Analysis of how the properties of structured data can influence the way these data are processed

User Rating:  / 0
PoorBest 

Authors:

O.Syrotkina, Cand. Sc. (Tech.), orcid.org/0000-0002-4069-6984, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

M.Alekseyev, Dr. Sc. (Tech.), Prof., orcid.org/0000-0001-8726-7469, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

V.Asotskyi, Cand. Sc. (Psychol.), orcid.org/0000-0001-5403-3156, National University of Civil Defence of Ukraine, Kharkiv, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

I.Udovyk, Cand. Sc. (Tech.), Assoc. Prof., orcid.org/0000-0002-5190-841X, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Abstract:

Purpose. The purpose of the article is to develop mathematical methods for processing “big data”. This is based on the system analysis of properties for their structural organization. These methods allow us to optimize the basic characteristics of “big data”. This includes increasing the search speed to process large volumes of fast incoming data while preserving their relevance.

Methodology. We suggested mathematical methods to work with a data structure “m-tuples based on ordered sets of arbitrary cardinality (OSAC)”. We determined pairwise combinations of Boolean elements as operands of the operations investigated. The foregoing is based on the analysis of the data structure properties. We also calculated the dynamics of changes in the constituent pairwise combinations of the Boolean elements depending on the basis set cardinality for different groups of the given data structure.

Findings. We estimated the time needed to execute methods of working with the OSAC data structure as functional dependencies of the amount of data O( f (n)). We also determined the component of combinations for Boolean elements. For these elements, the execution of algorithms that implement the operation investigated is not required as the desired result is defined in the data structure property.

Originality. We further developed a mathematical method which allows us to forecast the result of performing certain operations on elements of ordered data structure. This takes into account the position of the elements in the structure without using the computational algorithm. For the first time, we obtained an analytical dependency to determine the component number for Boolean elements of length m2. This includes an element represented by a tuple of smaller length m1 in relation to the total number of Boolean elements of length m2. For the first time we also obtained an analytical dependency to determine the minimum maxima of the functional dependency described above.

Practical value. The results obtained in this paper can be used to minimize the time and computational resources needed to process “big data” represented by m-tuples based on OSAC.

References.

1. Hunzinger, R., (2016). SCADA Fundamentals and Applications in the IoT. Internet of Things and Data Analytics Handbook, 283-293. DOI: 10.1002/9781119173601.ch17.

2. Charbonnier, S., Bouchair, N., & Gayet, P. (2014). Analysis of Fault Diagnosability from SCADA Alarms Signatures Using Relevance Indices. IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 2739-2744). DOI: 10.1109/SMC.2014.6974342.

3. Min Chen, Shiwen Mao, Yin Zhang, & Victor C. M. Leung (2014). Big Data. Related Technologies, Challenges, and Future Prospects. Spinger. DOI: 10.1007/s40558-015-0027-y.

4. Shi-Nash, A., & Hardoon, D. R. (2016). Data Analytics and Predictive Analytics in the Era of Big Data. Internet of Things and Data Analytics Handbook, 329-345. DOI: 10.1002/9781119173601.ch19.

5. Volkova, V. N., Kozlov, V. N., Mager, V. E., & Cher­nen­kaya, L. V. (2017). Classification of Methods and Models in System Analysis.XX IEEE International Conference on Soft Computing and Measurements (SCM). (pp. 183-186). DOI: 10.1109/scm.2017.7970533.

6. Massanet, S., Riera, J. V., Torrens, J., & Herrera-Viedma, E. (2015). A Consensus Model for Group Decision-Making Problems with Subjective Linguistic Preference Relations.IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp.1-8). DOI: 10.1109/fuzz-ieee.2015.7337886. 

7. Chang, L. L., Zhou, Z. J., Chen, Y. W., Liao, T. J., Hu, Y., & Yang, L. H. (2018). Belief Rule Base Structure and Parameter Joint Optimization under Disjunctive Assumption for Nonlinear Complex System Modeling. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(9), 1542-1554. DOI: 10.1109/tsmc.2017.2678607. 

8. Stefanovych, T., Shcherbovskykh, S., & Droździel, P. (2015). The reliability model for failure cause analysis of pressure vessel protective fittings with taking into account load-sharing effect between valves. Diagnostyka. 16(4), 17-24.

9. Shcherbovskykh, S., Spodyniuk, N., Zhelykh, V., Stefanovych, T., & Shepitchak, V. (2016). Development of a reliability model to analyse the causes of a poultry module failure. Easter-European Journal of Enterprise Technologies, 4(3(82)), 4-9. DOI: 10.15587/1729-4061.2016.73354.

10. Tkachov, V., Bublikov, A., & Isakova, M. (2013). Control automation of shearers in terms of auger gumming criterion. Energy Efficiency Improvement of Geotechnical Systems. Proceedings of the International Forum on Energy Efficiency, 137-145. DOI: 10.1201/b16355-19.

11. Syrotkina, O., (2015). The Application of Specialized Data Structures for SCADA Diagnostics. System technologies. Regional interuniversity collection of scientific papers, 4, 72-81.

12. Syrotkina, O., Alekseyev, M., & Aleksieiev, O. (2017). Evaluation to Determine the Efficiency for the Diagnosis Search Formation Method of Failures in Automated Systems. Eastern-European Journal of Enterprise Technologies, 4(9(88)), 59-68. DOI: 10.15587/1729-4061.2017.108454.

 повний текст / full article



Visitors

6227351
Today
This Month
All days
1183
54028
6227351

Guest Book

If you have questions, comments or suggestions, you can write them in our "Guest Book"

Registration data

ISSN (print) 2071-2227,
ISSN (online) 2223-2362.
Journal was registered by Ministry of Justice of Ukraine.
Registration number КВ No.17742-6592PR dated April 27, 2011.

Contacts

D.Yavornytskyi ave.,19, pavilion 3, room 24-а, Dnipro, 49005
Tel.: +38 (056) 746 32 79.
e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
You are here: Home Home EngCat Archive 2019 Contens №3 2019 Information Technologies Analysis of how the properties of structured data can influence the way these data are processed