ISSN 1991-2927
 

ACP № 3 (65) 2021

Author: "Aleksei Andreevich Sapunkov"

Aleksei Andreevich Sapunkov, graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; Assistant Professor at the Department of Information Systems of UlSTU; an author of articles in the field of analyzing and forecasting the time series. e-mail: sapalks@gmail.comA.A. Sapunkov

Tatiana Vasilevna Afanaseva, Doctor of Sciences in Engineering, Associate Professor; graduated from the Radioengineering Faculty of Ulyanovsk Polytechnic Institute, Professor of the Department of Information Systems of UlSTU; an author of articles in the field of analyzing and forecasting the time series. e-mail: tv.afanasjeva@ gmail.comT.V. Afanaseva

A decision support methodology for prioritizing user requests for software modifications59_7.pdf

In this paper, approaches are analyzed and a decision support methodology for prioritizing user requests for software modifications received through a technical support service is described. This task is relevant for iteratively developing software, since at each iteration a stream of requests from end users for software modification is received. The aim of the proposed methodology is to automate the process of evaluating and prioritizing (ranking) requests to convert them into requirements. A distinctive feature of the methodology is the inclusion in the query assessment of information about the sources of queries, as well as point and temporal estimates. To analyze the changes in the number of requests of each type, it is proposed to use their forecasting based on fuzzy time series models. The proposed methodology will reduce the time costs for managers and software developers to analyze problems and make decisions on how to fix them. The article provides a formal description of the stages of the proposed methodology and considers an example of its application as a means of supporting decision-making on the inclusion of high-priority requests in the list of requirements for software development. In conclusion, conclusions are drawn on the limits of applicability of the proposed methodology.

Intellectual decision support, prioritization of requirements, System analysis, developing software products, forecasting, fuzzy forecasting models, time series.

2020_ 1

Sections: Information systems

Subjects: Information systems.



Tatiana Vasilevna Afanaseva, Ulyanovsk State Technical University, Doctor of Engineering; Associate Professor, Deputy Head of Information Systems Department at Ulyanovsk State Technical University; graduated from the Faculty of Radioengineering of Ulyanovsk State Technical University; an author of articles and monographs in the field of the intellectual analysis of time series. [e-mail: tv.afanasjeva@gmail.com]T. Afanaseva,

Aleksei Andreevich Sapunkov, Ulyanovsk State Technical University, Postgraduate Student at the Information Systems Department of Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of articles in the field of the intellectual analysis of time series. [e-mail: sapalks@gmail.com]A. Sapunkov,

Denis Valerevich Zavarzin, Ulyanovsk State Technical University, Postgraduate Student at the Information Systems Department of Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of articles in the field of the intellectual analysis of time series. [e-mail: dzavarzin91@gmail.com]D. Zavarzin

Using the K-means Clustering Algorithm for Improving the Temporal Statitics of Commercial Proposals Views 000_6.pdf

Anomalies are considered as not typical and rare values, which decrease accuracy in date significant. Such values would generally cause inaccuracy in date analysis results, so they must be deleted. The article proposes to use the k-means clustering method in order to solve practical problems of data processing for displaying the temporal statistics in the B2B sector. The B2BFamily service for sending and tracking commercial offer represents the subject area and the data source. The article also proposes to remove anomalies and display more adequate temporal view statistics about the average time of the commercial offer slide review. That will help the sales manager to adjust the strategy of communication with customers. Finally, the authors discuss the results and trends of the study further development.

B2bfamily, clustering, anomaly, b2bfamily, k-means clustering algorithm, detection and removal of anomalies.

2016_ 4

Sections: Mathematical modeling

Subjects: Mathematical modeling.


Tatiana Vasilievna Afanaseva, Ulyanovsk State Technical University, Doctor of Engineering; Associate Professor, Deputy Head of Information System Department at Ulyanovsk State Technical University; graduated from the Faculty of Radioengineering at Ulyanovsk State Technical University; an author of articles and monographs in the field of the intellectual analysis of time series. [e-mail: tv.afanasjeva@gmail.com]T. Afanaseva,

Aleksei Andreevich Sapunkov, Ulyanovsk State Technical University, Post-graduate Student of the Information System Department at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies at Ulyanovsk State Technical University; an author of articles in the field of the intellectual analysis of time series. [e-mail: sapalks@gmail.com]A. Sapunkov,

Mkrtych Sarkisovich Tonerian, Ulyanovsk State Technical University, Post-graduate Student of the Information System Department at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies at Ulyanovsk State Technical University; an author of articles in the field of the intellectual analysis of time series. [e-mail: mkr73@yandex.ru]M. Tonerian

The Two-stage Algorithm of Choosing the Fuzzy Model for Time Series Firecasting 000_8.pdf

The article deals with a two-stage algorithm for the best time-series forecasting model based on the assessment of the model adequacy with the use of behavior and accuracy. For testing, the authors use time series that have been exploited at the International Time Series Forecasting Competition IFAS-EUSLAT in 2015 ([http://irafm.osu.cz/cif/main.php]). Database of this Competition includes 91 numerical time series of different length, tendency, and data reading frequency. Time series values depicted the dynamic of parameters reported from banking area, social networks, and medicine. Three models based on the fuzzy time series concept have been used for time-series forecasting. In order to choose the best model, the two-stage algorithm based on the comparison of time series and model tendencies has been proposed. In addition to the already known quality criterion, the new ones are also exploited in the algorithm. In the conclusion, the results obtained are discussed and the effectiveness of the suggested algorithm is demonstrated.

Fuzzy tendency, fuzzy time series, forecasting, linguistic description.

2015_ 4

Sections: Information systems

Subjects: Information systems.


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