Statistical Analysis of the Popularity of Programming Language Libraries Based on StackOverflow Queries

This paper presents a statistical analysis of existing trends in the spread of programming language libraries based on data set studies. The various problems that arise when using specific libraries of different programming languages for certain periods, the most common is the month, are studied and analyzed. The results of the study of existing trends in the spread of programming language libraries, collected in the studied dataset, are presented graphically, set key descriptive characteristics, taking into account the correlation of data. Trends in the behavior of the studied indicators using the methods of smoothing time series are determined. A cluster analysis of programming language libraries was performed, making it possible to group data by clusters and form appropriate data groups for ranking programming language libraries.

[1]  V. Vysotska,et al.  The Ukrainian Economy Transformation into the Circular Based on Fuzzy-Logic Cluster Analysis , 2021, Energies.

[2]  O. Rudenko,et al.  Robust Training of ADALINA Based on the Criterion of the Maximum Correntropy in the Presence of Outliers and Correlated Noise , 2021, COLINS.

[3]  Nataliia Romanyshyn,et al.  Algorithm for Disclosing Artistic Concepts in the Correlation of Explicitness and Implicitness of Their Textual Manifestation , 2021, COLINS.

[4]  Nataliya Boyko,et al.  Comparative Analysis of Regression Regularization Methods for Life Expectancy Prediction , 2021, MoMLeT+DS.

[5]  Victoria Vysotska,et al.  Real-Time Ukrainian Text Recognition and Voicing , 2021, COLINS.

[6]  Alina Shafronenko,et al.  Adaptive Recovery of Distorted Data Based on Credibilistic Fuzzy Clustering Approach , 2021, COLINS.

[7]  N. Boyko,et al.  Comparison of Clustering Algorithms for Revenue and Cost Analysis , 2021, COLINS.

[8]  L. Mochurad.  Optimization of Regression Analysis by Conducting Parallel Calculations , 2021, COLINS.

[9]  Victoria Vysotska,et al.  A Comparative Analysis for English and Ukrainian Texts Processing Based on Semantics and Syntax Approach , 2021, COLINS.

[10]  Yelyzaveta V. Meleshko,et al.  A Method of Detecting Bot Networks Based on Graph Clustering in the Recommendation System of Social Network , 2021, COLINS.

[11]  Myroslava Bublyk,et al.  Small-Batteries Utilization Analysis Based on Mathematical Statistics Methods in Challenges of Circular Economy , 2021, COLINS.

[12]  Yevhen Burov,et al.  Stochastic Game Model of Data Clustering , 2021, IntelITSIS.

[13]  L. Chyrun,et al.  Methods and Tools for Web Resources Processing in E-Commercial Content Systems , 2020, 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT).

[14]  Vitaliy Yakovyna,et al.  An Improved Software Defect Prediction Algorithm Using Self-organizing Maps Combined with Hierarchical Clustering and Data Preprocessing , 2020, DEXA.

[15]  Victoria Vysotska,et al.  Forecasting Nonlinear Nonstationary Processes in Machine Learning Task , 2020, 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP).

[16]  Lyubomyr Chyrun,et al.  Application of Ontologies And Meta-Models for Dynamic Integration of Weakly Structured Data , 2020, 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP).

[17]  Hao Wang,et al.  Cluster-based Kriging approximation algorithms for complexity reduction , 2017, Applied Intelligence.

[18]  Victoria Vysotska,et al.  Methods for Forecasting Nonlinear Non-stationary Processes in Machine Learning , 2020, DSMP.

[19]  Yakiv Yusyn,et al.  Methods of Acceleration of Term Correlation Matrix Calculation in the Island Text Clustering Method , 2020, COLINS.

[20]  V. Vysotska,et al.  Implementation Models Application for IT Project Risk Management , 2020, CITRisk.

[21]  V. Vysotska,et al.  Assessing Losses of Human Capital Due to Man-Made Pollution Caused by Emergencies , 2020, CITRisk.

[22]  Svitlana Sachenko,et al.  Qualitative and Quantitative Characteristics Analysis for Information Security Risk Assessment in E-Commerce Systems , 2020, ICTES.

[23]  Natalya Shakhovska,et al.  Retrospective Analysis by Multifactor Regression in the Evaluation of the Results of Fine-needle Aspiration Biopsy of Thyroid Nodules , 2020, IDDM.

[24]  Ihor Karpov,et al.  Optimization Model of the Buses Number on the Route Based on Queueing Theory in a Smart City , 2020, MoMLeT+DS.

[25]  Volodymyr Lytvynenko,et al.  The Use of Inductive Methods to Identify Subtypes of Glioblastomas in Gene Clustering , 2020, MoMLeT+DS.

[26]  Yevhen Burov,et al.  Propaganda Detection in Text Data Based on NLP and Machine Learning , 2020, MoMLeT+DS.

[27]  Ihor Karpov,et al.  Analysis of the Demand for Bicycle Use in a Smart City Based on Machine Learning , 2020, MoMLeT+DS.

[28]  Yevhen Burov,et al.  DDOS Attacks Analysis Based on Machine Learning in Challenges of Global Changes , 2020, MoMLeT+DS.

[29]  Olena Vovk,et al.  Forecasting the Risk of Cervical Cancer in Women in the Human Capital Development Context Using Machine Learning , 2020, MoMLeT+DS.

[30]  Victoria Vysotska,et al.  Research of Pareto-Optimal Schemes of Control of Availability of the Information System for Critical Use , 2020, IntelITSIS.

[31]  Myroslava Bublyk,et al.  Intelligent System of Passenger Transportation by Autopiloted Electric Buses in Smart City , 2020, COLINS.

[32]  Victoria Vysotska,et al.  Ukrainian Participles Formation by the Generative Grammars Use , 2020, COLINS.

[33]  M. Bublyk,et al.  Innovative development of human capital in the conditions of globalization , 2020, E3S Web of Conferences.

[34]  Victoria Vysotska,et al.  Web Service Interaction Modeling with Colored Petri Nets , 2019, 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS).

[35]  Mariia Voronenko,et al.  Application of a Combined Approach for Predicting a Peptide-Protein Binding Affinity Using Regulatory Regression Methods with Advance Reduction of Features , 2019, 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS).

[36]  Yevhen Burov,et al.  Gaming Method of Ontology Clusterization , 2019, Webology.

[37]  Bohdan Durnyak,et al.  An Evaluation of the Objective Clustering Inductive Technology Effectiveness Implemented Using Density-Based and Agglomerative Hierarchical Clustering Algorithms , 2019, ISDMCI.

[38]  B. P. Rusyn,et al.  Intensity Estimation of Noise-Like Signal in Presence of Uncorrelated Pulse Interferences , 2019, Radioelectronics and Communications Systems.

[39]  Sergii Mashtalir,et al.  MULTIDIMENSIONAL SEQUENCE CLUSTERING WITH ADAPTIVE ITERATIVE DYNAMIC TIME WARPING , 2019, International Journal of Computing.

[40]  Vasyl Lytvyn,et al.  Technology for the Psychological Portraits Formation of Social Networks Users for the IT Specialists Recruitment Based on Big Five, NLP and Big Data Analysis , 2019, COAPSN.

[41]  Volodymyr Lytvynenko,et al.  Two Step Density-Based Object-Inductive Clustering Algorithm , 2019, MoMLeT.

[42]  Yevhen Burov,et al.  Ontological Approach to Plot Analysis and Modeling , 2019, COLINS.

[43]  Yaroslav Kis,et al.  Risk Assessment Technology of Crediting with the Use of Logistic Regression Model , 2019, COLINS.

[44]  P. S. Malachivskyy,et al.  Chebyshev Approximation by the Sum of the Polynomial and Logarithmic Expression with Hermite Interpolation , 2018, Cybernetics and Systems Analysis.

[45]  Victoria Vysotska,et al.  Authorship Identification of the Scientific Text in Ukrainian with Using the Lingvometry Methods , 2018, 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT).

[46]  Bohdan Rusyn,et al.  Robust Approach to Estimation of the Intensity of Noisy Signal with Additive Uncorrelated Impulse Interference , 2018, 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP).

[47]  S. Babichev,et al.  Comparison Analysis of Biclustering Algorithms with the use of Artificial Data and Gene Expression Profiles , 2018, 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO).

[48]  Volodymyr Lytvynenko,et al.  Objective clustering inductive technology of gene expression profiles based on sota clustering algorithm , 2017 .

[49]  Volodymyr Lytvynenko,et al.  Model of the Objective Clustering Inductive Technology of Gene Expression Profiles Based on SOTA and DBSCAN Clustering Algorithms , 2017 .

[50]  S. Babichev,et al.  Gene expression sequences clustering based on the internal and external clustering quality criteria , 2017, 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT).

[51]  S. Babichev,et al.  Implementation of the objective clustering inductive technology based on DBSCAN clustering algorithm , 2017, 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT).

[52]  Lytvyn Vasyl,et al.  Application of sentence parsing for determining keywords in Ukrainian texts , 2017, 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT).

[53]  Hao Wang,et al.  Time complexity reduction in efficient global optimization using cluster kriging , 2017, GECCO.

[54]  S. Sawicki,et al.  Price modeling of laaS providers using multiple regression , 2017, 2017 12th Iberian Conference on Information Systems and Technologies (CISTI).

[55]  S. Babichev,et al.  Criterial analysis of gene expression sequences to create the objective clustering inductive technology , 2017, 2017 IEEE 37th International Conference on Electronics and Nanotechnology (ELNANO).

[56]  Cássio L. M. Belusso,et al.  Modelagem de preços de provedores de IaaS utilizando regressão múltipla , 2017, CISTI 2017.

[57]  Lyubomyr Chyrun,et al.  Intelligent Systems Design of Distance Learning Realization for Modern Youth Promotion and Involvement in Independent Scientific Researches , 2017, CSIT.

[58]  Lyubomyr Chyrun,et al.  Features of e-learning realization using virtual research laboratory , 2016, 2016 XIth International Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT).

[59]  Victoria Vysotska,et al.  Linguistic analysis of textual commercial content for information resources processing , 2016, 2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET).

[60]  Hao Wang,et al.  Optimally Weighted Cluster Kriging for Big Data Regression , 2015, IDA.

[61]  B. P. Rusyn,et al.  Image Segmentation Based on the Evaluation of the Tendency of Image Elements to form Clusters with the Help of Point Field Characteristics , 2015 .

[62]  Roman Melnyk,et al.  4-D pattern structure features by three stages clustering algorithm for image analysis and classification , 2013, Pattern Analysis and Applications.

[63]  R. Melnyk,et al.  Circuit board image analysis by clustering , 2008, 2008 International Conference on Perspective Technologies and Methods in MEMS Design.

[64]  B. Rusyn,et al.  A correlation method for fingerprint image recognition using spectral features , 2002, Modern Problems of Radio Engineering, Telecommunications and Computer Science (IEEE Cat. No.02EX542).

[65]  B. Hehir,et al.  Standard deviation , 1994, Nature.