Real-Time High-Load Infrastructure Transaction Status Output Prediction Using Operational Intelligence and Big Data Technologies

An approach to use Operational Intelligence with mathematical modeling and Machine Learning to solve industrial technology projects problems are very crucial for today’s IT (information technology) processes and operations, taking into account the exponential growth of information and the growing trend of Big Data-based projects. Monitoring and managing high-load data projects require new approaches to infrastructure, risk management, and data-driven decision support. Key difficulties that might arise when performing IT Operations are high error rates, unplanned downtimes, poor infrastructure KPIs and metrics. The methods used in the study include machine learning models, data preprocessing, missing data imputation, SRE (site reliability engineering) indicators computation, quantitative research, and a qualitative study of data project demands. A requirements analysis for the implementation of an Operational Intelligence solution with Machine learning capabilities has been conducted and represented in the study. A model based on machine learning algorithms for transaction status code and output predictions, in order to execute system load testing, risks identification and, to avoid downtimes, is developed. Metrics and indicators for determining infrastructure load are given in the paper to obtain Operational intelligence and Site reliability insights. It turned out that data mining among the set of Operational Big Data simplifies the task of getting an understanding of what is happening with requests within the data acquisition pipeline and helps identify errors before a user faces them. Transaction tracing in a distributed environment has been enhanced using machine learning and mathematical modelling. Additionally, a step-by-step algorithm for applying the application monitoring solution in a data-based project, especially when it is dealing with Big Data is described and proposed within the study

Intelligent Academic Specialties Selection in Higher Education for Ukrainian Entrants: A Recommendation System

In this article, we provide an approach to solve the problem of academic specialty selection in higher educational institutions with Ukrainian entrants as our target audience. This concern affects operations at universities or other academic institutions, the labor market, and the availability of in-demand professionals. We propose a decision-making architecture for a recommendation system to assist entrants with specialty selection as a solution. The modeled database is an integral part of the system to provide an in-depth university specialties description. We consider developing an API to consume the data and return predictions to users in our future studies. The exploratory data analysis of the 2021 university admission campaign in Ukraine confirmed our assumptions and revealed valuable insights into the specifics of specialty selection among entrants. We developed a comprehension that most entrants apply for popular but not necessarily in-demand specialties at universities. Our findings on association rules mining show that entrants are able to select alternative specialties adequately. However, it does not lead to successful admission to a desired tuition-free education form in all cases. So, we find it appropriate to deliver better decision-making on specialty selection, thus increasing the likelihood of university admission and professional development based on intelligent algorithms, user behavior analytics, and consultations with academic and career orientation experts. The results will be built into an intelligent virtual entrant’s assistant as a service

User-Engagement Score and SLIs/SLOs/SLAs Measurements Correlation of E-Business Projects Through Big Data Analysis

The Covid-19 crisis lockdown caused rapid transformation to remote working/learning modes and the need for e-commerce-, web-education-related projects development, and maintenance. However, an increase in internet traffic has a direct impact on infrastructure and software performance. We study the problem of accurate and quick web-project infrastructure issues/bottleneck/overload identification. The research aims to achieve and ensure the reliability and availability of a commerce/educational web project by providing system observability and Site Reliability Engineering (SRE) methods. In this research, we propose methods for technical condition assessment by applying the correlation of user-engagement score and Service Level Indicators (SLIs)/Service Level Objectives (SLOs)/Service Level Agreements (SLAs) measurements to identify user satisfaction types along with the infrastructure state. Our solution helps to improve content quality and, mainly, detect abnormal system behavior and poor infrastructure conditions. A straightforward interpretation of potential performance bottlenecks and vulnerabilities is achieved with the developed contingency table and correlation matrix for that purpose. We identify big data and system logs and metrics as the central sources that have performance issues during web-project usage. Throughout the analysis of an educational platform dataset, we found the main features of web-project content that have high user-engagement and provide value to services’ customers. According to our study, the usage and correlation of SLOs/SLAs with other critical metrics, such as user satisfaction or engagement improves early indication of potential system issues and avoids having users face them. These findings correspond to the concepts of SRE that focus on maintaining high service availability