New challenges in the dynamically changing business environment require companies to experience digital transformation and more effective use of Big Data generated in their expanding online business activities. A possible solution for solving real business problems concerning Big Data resources is proposed in this paper. The defined Agile Elastic Desktop Corporate Architecture for Big Data is based on virtualizing the unused desktop resources and organizing them in order to serve the needs of Big Data processing, thus saving resources needed for additional infrastructure in an organization. The specific corporate business needs are analyzed within the developed R&D environment and, based on that, the unused desktop resources are customized and configured into required Big Data tools. The R&D environment of the proposed Agile Elastic Desktop Corporate Architecture for Big Data could be implemented on the available unused resources of hundreds desktops.

The research explores post-pandemic travel intentions, the availability of travel funds and accommodation preferences in Bulgaria and Azerbaijan – two countries with similar demographic and territorial characteristics but relatively different consumption patterns and tourist behaviour. In both countries, tourism forms a major part of the national economy and it has been hit by the ongoing COVID-19 pandemic. The findings reveal major differences between the responses received in these countries in terms of how respondents perceive the current phase of the pandemic, and of post-pandemic travel intentions. The study concludes that it will be difficult to implement a common strategy for a post-pandemic tourism recovery.

A critical review of the most popular statistical ideas and methods related to the protection of statistical confidentiality and the control of disclosure of confidential statistical data is provided. Issues that are essential for both producers and users of statistical information but have not yet been widely discussed in the specialized Bulgarian scientific literature are also addressed.

A description of the mathematical model of a neural network classifier of data on healthcare in the institutions of the Lipetsk region is given in order to identify atypical (abnormal) records. Anomaly detection refers to the problem of finding data that is inconsistent with some expected process behavior or metric occurring in the system. Due to the large number of inputs to the neural network model, the time it takes to process the incoming information also increases. To assess what factors should be transmitted to the input of the neural network classifier, an approach to the reduction of the neural network model based on sensitivity analysis is proposed. The description of a set of software tools for solving the problem is presented.