The E-Commerce Systems Modelling Based on Petri Networks

The paper simulates the process of interaction between virtual enterprises (participants) in the Internet world of e-commerce based on Petri nets. The general formal model of mutual relations using payment systems (processes) between participants of e-commerce is described. Regardless of the type of e-commerce, the primary indicator of the impact on the profit of ecommerce is the involvement of potential audiences to visit the relevant e-commerce sites and their further motivation to make the applicable orders. Appropriate e-commerce referral systems should withstand the worst of motivating the user to place an order. Timely further analysis of the statistics of visits and conversions depends on the formation of new goals to encourage potential e-commerce customers. The e-commerce system model based on the Petri net allows determining the set of financial relations between the participants of e-business. Analysis of labelled systems based on the Petri net enables you to build a tree of achievement of e-commerce goals and finding the relationships between site visits and e-business profits. Further application of the methods of correlation and cluster analysis to the obtained data allows classifying the purposes of conversions depending on the market reaction and the current time for the necessary adjustment.

[1]  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).

[2]  V. Vysotska,et al.  Analysis and Evaluation of Risks in Electronic Commerce , 2007, 2007 9th International Conference - The Experience of Designing and Applications of CAD Systems in Microelectronics.

[3]  Petro Pukach,et al.  The mathematical method development of decisions supporting concerning products placement based on analysis of market basket content , 2017, 2017 14th International Conference The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM).

[4]  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).

[5]  Yevhen Burov,et al.  Consolidated Information Web Resource for Online Tourism Based on Data Integration and Geolocation , 2019, 2019 IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT).

[6]  Changjun Jiang,et al.  Modeling and Vulnerable Points Analysis for E-commerce Transaction System with a Known Attack , 2016, SpaCCS.

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

[8]  Lyubomyr Chyrun,et al.  Method of Similar Textual Content Selection Based on Thematic Information Retrieval , 2019, 2019 IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT).

[9]  Vasyl Lytvyn,et al.  The Virtual Library System Design and Development , 2018 .

[10]  Andrii Berko,et al.  A Method to Solve Uncertainty Problem for Big Data Sources , 2018, 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP).

[11]  Yevhen Burov,et al.  Heterogeneous Data with Agreed Content Aggregation System Development , 2019, MoMLeT.

[12]  Lyubomyr Chyrun,et al.  Identifying Textual Content Based on Thematic Analysis of Similar Texts in Big Data , 2019, 2019 IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT).

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

[14]  O. D. Vytvytska,et al.  Structural-functional modeling for the determination of the company's equilibrium conditions in the dynamic business environment , 2020 .

[15]  Yevhen Burov,et al.  Web Resource Changes Monitoring System Development , 2019, MoMLeT.

[16]  W. Sroka,et al.  Evaluation of Product Competitiveness: A Case Study Analysis , 2019, Organizacija.

[17]  Lyubomyr Chyrun,et al.  Web Content Monitoring System Development , 2019, COLINS.

[18]  Marharyta Sharko,et al.  Methodological Basis of Causal Forecasting of the Economic Systems Development Management Processes Under the Uncertainty , 2020, ISDMCI.

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

[20]  S. G. Deshmukh,et al.  e-Commerce and supply chains: Modelling of dynamics through fuzzy enhanced high level petri net , 2005 .

[21]  Rostyslav Yurynets,et al.  Optimal Strategy for the Development of Insurance Business Structures in a Competitive Environment , 2020, MoMLeT+DS.

[22]  Vasyl Andrunyk,et al.  Analysis and Estimation of Popular Places in Online Tourism Based on Machine Learning Technology , 2020, MoMLeT+DS.

[23]  Svitlana Sachenko,et al.  Design of a recommendation system based on collaborative filtering and machine learning considering personal needs of the user , 2019, Eastern-European Journal of Enterprise Technologies.

[24]  Lyubomyr Chyrun,et al.  Information Model of the Tendering System for Large Projects , 2020, COLINS.

[25]  Nataliya Shakhovska,et al.  Methodical Approach to Assessing the Readiness Level of Technologies for the Transfer , 2019, CSIT.

[26]  Victoria Vysotska,et al.  Process analysis in electronic content commerce system , 2015, 2015 Xth International Scientific and Technical Conference "Computer Sciences and Information Technologies" (CSIT).

[27]  Victoria Vysotska,et al.  Model and Architecture for Virtual Library Information System , 2018, 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT).

[28]  Vasyl Andrunyk,et al.  ELECTRONIC CONTENT COMMERCE SYSTEM DEVELOPMENT , 2016 .

[29]  Rostyslav Yurynets,et al.  Econometric Analysis of the Impact of Expert Assessments on the Business Activity in the Context of Investment and Innovation Development , 2020, COLINS.

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

[31]  Nataliya Shakhovska,et al.  Consumer aspects in assessing the suitability of technologies for the transfer , 2019, 2019 IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT).

[32]  Myroslava Bublyk,et al.  Structuring the Fuzzy Knowledge Base of the IT Industry Impact Factors , 2018, 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT).

[33]  W. Sroka,et al.  The influence of age factors on the reform of the public service of Ukraine , 2019, Central European Journal of Public Policy.

[34]  Olga Artemenko,et al.  Using sentiment text analysis of user reviews in social media for e-tourism mobile recommender systems , 2020, COLINS.

[35]  Bohdan Rusyn,et al.  Choosing the Method of Finding Similar Images in the Reverse Search System , 2018, COLINS.

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

[37]  Oleh Kuzmin,et al.  Economic evaluation and government regulation of technogenic (Man-Made) damage in the national economy , 2016, 2016 XIth International Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT).

[38]  Sergey Orekhov,et al.  Using Internet News Flows as Marketing Data Component , 2020, COLINS.

[39]  A. Berko Consolidated Data Models for Electronic Business Systems , 2007, 2007 9th International Conference - The Experience of Designing and Applications of CAD Systems in Microelectronics.

[40]  Iryna Oksanych,et al.  Development of specialized services for predicting the business activity indicators based on micro–service architecture , 2017 .

[41]  Michael T. M. Emmerich,et al.  Forecasting Temperatures of a Synchronous Motor with Permanent Magnets Using Machine Learning , 2020, MoMLeT+DS.

[42]  L. Chyrun,et al.  Information technology of processing information resources in electronic content commerce systems , 2016, 2016 XIth International Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT).

[43]  Vasyl Lytvyn,et al.  Methods and Means of Web Content Personalization for Commercial Information Products Distribution , 2019, ISDMCI.

[44]  N. Shpak,et al.  Diversification Models of Sales Activity for Steady Development of an Enterprise , 2016 .

[45]  Vasyl Lytvyn,et al.  Designing architecture of electronic content commerce system , 2015, 2015 Xth International Scientific and Technical Conference "Computer Sciences and Information Technologies" (CSIT).

[46]  James L. Peterson,et al.  A Note on Colored Petri Nets , 1980, Inf. Process. Lett..

[47]  Jiacun Wang,et al.  Petri Nets for Dynamic Event-Driven System Modeling , 2007, Handbook of Dynamic System Modeling.

[48]  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).

[49]  Vitaliy A. Tayanov,et al.  Peculiarities of Application of Statistical Detection Criteria for Problems of Pattern Recognition , 2005 .

[50]  Vasyl Lytvyn,et al.  Textual Content Categorizing Technology Development Based on Ontology , 2019, MoMLeT.

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

[52]  Ihor Karpov,et al.  An Intelligent System for Commercial of Information Products Distribution Based SEO and Sitecore CMS , 2020, COLINS.

[53]  Oleh Kuzmin,et al.  Digitalization of the Marketing Activities of Enterprises: Case Study , 2020, Inf..

[54]  Yevhen Burov,et al.  An Intelligent System for Generating End-User Symptom Recommendations Based on Machine Learning Technology , 2020, COLINS.

[55]  Ihor Karpov,et al.  Information System for Recommendation List Formation of Clothes Style Image Selection According to User's Needs Based on NLP and Chatbots , 2020, COLINS.

[56]  Hrystyna Lipyanina,et al.  Decision tree based targeting model of customer interaction with business page , 2020, CMIS.

[57]  Victoria Vysotska,et al.  Intelligent System of Visual Simulation of Passenger Flows , 2020, COLINS.

[58]  M. Bublyk,et al.  The model of fuzzy expert system for establishing the pollution impact on the mortality rate in Ukraine , 2017, 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT).

[59]  Lyubomyr Chyrun,et al.  An Intelligent System of the Content Relevance at the Example of Films According to User Needs , 2019, ICTES.

[60]  James L. Peterson,et al.  Petri Nets , 1977, CSUR.

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

[62]  Lyubomyr Chyrun,et al.  Online Tourism System for Proposals Formation to User Based on Data Integration from Various Sources , 2019, 2019 IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT).

[63]  Lyubomyr Chyrun,et al.  Development of System for Managers Relationship Management with Customers , 2019, ISDMCI.

[64]  Lyubomyr Chyrun,et al.  The commercial content digest formation and distributional process , 2016, 2016 XIth International Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT).

[65]  Michael T. M. Emmerich,et al.  Web Content Support Method in Electronic Business Systems , 2018, COLINS.

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