Pub Date : 2021-06-30DOI: 10.17323/2587-814x.2021.2.7.20
V. Makarov, A. Bakhtizin, G. Beklaryan, A. Akopov
This article presents a new approach to the development of a ‘digital twin’ of a manufacturing enterprise, using a television manufacturing plant as the case study. The feature of the proposed approach is the use of hybrid methods of agent-based modeling and discrete-event simulation in order to implement a simulation model of a complex production process for assembling final products from supplied components. The most important requirement for such a system is the integration of all key chains of a digital plant: conveyor lines, warehouses with components and final products (TVs), sorting and conveyor system, assembly unit, technical control department, packing unit, etc. The proposed simulation model is implemented in the AnyLogic system, which supports the possibility of using agent-based and discrete-event modeling methods within one model. The system also supports using the built-in genetic algorithm to optimize the main parameters of the model: the most important production characteristics (for example, assembly time of a product, the number of employees involved in assembly, quality control and packaging processes). Optimization experiments were completed with the help of the developed model at various intensities of loading conveyor lines with components, various restrictions on labor resources, etc. Three scenarios of the production system behavior are investigated: the absence of the components deficit with the possibility of significantly increasing the labor resource involved, a components deficit while demand for final products is maintained, and the presence of hard restrictions on the number of employees who can be involved in the processes under conditions of components deficit.
{"title":"Digital plant: methods of discrete-event modeling and optimization of production characteristics","authors":"V. Makarov, A. Bakhtizin, G. Beklaryan, A. Akopov","doi":"10.17323/2587-814x.2021.2.7.20","DOIUrl":"https://doi.org/10.17323/2587-814x.2021.2.7.20","url":null,"abstract":"This article presents a new approach to the development of a ‘digital twin’ of a manufacturing enterprise, using a television manufacturing plant as the case study. The feature of the proposed approach is the use of hybrid methods of agent-based modeling and discrete-event simulation in order to implement a simulation model of a complex production process for assembling final products from supplied components. The most important requirement for such a system is the integration of all key chains of a digital plant: conveyor lines, warehouses with components and final products (TVs), sorting and conveyor system, assembly unit, technical control department, packing unit, etc. The proposed simulation model is implemented in the AnyLogic system, which supports the possibility of using agent-based and discrete-event modeling methods within one model. The system also supports using the built-in genetic algorithm to optimize the main parameters of the model: the most important production characteristics (for example, assembly time of a product, the number of employees involved in assembly, quality control and packaging processes). Optimization experiments were completed with the help of the developed model at various intensities of loading conveyor lines with components, various restrictions on labor resources, etc. Three scenarios of the production system behavior are investigated: the absence of the components deficit with the possibility of significantly increasing the labor resource involved, a components deficit while demand for final products is maintained, and the presence of hard restrictions on the number of employees who can be involved in the processes under conditions of components deficit.","PeriodicalId":41920,"journal":{"name":"Biznes Informatika-Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44145009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-30DOI: 10.17323/2587-814x.2021.2.60.74
F. Krasnov, Irina Smaznevich, E. Baskakova
This article considers the problem of finding text documents similar in meaning in the corpus. We investigate a problem arising when developing applied intelligent information systems that is non-detection of a part of solutions by the TF-IDF algorithm: one can lose some document pairs that are similar according to human assessment, but receive a low similarity assessment from the program. A modification of the algorithm, with the replacement of the complete vocabulary with a vocabulary of specific terms is proposed. The addition of thesauri when building a corpus vector model based on a ranking function has not been previously investigated; the use of thesauri has so far been studied only to improve topic models. The purpose of this work is to improve the quality of the solution by minimizing the loss of its significant part and not adding “false similar” pairs of documents. The improvement is provided by the use of a vocabulary of specific terms extracted from the text of the analyzed documents when calculating the TF-IDF values for corpus vector representation. The experiment was carried out on two corpora of structured normative and technical documents united by a subject: state standards related to information technology and to the field of railways. The glossary of specific terms was compiled by automatic analysis of the text of the documents under consideration, and rule-based NER methods were used. It was demonstrated that the calculation of TF-IDF based on the terminology vocabulary gives more relevant results for the problem under study, which confirmed the hypothesis put forward. The proposed method is less dependent on the shortcomings of the text layer (such as recognition errors) than the calculation of the documents’ proximity using the complete vocabulary of the corpus. We determined the factors that can affect the quality of the decision: the way of compiling a terminology vocabulary, the choice of the range of n-grams for the vocabulary, the correctness of the wording of specific terms and the validity of their inclusion in the glossary of the document. The findings can be used to solve applied problems related to the search for documents that are close in meaning, such as semantic search, taking into account the subject area, corporate search in multi-user mode, detection of hidden plagiarism, identification of contradictions in a collection of documents, determination of novelty in documents when building a knowledge base.
{"title":"The problem of loss of solutions in the task of searching similar documents: Applying terminology in the construction of a corpus vector model","authors":"F. Krasnov, Irina Smaznevich, E. Baskakova","doi":"10.17323/2587-814x.2021.2.60.74","DOIUrl":"https://doi.org/10.17323/2587-814x.2021.2.60.74","url":null,"abstract":"This article considers the problem of finding text documents similar in meaning in the corpus. We investigate a problem arising when developing applied intelligent information systems that is non-detection of a part of solutions by the TF-IDF algorithm: one can lose some document pairs that are similar according to human assessment, but receive a low similarity assessment from the program. A modification of the algorithm, with the replacement of the complete vocabulary with a vocabulary of specific terms is proposed. The addition of thesauri when building a corpus vector model based on a ranking function has not been previously investigated; the use of thesauri has so far been studied only to improve topic models. The purpose of this work is to improve the quality of the solution by minimizing the loss of its significant part and not adding “false similar” pairs of documents. The improvement is provided by the use of a vocabulary of specific terms extracted from the text of the analyzed documents when calculating the TF-IDF values for corpus vector representation. The experiment was carried out on two corpora of structured normative and technical documents united by a subject: state standards related to information technology and to the field of railways. The glossary of specific terms was compiled by automatic analysis of the text of the documents under consideration, and rule-based NER methods were used. It was demonstrated that the calculation of TF-IDF based on the terminology vocabulary gives more relevant results for the problem under study, which confirmed the hypothesis put forward. The proposed method is less dependent on the shortcomings of the text layer (such as recognition errors) than the calculation of the documents’ proximity using the complete vocabulary of the corpus. We determined the factors that can affect the quality of the decision: the way of compiling a terminology vocabulary, the choice of the range of n-grams for the vocabulary, the correctness of the wording of specific terms and the validity of their inclusion in the glossary of the document. The findings can be used to solve applied problems related to the search for documents that are close in meaning, such as semantic search, taking into account the subject area, corporate search in multi-user mode, detection of hidden plagiarism, identification of contradictions in a collection of documents, determination of novelty in documents when building a knowledge base.","PeriodicalId":41920,"journal":{"name":"Biznes Informatika-Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49640422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-31DOI: 10.17323/2587-814X.2021.1.19.29
E. Isaev, D. Pervukhin, Georgy Rytikov, E. Filyugina, Diana A. Hayrapetyan
The implementation of information systems is aimed at improving the financial performance of a company, creating a transparent reporting system and improving many other competitive factors. However, the acquisition of these benefits does not negate the complexity of making a decision BUSINESS INFORMATICS Vol. 15 No 1 – 2021
信息系统的实施旨在改善公司的财务业绩，建立一个透明的报告系统，并改善许多其他竞争因素。然而，获得这些好处并不能否定做出决策的复杂性。商业信息Vol. 15 No . 1 - 2021
{"title":"Risk-based efficiency assessment of information systems","authors":"E. Isaev, D. Pervukhin, Georgy Rytikov, E. Filyugina, Diana A. Hayrapetyan","doi":"10.17323/2587-814X.2021.1.19.29","DOIUrl":"https://doi.org/10.17323/2587-814X.2021.1.19.29","url":null,"abstract":"The implementation of information systems is aimed at improving the financial performance of a company, creating a transparent reporting system and improving many other competitive factors. However, the acquisition of these benefits does not negate the complexity of making a decision BUSINESS INFORMATICS Vol. 15 No 1 – 2021","PeriodicalId":41920,"journal":{"name":"Biznes Informatika-Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42182601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-31DOI: 10.17323/2587-814X.2021.1.78.96
H. Nozari, M. Fallah, H. Kazemipoor, S. Najafi
Supply chain is one of the main pillars of manufacturing and industrial companies whose smartness can help business to be intelligent. To this end, the use of innovative technologies to make it smart is always a concern. The smart supply chain utilizes innovative tools to enhance quality, improve performance and facilitate the decision-making process. Internet of things (IoT) is one of the key components of the IT infrastructure for the development of smart supply chains that have high potential for creating sustainability in systems. Furthermore, IoT is one of the most important sources of big data generation. Big data and strategies for data analysis as a deep and powerful solution for optimizing decisions and increasing productivity are growing rapidly. For this reason, this paper attempts to examine informative supply chain development strategies by investigating the supply chain in FMCG industries as a special case and to provide a complete analytical framework for building a sustainable smart supply chain using IoT-based big data analytics. The proposed framework is based on the IoT implementation methodology, with emphasis on the use of input big data and expert reviews. Given the nature of the FMCG industry, this can lead to better production decisions.
{"title":"Big data analysis of IoT-based supply chain management considering FMCG industries","authors":"H. Nozari, M. Fallah, H. Kazemipoor, S. Najafi","doi":"10.17323/2587-814X.2021.1.78.96","DOIUrl":"https://doi.org/10.17323/2587-814X.2021.1.78.96","url":null,"abstract":"Supply chain is one of the main pillars of manufacturing and industrial companies whose smartness can help business to be intelligent. To this end, the use of innovative technologies to make it smart is always a concern. The smart supply chain utilizes innovative tools to enhance quality, improve performance and facilitate the decision-making process. Internet of things (IoT) is one of the key components of the IT infrastructure for the development of smart supply chains that have high potential for creating sustainability in systems. Furthermore, IoT is one of the most important sources of big data generation. Big data and strategies for data analysis as a deep and powerful solution for optimizing decisions and increasing productivity are growing rapidly. For this reason, this paper attempts to examine informative supply chain development strategies by investigating the supply chain in FMCG industries as a special case and to provide a complete analytical framework for building a sustainable smart supply chain using IoT-based big data analytics. The proposed framework is based on the IoT implementation methodology, with emphasis on the use of input big data and expert reviews. Given the nature of the FMCG industry, this can lead to better production decisions.","PeriodicalId":41920,"journal":{"name":"Biznes Informatika-Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45216781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-31DOI: 10.17323/2587-814X.2021.1.7.18
E. Rumyantseva, K. Furmanov
The problem of assessing out-of-sample forecasting performance of event-history models is considered. Time-to-event data are usually incomplete because the event of interest can happen outside the period of observation or not happen at all. In this case, only the shortest possible time is observed and the data are right censored. Traditional accuracy measures like mean absolute or mean squared error cannot be applied directly to censored data, because forecasting errors also remain unobserved. Instead of mean error measures, researchers use rank correlation coefficients: concordance indices by Harrell and Uno and Somers’ Delta. These measures characterize not the distance between the actual and predicted values but the agreement between orderings of predicted and observed times-to-event. Hence, they take almost “ideal” values even in presence of substantial forecasting bias. Another drawback of using correlation measures when selecting a forecasting model is undesirable reduction of a forecast to a point estimate of predicted value. It is rarely possible to predict the timing of an event precisely, and it is reasonable to consider the forecast not as a point estimate but as an estimate of the whole distribution of the variable of interest. The article proposes computing Cox–Snell residuals for the test or validation dataset as a complement to rank correlation coefficients in model selection. Cox–Snell residuals for the correctly specified model are known to have unit exponential distribution, and that allows comparison of the observed out-of-sample performance of a forecasting model to the ideal case. The comparison can be done by plotting the estimate of integrated hazard function of residuals or by calculating the Kolmogorov distance between the observed and the ideal distribution of residuals. The proposed approach is illustrated with an example of selecting a forecasting model for the timing of mortgage termination. BUSINESS INFORMATICS Vol. 15 No 1 – 2021
{"title":"Using out-of-sample Cox–Snell residuals in time-to-event forecasting","authors":"E. Rumyantseva, K. Furmanov","doi":"10.17323/2587-814X.2021.1.7.18","DOIUrl":"https://doi.org/10.17323/2587-814X.2021.1.7.18","url":null,"abstract":"The problem of assessing out-of-sample forecasting performance of event-history models is considered. Time-to-event data are usually incomplete because the event of interest can happen outside the period of observation or not happen at all. In this case, only the shortest possible time is observed and the data are right censored. Traditional accuracy measures like mean absolute or mean squared error cannot be applied directly to censored data, because forecasting errors also remain unobserved. Instead of mean error measures, researchers use rank correlation coefficients: concordance indices by Harrell and Uno and Somers’ Delta. These measures characterize not the distance between the actual and predicted values but the agreement between orderings of predicted and observed times-to-event. Hence, they take almost “ideal” values even in presence of substantial forecasting bias. Another drawback of using correlation measures when selecting a forecasting model is undesirable reduction of a forecast to a point estimate of predicted value. It is rarely possible to predict the timing of an event precisely, and it is reasonable to consider the forecast not as a point estimate but as an estimate of the whole distribution of the variable of interest. The article proposes computing Cox–Snell residuals for the test or validation dataset as a complement to rank correlation coefficients in model selection. Cox–Snell residuals for the correctly specified model are known to have unit exponential distribution, and that allows comparison of the observed out-of-sample performance of a forecasting model to the ideal case. The comparison can be done by plotting the estimate of integrated hazard function of residuals or by calculating the Kolmogorov distance between the observed and the ideal distribution of residuals. The proposed approach is illustrated with an example of selecting a forecasting model for the timing of mortgage termination. BUSINESS INFORMATICS Vol. 15 No 1 – 2021","PeriodicalId":41920,"journal":{"name":"Biznes Informatika-Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42546533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-31DOI: 10.17323/2587-814X.2021.1.47.58
Vladimir I. Ananyin, Konstantin V. Zimin, M. Lugachev, R. Gimranov
An important feature of a digital organization is its ability to change rapidly. For an organization to remain capable of rapid change, it must be on the brink of resilience, since a resilient organization always resists change. The article examines the borderline state of the organization, which is on the verge of its stability and instability. In this state, the organization begins to lose predictability in the details of behavior, but still retains predictability in general. The authors called this borderline state the statistical sustainability of the organization. The phenomenon of statistical sustainability of an organization is very similar to the property of stability of the frequency of mass events and average values described in mathematical statistics by a similar term. To analyze the nature of the statistical sustainability of the organization, the authors used the ideas of strange attractors and modes with sharpening from the theory of complex systems. A strange attractor is an area of the organization’s behavior that, outside this area, is an area of stability for the organization, and inside it is an area of complete unpredictability. The theory of complex systems has shown that it is in the regions of strange attractors that the conditions for the variability of systems are created, and the theory of modes with aggravation shows the conditions under which this variability can lead to self-organization, that is, the spontaneous emergence of new structures. This article shows that systematic digitalization objectively leads to the formation of the statistical sustainability of the organization and creates the preconditions for maintaining the organization’s ability to make rapid changes. In traditional management, the statistical sustainability of an organization is viewed as a threat and a source of risk. Therefore, in the context of systematic digitalization, traditional approaches to management should be significantly refined.
{"title":"Statistical sustainability of a digital organization","authors":"Vladimir I. Ananyin, Konstantin V. Zimin, M. Lugachev, R. Gimranov","doi":"10.17323/2587-814X.2021.1.47.58","DOIUrl":"https://doi.org/10.17323/2587-814X.2021.1.47.58","url":null,"abstract":"An important feature of a digital organization is its ability to change rapidly. For an organization to remain capable of rapid change, it must be on the brink of resilience, since a resilient organization always resists change. The article examines the borderline state of the organization, which is on the verge of its stability and instability. In this state, the organization begins to lose predictability in the details of behavior, but still retains predictability in general. The authors called this borderline state the statistical sustainability of the organization. The phenomenon of statistical sustainability of an organization is very similar to the property of stability of the frequency of mass events and average values described in mathematical statistics by a similar term. To analyze the nature of the statistical sustainability of the organization, the authors used the ideas of strange attractors and modes with sharpening from the theory of complex systems. A strange attractor is an area of the organization’s behavior that, outside this area, is an area of stability for the organization, and inside it is an area of complete unpredictability. The theory of complex systems has shown that it is in the regions of strange attractors that the conditions for the variability of systems are created, and the theory of modes with aggravation shows the conditions under which this variability can lead to self-organization, that is, the spontaneous emergence of new structures. This article shows that systematic digitalization objectively leads to the formation of the statistical sustainability of the organization and creates the preconditions for maintaining the organization’s ability to make rapid changes. In traditional management, the statistical sustainability of an organization is viewed as a threat and a source of risk. Therefore, in the context of systematic digitalization, traditional approaches to management should be significantly refined.","PeriodicalId":41920,"journal":{"name":"Biznes Informatika-Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47601362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-31DOI: 10.17323/2587-814X.2021.1.30.46
Yury Zelenkov, Ekaterina Anisichkina
This work analyses the intellectual structure of data mining as a scientific discipline. To do this, we use topic analysis (namely, latent Dirichlet allocation, DLA) applied to the proceedings of the International Conference on Data Mining (ICDM) for 2001–2019. Using this technique, we identified the nine most significant research flows. For each topic, we analyse the dynamics of its popularity (number of publications) and influence (number of citations). The central topic, which unites all other direction, is General Learning, which includes machine learning algorithms. About 20% of the research efforts were spent on the development of this direction for the entire time under review, however, its influence has declined most recently. The analysis also showed that attention to topics such as Pattern Mining (detecting associations) and Segmentation (object separation algorithms such as clustering) is decreasing. At the same time, the popularity of research related to Recommender Systems, Network Analysis, and Human Behaviour Analysis is growing, which is most likely due to the increasing availability of data and the practical value of these topics. The research direction related to practical Applications of data mining also shows a tendency to grow. The last two topics, Text Mining and Data Streams have attracted steady interest from researchers. The results presented here shed light on the structure and trends of data mining over the past twenty years and allow us to expand our understanding of this scientific discipline. We can argue that in the last five years a new research agenda has been formed, which is characterized by a shift in interest from algorithms to practical applications that affect all aspects of human activity.
{"title":"Trends in data mining research: A two-decade review using topic analysis","authors":"Yury Zelenkov, Ekaterina Anisichkina","doi":"10.17323/2587-814X.2021.1.30.46","DOIUrl":"https://doi.org/10.17323/2587-814X.2021.1.30.46","url":null,"abstract":"This work analyses the intellectual structure of data mining as a scientific discipline. To do this, we use topic analysis (namely, latent Dirichlet allocation, DLA) applied to the proceedings of the International Conference on Data Mining (ICDM) for 2001–2019. Using this technique, we identified the nine most significant research flows. For each topic, we analyse the dynamics of its popularity (number of publications) and influence (number of citations). The central topic, which unites all other direction, is General Learning, which includes machine learning algorithms. About 20% of the research efforts were spent on the development of this direction for the entire time under review, however, its influence has declined most recently. The analysis also showed that attention to topics such as Pattern Mining (detecting associations) and Segmentation (object separation algorithms such as clustering) is decreasing. At the same time, the popularity of research related to Recommender Systems, Network Analysis, and Human Behaviour Analysis is growing, which is most likely due to the increasing availability of data and the practical value of these topics. The research direction related to practical Applications of data mining also shows a tendency to grow. The last two topics, Text Mining and Data Streams have attracted steady interest from researchers. The results presented here shed light on the structure and trends of data mining over the past twenty years and allow us to expand our understanding of this scientific discipline. We can argue that in the last five years a new research agenda has been formed, which is characterized by a shift in interest from algorithms to practical applications that affect all aspects of human activity.","PeriodicalId":41920,"journal":{"name":"Biznes Informatika-Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67929921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-31DOI: 10.17323/2587-814x.2020.4.36.46
A. Doljenko, I. Shpolianskaya, S. Glushenko
This article describes the analysis of the quality of microservice architectures, which are one of the main approaches to the creation and maintenance of modern information systems capable of quickly respond to changes in business demands. The implementation of continuous delivery of software components for dynamic business processes of information systems can be carried out by various sets of microservices, the optimal choice of which is a complex multi-alternative task. The paper presents a review of existing approaches to solving the problem, which showed that the development of models for assessing the quality of microservices of information systems requires further elaboration in terms of accounting for uncertainty in the initial data and modes of operation. The authors have proposed an approach to solving the problem of analyzing the quality of a microservice architecture which is implemented on the basis of a fuzzy production network model. The model allows for comprehensive accounting of various parameters (qualitative and quantitative). The article shows the implementation process of the fuzzy production network that was developed to analyze the functional quality of the microservice architecture for processing customer orders using fuzzy modeling software. The results of the analysis will allow managers and system architects to make an informed choice of the microservice architecture of the information system, as well as use it in their reports when arguing the need for scaling the system and increasing the availability of microservices.
{"title":"Fuzzy production network model for quality assessment of an information system based on microservices","authors":"A. Doljenko, I. Shpolianskaya, S. Glushenko","doi":"10.17323/2587-814x.2020.4.36.46","DOIUrl":"https://doi.org/10.17323/2587-814x.2020.4.36.46","url":null,"abstract":"This article describes the analysis of the quality of microservice architectures, which are one of the main approaches to the creation and maintenance of modern information systems capable of quickly respond to changes in business demands. The implementation of continuous delivery of software components for dynamic business processes of information systems can be carried out by various sets of microservices, the optimal choice of which is a complex multi-alternative task. The paper presents a review of existing approaches to solving the problem, which showed that the development of models for assessing the quality of microservices of information systems requires further elaboration in terms of accounting for uncertainty in the initial data and modes of operation. The authors have proposed an approach to solving the problem of analyzing the quality of a microservice architecture which is implemented on the basis of a fuzzy production network model. The model allows for comprehensive accounting of various parameters (qualitative and quantitative). The article shows the implementation process of the fuzzy production network that was developed to analyze the functional quality of the microservice architecture for processing customer orders using fuzzy modeling software. The results of the analysis will allow managers and system architects to make an informed choice of the microservice architecture of the information system, as well as use it in their reports when arguing the need for scaling the system and increasing the availability of microservices.","PeriodicalId":41920,"journal":{"name":"Biznes Informatika-Business Informatics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48899484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-31DOI: 10.17323/2587-814x.2020.4.47.61
D. Nazarov
Scientific research of any socio-economic and managerial process can be represented as a chain of reflections on the causes and consequences of this or that phenomenon's occurrence. At the same time, the authors can try not only to answer the question “why?” but also to study and understand the nature of cause-and-effect relationships, to find out the mechanisms of their occurrence, and also to get the answer to the question posed as accurately and reasonably as possible. Each author, using the accumulated experience, offers both qualitative and quantitative methods that allow him to obtain one or another assessment of causality. However, there are not enough articles devoted to a comprehensive review of the methods and technologies of cause-and-effect relationships in socio-economic processes. This article discusses three well-known conceptual approaches to the assessment of causation in socio-economic sciences: successionist causation, configurational causation and generative causation. The author gives his own interpretation of these approaches, builds graphic interpretations, and also offers such concepts as a linear sequence of factors, the causal field, and the causal space of factors in socio-economic processes. Within the framework of these approaches, a classification of mathematical and instrumental models for assessing the causality of relationships in socio-economic processes is given, and trends in the development of these and new models are formulated, taking into account the global transition to a digital format. All of these trends are based on the use of digital technologies in different formats and include descriptions of such formats. The article contains specific author’s examples of causality model implementation in scientific research related to economics and management.
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