doi.org/10.1109/hpcc-css-icess.2015.80

R. O. Sinnott, W. Voorsluys, A scalable Cloud-based system for data-intensive spatial analysis, Int J Softw Tools Technol Transfer (2016) 18:587–605, Published online: 28 August 2015 © Springer-Verlag Berlin Heidelberg 2015.
https://doi.org/10.1007/s10009-015-0398-6

Archana Dhankar, Vikram Singh, A Scalable Query Materialization Algorithm for Interactive Data Exploration, 2016 Fourth International Conferenceon Parallel, Distributed and Grid Computing, 978-1-5090-3669-1/16© 2016 IEEE
https://doi.org/10.1109/pdgc.2016.7913129

S. Goil and A. Choudhary, A parallel scalable infrastructure for OLAP and data mining, Proceedings. IDEAS'99. International Database Engineering and Applications Symposium (Cat. No.PR00265), Montreal, Quebec, Canada, 1999, pp. 178-186.
https://doi.org/10.1109/ideas.1999.787266

A. Shukla, P.M. Deshpande, J. Naughton, and K. Ramaswamy. Storage estimation for multidimensional aggregates in the resence of hierarchies. In Proc. of the 22nd International VLDB Conference, May 1996.

Dimitris Stripelis, Jos´e Luis Ambite, Yao-Yi Chiang, Sandrah P. Eckel, and Rima Habre, A Scalable Data Integration and Analysis Architecture for Sensor Data of Pediatric Asthma, ,2017 IEEE 33rd International Conference on Data Engineering, 2375-026X/17 © 2017 IEEE Computer Society.
https://doi.org/10.1109/icde.2017.198

Mariam Malak Fahmy, Iman Elghandour, Magdy Nagi , CoS-HDFS: Co-Locating Geo-Distributed Spatial Data in Hadoop Distributed File System, 2016 IEEE/ACM 3rd International Conference on Big Data Computing, Applications and Technologies, BDCAT’16, December 06-09, 2016, Shanghai, China c 2016 ACM. ISBN 978-1-4503-4617-7/16/12.
https://doi.org/10.1145/3006299.3006314

Alexander Alexandrov Andreas Salzmann, Emma in Action: Declarative Dataflows for Scalable Data Analysis, SIGMOD’16, June 26-July 01, 2016, San Francisco, CA, USA © 2016 Publication rights licensed to ACM. ISBN 978-1-4503-3531-7/16/06.
https://doi.org/10.1145/2882903.2899396

Sooyoung Cha, Sehun Jeong, Hakjoo Oh, A scalable learning algorithm for data-driven program analysis, Information and Software Technology,104 (2018) 1–13, Available online 11 July 2018, 0950-5849/ © 2018 Elsevier B.V. All rights reserved.
https://doi.org/10.1016/j.infsof.2018.07.002

W. Ban, J. Lin, J. Tong, and S Li, "Query optimization of distributed database based on parallel genetic algorithm and max-min ant system", 2015 8th International Symposium on Computational Intelligence and Design (ISCID) pp.581-585, 2015.
https://doi.org/10.1109/iscid.2015.199

Y. Liu, S. Guo, S. Hu, T. Rab, H. A. Jacobsen, and J. Li, Performance evaluation and optimization of multi-dimensional indexes in hive, IEEE Transactions on Services Computing, Vol. 14, No., pp.1-14, August 2015.
https://doi.org/10.1109/tsc.2016.2594778

X. Liu, L. Chen, and C. Wan, LINQ: A framework for location aware indexing and query processing, IEEE Transactions on Knowledge and Data Engineering, Vol.27, No.5, pp.1288-1300, May 2015.
https://doi.org/10.1109/tkde.2014.2365792

Q. Shi, B. Du, and L. Zhang, Spatial coherence-based batchmode active learning for remote sensing image classification, IEEE Transactions On Image Processing, Vol. 24, No. 7, pp.2037-2050, July 2015.
https://doi.org/10.1109/tip.2015.2405335

M. Chen, S. Huang, X. Fu, X. Liu, and J. He, Statistical model checking-based evaluation and optimization for cloud workflow resource allocation, IEEE Transactions on Cloud Computing, pp.1-14, 2016.
https://doi.org/10.1109/tcc.2016.2586067

T. Li, J. Tang, and J. Xu, Performance modeling and predictive scheduling for distributed stream data processing, IEEE Transactions on Big Data, March 2015.
https://doi.org/10.1109/bigdata.2015.7363773

O. Diallo, J.P.C. Rodrigues Joel, M. Sene, and J. Lloret, Distributed database management techniques for wireless Sensor networks, IEEE Transactions on Parallel and Distributed Systems, Vol. 26, No. 2, pp.604-620, February 2015.
https://doi.org/10.1109/tpds.2013.207

L. Wan, K. Tang, M. Li, Y. Zhong, and A.K. Qin, Collaborative active and semi supervised learning for hyper Spectral remote sensing image classification, IEEE Transactions On Geoscience and Remote Sensing, Vol. 53, No. 5, pp.2384-2396, May 2015.
https://doi.org/10.1109/tgrs.2014.2359933

Mr. A. P. Chendke, Dr. Mrs. S. S. Sherekar, Dr. V. M. Thakare, Advanced Query Processing and its Optimization for Mobile Computing, Environment, proceedings of the Second International Conference on Inventive Systems and Control (ICISC 2018) IEEE Xplore Compliant - Part Number:CFP18J06-ART, ISBN:978-1-5386-0807-4.
https://doi.org/10.1109/icisc.2018.8399085

DVD Part Number:CFP18J06DVD, ISBN:978-1-5386-0806-7

Singh, Hari & Bawa, Seema. (2016). Spatial Data Analysis with ArcGIS and MapReduce. 2016 International Conference on Computing, Communication and Automation (ICCCA).
https://doi.org/10.1109/ccaa.2016.7813687

Nurhendratno, S., Setyowati, M., Vertical Fragmentation Impacts in the Performance and Management Database Health Information System (Comparative Study of Fragmentation Vertically Algorithm), (2016) International Journal on Information Technology (IREIT), 4 (4), pp. 97-104.

Azough, S., Bellafkih, M., Bouyakhf, E., Adapted Learning Path Using Genetic Algorithm: Introducing Fuzzy Logic, (2013) International Journal on Information Technology (IREIT), 1 (4), pp. 261-268.


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize