An intelligent information system for analysing the mobile applications market

Keywords: mobile applications, monitoring of online stores, data aggregation, interpretation of functional dependencies, regression analysis, data interpolation and extrapolation, online sales dynamics

Abstract

The article considers the main advantages and disadvantages of similar information systems for the analysis of online application stores: “AppFollow”, “42matters: App Market Data & Mobile Audience Data”, and “AppAnnie”. The article presents a functional diagram of the system being developed, which includes four functional blocks responsible for monitoring online application stores, updating data in the database, obtaining aggregated data, and visualising the results of the aggregation. The visualisation block is considered in detail. This block uses the results of aggregation and custom visualisation settings to determine the parameters and to create graphs, charts, and reports. Interpolation, extrapolation, and regression analysis can be performed based on the plotted graphs. The article presents a mathematical model of the database for the developed information system. It also suggests a mathematical description of the formal language designed to describe functional dependencies between various characteristics of mobile applications. The formal language is used during the stages of data aggregation and visualisation. The language model of the formal language allows building more than 100 types of graphs and can be used in other subject areas. The article considers the functions of the developed information system, which solves the problems of monitoring, collecting, and updating information about various characteristics of mobile applications in online stores. The developed information system provides analysts with tools for automating the procedure of comprehensive analysis of the sales dynamics of online application stores. The system allows developers of mobile applications to determine the most promising directions, to estimate sales volumes in various market segments, and to obtain new knowledge. For instance, during the analysis, a correlation was found between the parameters “Rating” and “Date of last update” — the more often the application is updated, the higher its rating.

Downloads

Download data is not yet available.

Author Biographies

Anastasia S. Zueva, Bryansk State Technical University

a 6-year student, Department of Computer Technologies and Systems, Bryansk State Technical University

Yuri A. Leonov, Bryansk State Technical University

PhD in Technical Sciences, Associate Professor, Department of Computer Technologies and Systems, Bryansk State Technical University

Maxim V. Terekhov, Bryansk State Technical University

PhD in Technical Sciences, Associate Professor, Department of Computer Technologies and Systems, Bryansk State Technical University

Rodion A. Filippov, Bryansk State Technical University

PhD in Technical Sciences, Associate Professor, Department of Computer Technologies and Systems, Bryansk State Technical University

Alexander A. Kuzmenko, Bryansk State Technical University

PhD in Biology, Associate Professor, Department of Computer Technologies and Systems, Bryansk State Technical University

References

1. Narang U., Shankar V. Mobile Marketing 2.0: State of the Art and Research Agenda // Marketing in a Digital World. 2019. 16. P. 97–119. DOI
2. Liu X. et al. Micro- and macro-level churn analysis of large-scale mobile games // Knowledge and Information Systems. 2019. 62 (4). P. 1465–1496. DOI
3. Kim S., Baek T. H. Examining the antecedents and consequences of mobile app engagement // Telematics and Informatics. 2018. 35 (1). P. 148–158. DOI
4. Rutz O., Aravindakshan A., Rubel O. Measuring and forecasting mobile game app engagement // International Journal of Research in Marketing. 2019. 36 (2). P. 185–199. DOI
5. Finkelstein A. et al. Investigating the relationship between price, rating, and popularity in the Blackberry World App Store // Information and Software Technology. 2017. 87. P. 119–139. DOI
6. Al-Subaihin A. et al. App store mining and analysis // DeMobile 2015: Proceedings of the 3rd International Workshop on Software Development Lifecycle for Mobile. 2015. P. 1–2. DOI
7. Martin W. et al. A Survey of App Store Analysis for Software Engineering // IEEE Transactions on Software Engineering. 2017. 43 (9). P. 817–847. DOI
8. AppFollow. Available at: URL
9. 42matters. Available at: URL
10. AppAnnie. Available at: URL
11. Zueva A. S., Leonov Yu. A. Researching the functionality of application stores analytics systems // Akademicheskaya publicistika. 2018. 03. P. 6–9. (in Russian).
12. Manenti G. et al. Functional Modelling and IDEF0 to Enhance and Support Process Tailoring in Systems Engineering // 2019 International Symposium on Systems Engineering (ISSE). 2019. P. 1–8. DOI
13. Islam K. et al. Huge and Real-Time Data-base Systems: A Comparative Study and Review for SQL Server 2016, Oracle 12c & MySQL 5.7 for Personal Computer // Journal of Basic & Applied Sciences. 2017. 13. P. 481–490. DOI
14. Zueva A. S., Leonov Yu. A. Forecasting the dynamics of changes in volume of sales of mobile applications category “Business” // International Journal of Advanced Studies in Computer Engineering. 2019. 01. P. 12–15. (in Russian).
15. Wang S., Wu W., Zhou X. App Store Analysis: Using Regression Model for App Downloads Prediction // Social Computing. ICYCSEE 2016. Communications in Computer and Information Science. 2016. 623. P. 206–220. DOI
16. Mundbrod N., Reichert M. Object-Specific Role-Based Access Control // International Journal of Cooperative Information Systems. 2019. 28 (01). P. 1–30. 1950003. DOI
17. McIlroy S., Ali N., Hassan A. E. Fresh apps: an empirical study of frequently-updated mobile apps in the Google play store // Empirical Soft-ware Engineering. 2015. 21 (3). P. 1346–1370. DOI
18. Nayebi M., Adams B., Ruhe G. Release Practices for Mobile Apps – What do Users and Developers Think? // 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER). 2016. P. 552–562. DOI
19. Tao K., Edmunds P. Mobile APPs and Global Markets // Theoretical Economics Letters. 2018. 08 (08). P. 1510–1524. DOI
20. Tang A. K. Y. Mobile App Monetization: App Business Models in the Digital Era // International Journal of Innovation, Management and Technology. 2016. 7. P. 224–227. DOI
Published
2021-02-02
How to Cite
Zueva, A. S., Leonov, Y. A., Terekhov, M. V., Filippov, R. A., & Kuzmenko, A. A. (2021). An intelligent information system for analysing the mobile applications market. Proceedings of Voronezh State University. Series: Systems Analysis and Information Technologies, (4), 98-110. https://doi.org/10.17308/sait.2020.4/3207
Section
Intelligent Information Systems, Data Analysis and Machine Learning