Special Issue "Data Stream Mining and Processing"

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: closed (10 November 2018)

Special Issue Editors

Guest Editor
Prof. Dr.Sc. Dmytro Peleshko

IT Step University, Lviv, Ukraine
Website | E-Mail
Interests: computer vision; artificial intelligence; machine learning; video data stream processing; neural networks; deep learning; IoT; pattern recognition; big data modelling
Guest Editor
Prof. Dr.Sc. Olena Vynokurova

IT Step University, Lviv, Ukraine
Website | E-Mail
Interests: machine learning; computational intelligence; hybrid systems; wavelet neural networks; deep learning; prediction; clustering; classification; IoT; pattern recognition
Guest Editor
Associate Prof. CSc. Sergii Babichev

1. Department of Informatics, Jan Evangelista Purkyně University in Usti nad Labem, Czech Republic;
2. IT Step University, Lviv, Ukraine
Website | E-Mail
Interests: data mining of complex data; objective clustering; bioinformatics; gene expression profile processing; gene regulatory network reconstruction and simulation

Special Issue Information

Dear Colleagues,

This Special Issue of Data is dedicated mainly to selected papers from the 2018 IEEE International Conference of Data Stream Mining and Processing held in Lviv, Ukraine, 21–25 August, 2018. Expanded versions of papers presented at the conference will be invited for submission to this special issue. However, it should be noted that this Special Issue is not limited conference materials. Original papers, which correspond to hereinbelow presented topics can also be published.

Topics include:

  • Hybrid Systems of Computational Intelligence

Information processing systems which combine different approaches of Computational Intelligence, for example, artificial neural networks which are learnt by evolutionary algorithms, neuro-fuzzy systems, wavelet-neuro-fuzzy systems, neuro-neo-fuzzy systems, particle swarm algorithms, evolving systems, deep learning, etc.

  • Machine Vision and Pattern Recognition

Video Streams that are fed from video cameras in an online mode under environment uncertainty and variability conditions.

  • Dynamic Data Mining and Data Stream Mining

Data Mining problems (classification, clustering, prediction, identification, etc.) when information is fed in an online mode in the form of data streams.

  • Big Data and Data Science Using Intelligent Approaches

Systems of Computational Intelligence (artificial neural networks, fuzzy reasoning systems, evolutionary algorithms) in the tasks of Big Data processing (high-dimensional data) where data are stored in VLDB or fed in an unlimited data stream. Natural Language Processing—machine learning using to get the semantic objects from natural language; the deep learning methods for natural language understanding.

Prof. Dr.Sc. Dmytro Peleshko
Prof. Dr.Sc. Olena Vynokurova
Associate prof. CSc. Sergii Babichev
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access quarterly journal published by MDPI, indexed in the Emerging Sources Citation Index (ESCI) - Web of Science and Inspec (IET).

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Additional Information for Authors

Authors are obliged to expand their conference papers by adding 60% of the new research results, changing the title, and partly changing abstract and conclusions. Moreover, a reference to the paper from conference proceedings should be in the journal paper.

Technical Program Committee

List of the reviewers

Aizenberg I., D.Sc., Prof. (New York, USA), igor.aizenberg@manhattan.edu

Antoshchuk S., D.Sc., Prof. (Odesa, Ukraine), asgonpu@gmail.com

Bidyuk P., D.Sc., Prof. (Kyiv, Ukraine), pbidyuke@gmail.com

Bodyanskiy Ye., D.Sc., Prof., (Kharkiv, Ukraine), yevgeniy.bodyanskiy@nure.ua

Boyun V., D.Sc., Prof. (Kyiv, Ukraine), vboyun@gmail.com

Churyumov G., D.Sc., Prof., IEEE Senior Member (Kharkiv, Ukraine), g.churyumov@ieee.org

Dyvak М., D.Sc., Prof. (Ternopil, Ukraine), mdy@tanet.edu.te.ua

Gozhiy O., D.Sc., Assoc. Prof. (Mykolayv, Ukraine), alex.gozhyj@gmail.com

Hnatushenko V., D.Sc., Prof., IEEE Senior Member (Dnipro, Ukraine), vladimir.gnatushenko@eos.com

Kharchenko V., D.Sc., Prof. (Kharkiv, Ukraine), v.kharchenko@csn.khai.edu

Lytvynenko V., D.Sc., Prof. (Kherson, Ukraine), immun56@gmail.com

Lyubchik L., D.Sc., Prof., IEEE Member (Kharkiv, Ukraine), lyubchik.leonid@gmail.com

Mashkov V., D.Sc., Assoc. Prof. (Ústi nad Labem, Czech republic)

Mashtalir V., D.Sc., Prof. (Kharkiv, Ukraine), volodymyr.mashtalir@nure.ua

Petlenkov E., Ph.D., Prof. (Tallinn, Estonia), eduard.petlenkov@dcc.ttu.ee

Rekik A., Ph.D. (Sfax, Tunisia), alirekik1@gmail.com

Romanyshyn Yu., D.Sc., Prof. (Lviv, Ukraine), yuriy.romanyshyn1@gmail.com

Sachenko A., D.Sc., Prof. (Ternopil, Ukraine), sachenkoa@yahoo.com

Setlak G., D.Sc., Prof. (Rzeszów, Poland), gsetlak@prz.edu.pl

Shelevytsky I., D.Sc., Prof. (Kryvyi Rih, Ukraine), sheleviv@gmail.com

Sokolovsky Ya., D.Sc., Prof. (Lviv, Ukraine), sokolowskyy@ukr.net

Stepashko V., D.Sc., Prof. (Kyiv, Ukraine), svs-unet@ukr.net

Štěpnička M., Ph.D., Assoc. Prof. (Ostrava, Czech Republic), martin.stepnicka@osu.cz

Vassiljeva K., Ph.D., Assoc. Prof. (Tallinn, Estonia), kristina.vassiljeva@ttu.ee

Wójcik W., Dr. hab.inz. (Lublin, Poland)

Kulishova N., Ph.D., Assoc. Prof., (Kharkiv, Ukraine), nokuliaux@gmail.com

Volkova V., Ph.D., Assoc. Prof., (Kyiv, Ukraine), volkovavv@gmail.com

Yatsymirskyy М., D.Sc., Prof. (Łódź, Poland), mykhaylo.yatsymirskyy@p.lodz.pl

Alekseyev V., Ph.D., Assoc. Prof. (Lviv, Ukraine), Vladislav Alekseyev <a.vladislav.i@gmail.com>

Dumin O., Ph.D., Assoc. Prof., IEEE Ukraine Section (Kharkiv) (Kharkiv, Ukraine), dumin@karazin.ua

Panchenko T., Ph.D., Assoc. Prof., Member of the Board of Directors at ACM Ukrainian Chapter (Kyiv, Ukraine) taras.panchenko@gmail.com

Andrew Smith, Ph.D., (Dublin, Ireland) ansmith@ucd.ie

Bohdan Pavlyshenko, Ph.D., Assoc. Prof., (Lviv, Ukraine), bohdan b.pavlyshenko@gmail.com

Mike Hinchey, Ph.D., President, International Federation for Information Processing (IFIP); Professor of Software Engineering, University of Limerick; Emeritus Director, Lero-the Irish Software Research Centre; Chair, IEEE UK & Ireland section (Limerick, Ireland), mike.hinchey@lero.ie

Minho Jo, Ph.D., Chairman of IoT and Cognitive Networks Lab and Professor of Department of Computer Convergence Software at Korea University (Sejong Metro, South Korea), minhojo@webmail.korea.ac.kr   

Keywords

  • Big Data
  • Artificial Intelligence
  • Data Mining
  • Data Science
  • Deep learning
  • Machine Vision
  • Pattern Recognition
  • Computational Intelligence
  • Hybrid Systems

Published Papers (4 papers)

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Research

Open AccessArticle Analysis of Application of Cluster Descriptions in Space of Characteristic Image Features
Received: 2 October 2018 / Revised: 10 November 2018 / Accepted: 12 November 2018 / Published: 14 November 2018
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Abstract
In this paper, we propose an investigation of the properties of structural image recognition methods in the cluster space of characteristic features. Recognition, which is based on key point descriptors like SIFT (Scale-invariant Feature Transform), SURF (Speeded Up Robust Features), ORB (Oriented FAST
[...] Read more.
In this paper, we propose an investigation of the properties of structural image recognition methods in the cluster space of characteristic features. Recognition, which is based on key point descriptors like SIFT (Scale-invariant Feature Transform), SURF (Speeded Up Robust Features), ORB (Oriented FAST and Rotated BRIEF), etc., often relating to the search for corresponding descriptor values between an input image and all etalon images, which require many operations and time. Recognition of the previously quantized (clustered) sets of descriptor features is described. Clustering is performed across the complete set of etalon image descriptors and followed by screening, which allows for representation of each etalon image in vector form as a distribution of clusters. Due to such representations, the number of computation and comparison procedures, which are the core of the recognition process, might be reduced tens of times. Respectively, the preprocessing stage takes additional time for clustering. The implementation of the proposed approach was tested on the Leeds Butterfly dataset. The dependence of cluster amount on recognition performance and processing time was investigated. It was proven that recognition may be performed up to nine times faster with only a moderate decrease in quality recognition compared to searching for correspondences between all existing descriptors in etalon images and input one without quantization. Full article
(This article belongs to the Special Issue Data Stream Mining and Processing)
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Open AccessArticle An Evaluation of the Information Technology of Gene Expression Profiles Processing Stability for Different Levels of Noise Components
Received: 10 September 2018 / Revised: 29 October 2018 / Accepted: 1 November 2018 / Published: 5 November 2018
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Abstract
This paper presents the results of research concerning the evaluation of stability of information technology of gene expression profiles processing with the use of gene expression profiles, which contain different levels of noise components. The information technology is presented as a structural block-chart,
[...] Read more.
This paper presents the results of research concerning the evaluation of stability of information technology of gene expression profiles processing with the use of gene expression profiles, which contain different levels of noise components. The information technology is presented as a structural block-chart, which contains all stages of the studied data processing. The hybrid model of objective clustering based on the SOTA algorithm and the technology of gene regulatory networks reconstruction have been investigated to evaluate the stability to the level of the noise components. The results of the simulation have shown that the hybrid model of the objective clustering has high level of stability to noise components and vice versa, the technology of gene regulatory networks reconstruction is rather sensitive to the level of noise component. The obtained results indicate the importance of gene expression profiles preprocessing at the early stage of the gene regulatory network reconstruction in order to remove background noise and non-informative genes in terms of the used criteria. Full article
(This article belongs to the Special Issue Data Stream Mining and Processing)
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Open AccessArticle Development of the Non-Iterative Supervised Learning Predictor Based on the Ito Decomposition and SGTM Neural-Like Structure for Managing Medical Insurance Costs
Received: 23 September 2018 / Revised: 24 October 2018 / Accepted: 29 October 2018 / Published: 31 October 2018
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Abstract
The paper describes a new non-iterative linear supervised learning predictor. It is based on the use of Ito decomposition and the neural-like structure of the successive geometric transformations model (SGTM). Ito decomposition (Kolmogorov–Gabor polynomial) is used to extend the inputs of the SGTM
[...] Read more.
The paper describes a new non-iterative linear supervised learning predictor. It is based on the use of Ito decomposition and the neural-like structure of the successive geometric transformations model (SGTM). Ito decomposition (Kolmogorov–Gabor polynomial) is used to extend the inputs of the SGTM neural-like structure. This provides high approximation properties for solving various tasks. The search for the coefficients of this polynomial is carried out using the fast, non-iterative training algorithm of the SGTM linear neural-like structure. The developed method provides high speed and increased generalization properties. The simulation of the developed method’s work for solving the medical insurance costs prediction task showed a significant increase in accuracy compared with existing methods (common SGTM neural-like structure, multilayer perceptron, Support Vector Machine, adaptive boosting, linear regression). Given the above, the developed method can be used to process large amounts of data from a variety of industries (medicine, materials science, economics, etc.) to improve the accuracy and speed of their processing. Full article
(This article belongs to the Special Issue Data Stream Mining and Processing)
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Open AccessArticle Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market
Received: 4 October 2018 / Revised: 18 October 2018 / Accepted: 21 October 2018 / Published: 23 October 2018
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Abstract
The databases of Iran’s electricity market have been storing large sizes of data. Retail buyers and retailers will operate in Iran’s electricity market in the foreseeable future when smart grids are implemented thoroughly across Iran. As a result, there will be very much
[...] Read more.
The databases of Iran’s electricity market have been storing large sizes of data. Retail buyers and retailers will operate in Iran’s electricity market in the foreseeable future when smart grids are implemented thoroughly across Iran. As a result, there will be very much larger data of the electricity market in the future than ever before. If certain methods are devised to perform quick search in such large sizes of stored data, it will be possible to improve the forecasting accuracy of important variables in Iran’s electricity market. In this paper, available methods were employed to develop a new technique of Wavelet-Neural Networks-Particle Swarm Optimization-Simulation-Optimization (WT-NNPSO-SO) with the purpose of searching in Big Data stored in the electricity market and improving the accuracy of short-term forecasting of electricity supply and demand. The electricity market data exploration approach was based on the simulation-optimization algorithms. It was combined with the Wavelet-Neural Networks-Particle Swarm Optimization (Wavelet-NNPSO) method to improve the forecasting accuracy with the assumption Length of Training Data (LOTD) increased. In comparison with previous techniques, the runtime of the proposed technique was improved in larger sizes of data due to the use of metaheuristic algorithms. The findings were dealt with in the Results section. Full article
(This article belongs to the Special Issue Data Stream Mining and Processing)
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