Comparative Study of Disk Resident and Column Oriented Memory Resident Technique for Healthcare Big Data Management Using Retrieval Time

  • Famutimi R.F. Bowen University, Computer Science & Info. Technology Dept, Iwo, Nigeria
  • Ibitoye A.O. Bowen University, Computer Science & Info. Technology Dept, Iwo, Nigeria
  • Ikono R.N Obafemi Awolowo University, Computer Science & Engineering Dept, Ile Ife, Nigeria
  • Famutimi T.I. Bowen University, Computer Science & Info. Technology Dept, Iwo, Nigeria
Keywords: Big Data, columnar-oriented, caching, disk-I/O, disk-resident, memory-resident, speed-up.

Abstract

The rate at which information are being shared among people of diverse discipline, is continuously increasing the volume of data available for different forms of processing and storage. The channels for collecting data is increasing on daily basis; customers need to supply data to business owners; online social media keep on evolving; educational institutions are faced with keeping records of ever growing students’ enrollment and keeping their records after graduating is now a challenge; health institutions keep on experiencing unprecedented growth in child birth on daily basis and the need to keep and maintain adequate health records is a necessity. This resultant data flood has called for the need to explore new cost effective storage options and analysis techniques in other to benefit from the dividends of Big Data. Some of the approaches involve investing more on hardware storage devices, some involve exploring other locations’ facilities while some adopt improved software techniques.  This paper is presenting some of the results obtained using software techniques. In this research, an improved column vector memory resident (in-memory) database management was employed to manage Big Data in which a comparative study of Disk and Memory resident Big Data mining from the study was shown.

References

. Brynjolfsson, E., Hitt, L., and Kim, H. (2011). “Strength in Numbers: How Does Data-Driven Decision making Affect Firm Performance?” ICIS 2011 Proceedings. 13. https://aisel.aisnet.org/icis2011/proceedings/economicvalueIS/13.

. Dean, J. and Ghemawat, S.(2008).”MapReduce: Simplified data processing on large clusters”. Communications of the ACM, 51(1):107–113.

. Demchenko, Y., Ngo, C,.Membrey, P.(2013). “Architecture Framework and Components for the Big Data Ecosystem”. System and Network Engineering (SNE) publication.Universiteit van amsterdam

. Eijnatten, J.V., Toine, P. and Verheul, J. (2013). “Big Data for Global History, The Transformative Promise of Digital Humanities”. BMGN - Low Countries Historical Review , Volume 128-4 (2013) ,| pp. 55-77

. Famutimi, R. F, Soriyan, H. A, Ibitoye, A. O., and Famutimi, T.I. (2017). “A Case for the Adoption of an In-Memory Based Technique for Healthcare Big Data Management”. International Journal of Computer (IJC). 27(1):141-145.

. Gajakosh, S and Takalikar, M.(2013). “Multitenant Software as a Service: Application Development Approach”. International Journal of Advanced Computer Research (ISSN (print): 2249-7277 ISSN (online): 2277-7970) Volume-3 Number-3 Issue-11.

. Gartner (2011). “Pattern-Based Strategy: Getting Value from Big Data”. Gartner Group press release. July 2011. Available at http://www.gartner.com/it/page.jsp?id=1731916

. Groves, P.,Kayyali, B., Knott, D.,Kuiken, S.,(2013) “The ‘big data’revolution in healthcare”. Center for US Health System Reform Business Technology Office Publication.

. Hellerstein, J.M., Stonebraker, M. and Hamilton,J.(2007). “Architecture of a database management System”. Foundations and Trends in Databases. Vol. 1, No. 2 (2007) 141–259

. Khalilian, M.,Mustapha, N., Sulaiman, N. (2016). “Data stream clustering by divide and conquer approach based on vector model”. Journal of Big Data. (2016) 3:1

. Lankhorst, M.H.R., Ketelaars, B.W.S.M. and. Wolters R. A. M. (2005). “Low-cost and nanoscale non-volatile memory concept for future silicon chips”. Nature Materials 4, 347 - 352 (2005).doi:10.1038/nmat1350.

. Larson, P. (2013) “Evolving the Architecture of SQL Server for Modern Hardware”.IMDM 2013.

. Lin, J. and Dyer, C. (2010). “Data-Intensive Text Processing with MapReduce. Manuscript of a book in the Morgan & Claypool Synthesis”. University of Maryland, College publication Park.https://lintool.github.io/MapReduceAlgorithms/MapReduce-book-final.pdf. Accessed December 22, 2015.

.Oracle (2014). “Integrate for Insight”. Oracle Big Data strategy guide, http://www.oracle.com/us/technologies /big-data/big-data-strategy-guide-1536569.pdf accessed December 22, 2017.

Published
2018-11-02
How to Cite
R.F., F., A.O., I., R.N, I., & T.I., F. (2018). Comparative Study of Disk Resident and Column Oriented Memory Resident Technique for Healthcare Big Data Management Using Retrieval Time. International Journal of Computer (IJC), 31(1), 92-99. Retrieved from https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1267
Section
Articles