Uncertainty reduction in big data catalogue for information product quality evaluation
DOI:
https://doi.org/10.15587/1729-4061.2018.123064Keywords:
Big data, uncertainty reduction, risk factor, F-dependence, usefulness of information productAbstract
The method of vitality of the information product evaluation is built. This method, as opposed to others, takes into account components of the information product, which gave an opportunity to forecast (predict) the sequence of changes in its states. The operations over the relation with indeterminacy for the purpose of their application in the data warehouse with the consolidated data are improved that allowed realizing unary operations of Big data catalogue.
The method for reducing the indeterminacy of data available in the repository of consolidated data as a basis for further evaluation of the quality of consolidated data was created. The considered methods are useful also for decision making, because they provide a search for hidden relationships between the characteristics of the consolidated data repository. Such dependence should be considered when making decisions based on consolidated data.
The result of this work is to reduce the uncertainty for assessing the viability of the information product. This allows us to increase the quality of the information product for Big data analysis.
References
- Shakhovska, N. B., Bolubash, Y. J., Veres, O. M. (2015). Big data federated repository model. The Experience of Designing and Application of CAD Systems in Microelectronics. doi: 10.1109/cadsm.2015.7230882
- Zadeh, L. (1976). The concept of a linguistic variable and its application to the adoption of approximate solutions. New York, 166.
- Tselmer, G. (1980). Risk consideration in management decisions. Problems of ICSTI, 3, 94–105
- Knight, F. K. (2003). Risk, uncertainty and profit. Moscow: Business, 358.
- Moiseyev, N. N. (1975). Elements of the theory of optimum systems. Moscow: Science, 528.
- Trukhachev, R. I. (1981). Decision-making models in the conditions of uncertainty. Moscow: Science, 151.
- Shakhovska, N., Medykovsky, M., Stakhiv, P. (2013). Application of algorithms of classification for uncertainty reduction. Przeglad Elektrotechniczny, 4 (89), 284–286.
- Wang, R., He, Y.-L., Chow, C.-Y., Ou, F.-F., Zhang, J. (2015). Learning ELM-Tree from big data based on uncertainty reduction. Fuzzy Sets and Systems, 258, 79–100. doi: 10.1016/j.fss.2014.04.028
- Triguero, I., Peralta, D., Bacardit, J., García, S., Herrera, F. (2015). MRPR: A MapReduce solution for prototype reduction in big data classification. Neurocomputing, 150, 331–345. doi: 10.1016/j.neucom.2014.04.078
- Karami, A. (2015). A Framework for Uncertainty-Aware Visual Analytics in Big Data. In AIC, 146–155.
- Wang, X., He, Y. (2016). Learning from Uncertainty for Big Data: Future Analytical Challenges and Strategies. IEEE Systems, Man, and Cybernetics Magazine, 2 (2), 26–31. doi: 10.1109/msmc.2016.2557479
- Bendler, J., Wagner, S., Brandt, T., Neumann, D. (2014). Taming Uncertainty in Big Data. Business & Information Systems Engineering, 6 (5), 279–288. doi: 10.1007/s12599-014-0342-4
- Veres, O., Shakhovska, N. (2015). Elements of the formal model big date. In Perspective Technologies and Methods in MEMS Design (MEMSTECH). 2015 XI International Conference, 81–83.
- Vovk, O. B., Shakhovska, N. B. (2015). Formation of the factors influencing the behavior of the information product. Radioelectronics, informatic, management, 2, 43–53.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2018 Natalya Shakhovska, Olena Vovk, Yurii Kryvenchuk
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.
