The problem of correctly interpreting data on the mean age of people’s death in different historical periods based on information presented in Wikipedia and included in the Personalities of the Encyclopedia Historical Database is considered. The specificity of these data associated with censoring and the nature of their collection that lead to selectivity is noted. An approach is proposed that takes into account these circumstances, which makes it possible to clarify the obtained patterns of life expectancy and expand the historical range of research.

The paper focuses on the role of small and medium-sized entrepreneurship in the innovative development of modern countries and assesses its level in the Russian economy. We analyze the main strategic documents aimed at the innovative development of the Russian economy and support for small and medium-sized entrepreneurship. In addition, we discuss the current Russian policy regarding small and medium-sized knowledge-intensive entrepreneurship in recent years as part of the overall innovation policy and evaluate its effectiveness. Besides, we pay particular attention to the institutional and infrastructural problems hindering the development of small and medium-sized high-tech entrepreneurship in Russia. In this regard, we discuss the growth potential of small and medium-sized knowledge-based entrepreneurship in Russia and the conditions for its dynamic development.

We study the properties of the new threshold selection method for non-parametric estimation of the extremal index of a stationary sequence proposed in [15]. The method is to apply the so-called A1 discrepancy method based on the Cram´er–von Mises–Smirnov’s statistic calculated by the largest order statistics of a sample. The limit distribution of this statistic is derived if the proportion of the largest order statistics used tends to some nonzero constant. We also use the nonstandard modification of the Cram´er–von Mises–Smirnov’s statistic to propose the goodness-of-fit test procedure of ω2 type for distribution tails.

Extract-transform-load (ETL) processes play a crucial role in data analysis in real-time data warehouse environments which demand low latency and high availability features for functionality. In essence, ETL- processes are becoming bottlenecks in such environments due to complexity growth, number of steps in data transformations, number of machines used for data processing and finally, increasing impact of human factors on development of new ETL-processes. In order to mitigate this impact and provide resilience of the ETL process, a special Metadata Framework is needed that can manage the design of new data pipelines and processes. In this work, we focus on ETL metadata and its use in driving process execution and present a proprietary approach to the design of the metadata-based process control that can reduce complexity, enhance resilience of ETL processes and allow their adaptive self-reorganization. We present a metadata framework implementation which is based on open-source Big Data technologies, describing its architecture and interconnections with external systems, data model, functions, quality metrics, and templates. A test execution of an experimental Airflow Directed Acyclic Graph (DAG) with randomly selected data is performed to evaluate the proposed framework.

In this paper, the architecture of Digital Ecosystems Control based on Predictive Real-Time Situational models is presented. It has been reviewed Data Fusion issues in Digital Ecosystems and proposed a methodological approach for solving such issues based on modern big data technologies, investigated the Digital Ecosystem control in real-time as a situational management , modeled the architecture of the real-time DES control systems with in-memory technologies, message-oriented systems, virtualization, containerization and ability for horizontal scaling. Finally, the paper proposes the principles of predictive models developing for situational control. The methods are based on the intelligent analysis of DES statistical data and knowledge bases development. Retraining the model is carried out in real-time regime.