Possibilities of Data Analysis Using Data Model
- 46 Downloads
The basic feature of the presented model, especially thanks to the intensive implementation of Industry 4.0 philosophy, is the large amount of data. The rapid development of measurement and communication technologies, linked to information technology, enables data to be gathered in different areas and from different sources. As a result, the amount of data increases. Managing such data is an administrative process that involves retrieving, verifying, storing, protecting and processing the required data to ensure the accessibility, reliability and timeliness of data for its users (Galeto. What is data management. https://www.ngdata.com/what-is-data-management/. Last accessed 2019/04/27). One method to process this data is also data analysis, which is defined as a computer system application to analyse large data files to support decision-making. Using this method, the paper will describe how to manage the measured data via data model. Analysed data was obtained by monitoring the heating system applied at different locations, using the Supervisory Control and Data Acquisition (SCADA) industrial control system and the web user interface. As a software tool for data processing, MS Excel spreadsheet tools were used to demonstrate their benefits in processing such data.
KeywordsData managing Heating process PowerPivot Model Power consumption
This work was supported by the Slovak Research and Development Agency under Contract No. APVV-15-0602.
- 1.Galeto, M. What is data management. https://www.ngdata.com/what-is-data-management/. Last accessed 2019/04/27.
- 2.The importance of data management in companies. https://www.ringlead.com/blog/the-importance-of-data-management-in-companies/. Last accessed 2019/04/27.
- 3.Data management. https://www.sas.com/content/dam/SAS/en_us/doc/other1/data-manage ment-why-is-it-important-107421.pdf. Last accessed 2019/05/05.
- 4.Sharma, R. Data analytics and business intelligence: Understanding the differences. http://techgenix.com/data-analytics-business-intelligence/. Last accessed 2019/05/05.
- 5.Piteľ, J., Mižáková, J., & Mižák, J. (2018). Monitoring and controlling of heat production and supply in the context of the Fourth Industrial Revolution. In Heating. SSTP, Slovakia.Google Scholar
- 6.Patil, M. V., & Yogi, A. M. N. (2011). Importance of data collection and validation for systematic software development process. International Journal of Computer Science and Information Technology, 3(2). http://airccse.org/journal/jcsit/0411csit20.pdf.
- 7.Trochim, W.M.K. Data preparation. Research methods knowledge base (2nd ed.). http://www.uniteforsight.org/research-methodology/module5#_ftn1. Last accessed 2019/05/05.
- 8.Russo, A., & Ferrari, M. (2013). Microsoft Excel 2013: Building data models with PowerPivot, Published by Microsoft Press. http://index-of.co.uk/OFIMATICA/Microsoft%20Press%20Excel%202013,%20Building%20Data%20Models%20with%20Power Pivot.pdf. Last accessed 2019/05/05.
- 9.Singh, A., & Collie, R. Power Pivot and Power BI. Chicago: Independent Publishers Group. http://www.allitebooks.in/power-pivot-and-power-bi/. Last accessed 2019/05/05.
- 10.The importance of managing data assets. https://searchdatamanagement.techtarget.com/feature/The-importance-of-managing-data-assets. Last accessed 2019/04/27.