A Safety Data Model for data analysis and decision making

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Ilyasu Anda 1,* Isah Omeiza Rabiu 2 Enesi Femi Aminu 3

1. Department of Library and Information Technology, School of Information & Communication Technology Federal University of Technology, Minna, Niger State Nigeria

2. Department of Computer Engineering, Federal University of Technology, Minna, Niger State Nigeria

3. Department of Computer Science School of Information & Communication Technology Federal University of Technology, Minna, Niger State Nigeria

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2017.04.04

Received: 15 Mar. 2017 / Revised: 26 Apr. 2017 / Accepted: 13 Jun. 2017 / Published: 8 Jul. 2017

Index Terms

Safety, Big-Data, Data Warehouse, Microsoft SQL Server, Metadata.


The systems related to safety are becoming more and more important and are dependent on complex data both in terms of volume and variety. This is especially of importance in applications demanding data analysis, intensive maintenance and focuses on the potential threats due to possible data errors, such as railway signaling, traffic management etc. Errors in analysis of data could result in loss of many lives and financial loss such as the cases of Annabella container ship- Baltic Sea accident (United Kingdom Merchant Shipping, Regulations 2005 – Regulation 5). Despite these potential errors in data leading to accidents or mishaps, this part of the system has been ignored; this study focuses on the integrity of data in safety critical applications. It did so by developing a method for building metadata through a data chain, mining this metadata and representing it in such a way that a consumer of the data can judge the integrity of the data and factor this into the decision-making aspect of their response. This research proposes a design, implementation and evaluation of a safety data model that helps to ensure integrity of data use for data analysis and decision making to prevent loss of lives and properties. Modern and sophisticated ETL software tools including Microsoft SQL Server 2012 Data Tools and Microsoft SQL Server Management Studio were explored. The data were extracted from Safety Related Condition Reports (SRCRs) dataset and used data mining techniques to transform and filter unsafe and hazardous data from the extracted data and stored the safe data into the Data Warehouses (DWs). The prototype was able to load data into designated DWs. The success of the developed model proved that the prototype was able to extract all datasets, transform and load data into the DWs and moved extracted files to archive folder within 7.406 seconds.

Cite This Paper

Ilyasu Anda, Isah Omeiza Rabiu, Enesi Femi Aminu, "A Safety Data Model for data analysis and decision making", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.9, No.4, pp.21-30, 2017. DOI:10.5815/ijieeb.2017.04.04


[1]Kumar, S and Patanka, S. (2012), Easy, Real-Time Big Data Analysis Using Storm, last retrieved from http://www.drdobbsom/open-source/easy-real-time-big-data-analysis-using-s/240143874 on 18th September 2013.
[2]Agile Alliance, Agile principles, 2001. Available: http://www.agilealliance.org/the-alliance/the-agile-manifesto /the-twelve-principles-of-agile-software/ 2001: [Accessed: 7 July 2013].
[3]Hadoop Analytics (2014), Hadoop Analytics, last retrieved from http://www.alteryx.com/hadoop-analytics on 19th September 2013
[4]N, Kushmerick. Wrapper induction: efficiency and expressiveness. Artificial Intelligence, 118:15-68, 2000.
[5]A, Arasu. and Garcia-Molina, H. Extracting Structured Data from Web Pages. SIGMOD-03, 2003.
[6]Chang, C. and Lui, S-L. IEPAD: Information extraction based on pattern discovery. WWW-10, 2001.
[7]Buttler, D., Liu, L., Pu, C. A fully automated extraction system for the World Wide Web. IEEE ICDCS-21, 2001.
[8]Liu, Bing, and Yanhong Zhai. "NET–a system for extracting web data from flat and nested data records." In International Conference on Web Information Systems Engineering, Springer Berlin Heidelberg, pp.487-495. 2005.
[9]O. Folorunshor, B.A Adesesan, “Application of Data Mining Techniques in Weather Prediction and climate Change Studies,” I.J, Information Engineering and Electronic Business, {2012}, vol.1 pp.51-59
[10]T. Ishaya and M. Folarin, “A Service Oriented Approach to Business Intelligence in Telecoms Industry” Telematics and Informatics, 29 (2012), pp. 273-285, Jan-2012. [Online]. Available:http://www.sciencedirect.com/science/article/pii/S0736585312000056. [Accessed: 16 November, 2013] K. Elissa, “Title of paper if known,” unpublished.