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Cross-Platform Relational Data Extraction Utilizing SQL Server (X-PRESS)

by Jimmy Z. Bantog, Luisito Lolong Lacatan, Mary Ann F. Quioc
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 31
Year of Publication: 2021
Authors: Jimmy Z. Bantog, Luisito Lolong Lacatan, Mary Ann F. Quioc
10.5120/ijca2021921703

Jimmy Z. Bantog, Luisito Lolong Lacatan, Mary Ann F. Quioc . Cross-Platform Relational Data Extraction Utilizing SQL Server (X-PRESS). International Journal of Computer Applications. 183, 31 ( Oct 2021), 34-41. DOI=10.5120/ijca2021921703

@article{ 10.5120/ijca2021921703,
author = { Jimmy Z. Bantog, Luisito Lolong Lacatan, Mary Ann F. Quioc },
title = { Cross-Platform Relational Data Extraction Utilizing SQL Server (X-PRESS) },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2021 },
volume = { 183 },
number = { 31 },
month = { Oct },
year = { 2021 },
issn = { 0975-8887 },
pages = { 34-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number31/32132-2021921703/ },
doi = { 10.5120/ijca2021921703 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:18:29.114727+05:30
%A Jimmy Z. Bantog
%A Luisito Lolong Lacatan
%A Mary Ann F. Quioc
%T Cross-Platform Relational Data Extraction Utilizing SQL Server (X-PRESS)
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 31
%P 34-41
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The use of data is vital to the advantage and success of every company and businesses. However, querying, extracting and analyzing data from cross-platform relational databases in just a single run without the need for programming is very challenging. While this process can be done manually, it requires more resources, very time consuming and prone to error. This also involves the use of different tools and lots of consolidation. Data are coming from multi-platform applications, from open source or proprietary software, from different operating systems, from different relational database management systems, from different servers and from any location within the organization. Data are complex that requires it to be processed, stored and managed in several ways. There is a need for a solution that can analyze through the company’s different data sources and allowing users to perform queries. Analyzing data is best performed with the use of tools that facilitates efficient data exploration and better querying. This study finds its way to the development of a single tool that is capable of exploring, analyzing and transforming data into information regardless of its environment and configuration and makes it available to everyone. This all-in-one database querying tool provides the facility for accessing data from any cross-platform database across the network via SQL Server linked server connection through distributed and heterogeneous query execution. This tool commonly known as X-PRESS is built on top of a framework based on dynamic creation of entities and attributes and utilizes the power of Structured Query Language. Connection to data is more secure and took advantage of server processing power thus making this tool faster and more suitable for data analysis. X-PRESS is the acronym of Cross-Platform Relational Data Extraction Utilizing SQL Server. “X-P” derives from Cross-Platform while “RESS” is the initial of the keywords Relational Data, Extraction, SQL and Server, respectively.

References
  1. K. Cukier, “Big Data and the Future of Business,” 2015. https://www.technologyreview.com/s/538916/big-data-and-the future -of-business/.
  2. T. C. Redman, “Does Your Company Know What to Do with All Its Data?,” 2017. https://hbr.org/2017/06/does-your-companyknow-what-to-do-with-all-its-data.
  3. M. Theriault, “6 Ways to Transform Data into Real Information That Drives Decision-Making,” 2017. https://www.allbusiness.com/transform-data-real-informa tion drives-decision-making-16096-1.html.
  4. S. Repiso, “Case study: The process of transforming data into useful information,” 2015. https://nae.global/en/ case-study-the-process-oftrans forming-data-into-useful-information/.
  5. D. Mitzner, “What is a data-driven company?,” 2016. http://www.infoworld.com/article/3074322/big-data/ what-is-a-datadriven-company.html.
  6. R. Shaw, “What is Business Intelligence?,” 2011. https://www.dbta.com/Editorial/Trends-and-Applications /What-isBusiness-Intelligence-73502.aspx.
  7. K. M. Cresswell, D. W. Bates, and A. Sheikh, “Why Every Health Care Organization Needs a Data Science Strategy,” 2017. http://catalyst.nejm.org/healthcare-needs-data-science-strategy/.
  8. S. Leung, “Fostering an Analytics-Driven Culture,” 2014. https://www.tibco.com/blog/2014/07/29/fostering-an-analytics driven-culture/.
  9. A. McAfee and E. Brynjolfsson, “Big Data: The Management Revolution,” 2012. https://hbr.org/2012/10/ big-data-themanagement-revolution.
  10. C. Carande, P. Lipinski, and T. Gusher, “How to Integrate Data and Analytics into Every Part of Your Organization,” 2017. https://hbr.org/2017/06/how-to-integrate-data-and-analytics-intoevery-part-of-your-organization.
  11. B. Wagner, L. Latham, and M. Wenzel, “C#,” 2017. https://docs.microsoft.com/en-us/dotnet/csharp/csharp.
  12. Y. Ono, S. Ono, H. Yasunaga, H. Matsui, K. Fushimi, and Y. Tanaka, “Clinical characteristics and outcomes of myxedema coma: Analysis of a national inpatient database in Japan,” J. Epidemiol., vol. 27, no. 3, pp. 117–122, 2017, doi: 10.1016/j.je.2016.04.002.
  13. L. Myatt, J. M. Roberts, and C. W. G. Redman, “Availability of COLLECT, a database for pregnancy and placental research studies worldwide,” Placenta, vol. 57, pp. 223–224, 2017, doi: 10.1016/j.placenta. 2017.07.014.
  14. A. Kretser, D. Murphy, and P. Starke-Reed, “A partnership for public health: USDA branded food products database,” J. Food Compos. Anal., vol. 64, no. July, pp. 10–12, 2017, doi: 10.1016/j.jfca.2017.07.019.
  15. M. Willemet, S. Vennin, and J. Alastruey, “Computational assessment of hemodynamics-based diagnostic tools using a database of virtual subjects: Application to three case studies,” J. Biomech., vol. 49, no. 16, pp. 3908–3914, 2016, doi: 10.1016/j.jbiomech. 2016.11.001.
  16. P. Garg and P. Jaiswal, “Databases and bioinformatics tools for rice research,” Curr. Plant Biol., vol. 7–8, pp. 39–52, 2016, doi: 10.1016/j.cpb.2016.12.006.
  17. A. R. Caliñgo, A. M. Sison, and B. T. Tanguilig III, “Prediction Model of the Stock Market Index Using Twitter Sentiment Analysis,” Int. J. Inf. Technol. Comput. Sci., vol. 8, no. 10, pp. 11– 21, 2016, doi: 10.5815/ijitcs.2016.10.02.
  18. C. Fan and F. Xiao, “Assessment of Building Operational Performance Using Data Mining Techniques: A Case Study,” Energy Procedia, vol. 111, no. September 2016, pp. 1070–1078, 2017, doi: 10.1016/j.egypro. 2017.03.270.
  19. I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas, and I. Chouvarda, “Machine Learning and Data Mining Methods in Diabetes Research,” Comput. Struct. Biotechnol. J., vol. 15, pp. 104–116, 2017, doi: 10.1016/ j.csbj.2016.12.005.
  20. A. M. Ahmed, A. Rizaner, and A. H. Ulusoy, “Using data Mining to Predict Instructor Performance,” Procedia Comput. Sci., vol. 102, no. August, pp. 137–142, 2016, doi: 10.1016/j.procs.2016.09.380.
  21. O. K. Oyedotun, S. N. Tackie, E. O. Olaniyi, and A. Khashman, “Data Mining of Students’ Performance: Turkish Students as a Case Study,” Int. J. Intell. Syst. Appl., vol. 7, no. 9, pp. 20–27, 2015, doi: 10.5815/ ijisa.2015.09.03.
  22. B. T. Femina and E. M. Sudheep, “An efficient CRM-data mining framework for the prediction of customer behaviour,” Procedia Comput. Sci., vol. 46, no. Icict 2014, pp. 725–731, 2015, doi: 10.1016/j.procs. 2015.02.136.
  23. H. A. Aboalsamh, “A Join Algorithm for Large Databases: A Quadtrees Structure Approach,” J. King Saud Univ. - Comput. Inf. Sci., vol. 22, pp. 1–11, 2010, doi: 10.1016/s1319-1578(10)80001-1.
  24. D. Cohen and K. Narayanaswamy, “Using first-order logic to query heterogeneous internet data sources,” Procedia Comput. Sci., vol. 62, no. Scse, pp. 170–177, 2015, doi: 10.1016/ j.procs.2015.08.431.
  25. T. A. Engel, A. S. Charão, M. Kirsch-Pinheiro, and L. A. Steffenel, “Performance improvement of data mining in weka through GPU acceleration,” Procedia Comput. Sci., vol. 32, pp. 93–100, 2014, doi: 10.1016/j.procs. 2014.05.402.
  26. M. Şerban, “Methods to Increase Search Performance for Encrypted Databases,” Procedia Econ. Financ., vol. 3, no. 12, pp. 1063–1068, 2012, doi: 10.1016/s2212-5671 (12)00274-2.
  27. D. J. Prajapati, S. Garg, and N. C. Chauhan, “Interesting association rule mining with consistent and inconsistent rule detection from big sales data in distributed environment,” Futur. Comput. Informatics J., vol. 2, no. 1, pp. 19–30, 2017, doi: 10.1016/j.fcij.2017.04.003.
  28. P. P. Beran, W. Mach, E. Schikuta, and R. Vigne, “A multi-staged blackboard query optimization framework for world-spanning distributed database resources,” Procedia Comput. Sci., vol. 4, pp. 156–165, 2011, doi: 10.1016/ j.procs.2011.04.017.
  29. R. Taylor, “Query Optimization for Distributed Database Systems Robert Taylor Candidate Number : 933597 Hertford College Supervisor : Dr . Dan Olteanu Submitted as part of Master of Computer Science Computing Laboratory University of Oxford August 2010,” no. August, 2010.
  30. K. Huang, “Query Optimization in Distributed Databases.,” vol. 6, no. 2, pp. 319–322, 1983, [Online]. Available: http://oai.dtic.mil/oai/oai?verb=getRecord& metadataPrefix=html&i dentifier=ADA124921.
  31. V. Mishra and V. Singh, “Generating Optimal Query Plans for Distributed Query Processing using Teacher-Learner Based Optimization,” Procedia Comput. Sci., vol. 54, pp. 281–290, 2015, doi: 10.1016/j.procs. 2015.06.033.
  32. J. N. Mindoro, N. U. Pilueta, Y. D. Austria, L. Lolong Lacatan, and R. M. Dellosa, “Capturing Students’ Attention through Visible Behavior: A Prediction Utilizing YOLOv3 Approach,” 2020 11th IEEE Control Syst. Grad. Res. Colloquium, ICSGRC 2020 - Proc., no. August, pp. 328–333, 2020, doi: 10.1109/ ICSGRC49013.2020.9232659.
Index Terms

Computer Science
Information Sciences

Keywords

Cross-Platform Relational Data Extraction Linked Server SQL SQL Server Database Querying Tool Database Analyzer RDBMS

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