For the purpose of improving the efficiency and quality of management decisions, preventing and eliminating the consequences of crises and emergency situations, the Information and Analytical Situation Center (IASC) operates as part of WDC-Ukraine, providing:
- analysis and scenario modeling of complex socio-economic systems,
- automated construction of possible scenarios for their development,
- development of management decision alternatives, calculation of their key parameters, and assessment of their consequences.
The concept of the situation center was proposed by the British cybernetician Stafford Beer in the 1970s. The first situation center for a nation’s top leadership was created under Beer’s guidance in the late 1970s.
According to the type of management tasks supported, three classes of situation centers are distinguished:
- Operational and dispatch centers, designed for solving real-time operational management tasks for complex organizational and technological processes (with primary focus on the presentation component for collective use).
- Situation centers for crisis analysis and management (operational headquarters for managing the processes of localization and elimination of crisis consequences).
- Information and analytical situation centers (multi-purpose centers providing information support for decision-making based on analytical processing of large volumes of heterogeneous data).
Concept of the WDC-Ukraine Information and Analytical Situation Center:
The structure of the IASC as an organizational and technical system includes various types of support: software, technical, organizational, informational, mathematical, etc.
The core of the IASC software suite is the online Advanced Analytics Platform, developed by WDC-Ukraine specialists. It is a distributed information system with a service-oriented architecture (technology stack: MongoDB, NodeJS, Foundation Zurb, AngularJS, React, Data-Driven Documents, etc.), designed to solve tasks that arise during the continuous data lifecycle used for scientific and applied research. The platform includes resources for monitoring indicator states, advanced tools for data analysis and visualization. The set of analytical tools is built on modern IT technologies, ensuring high reliability and usability. Each tool is implemented as a separate service responsible for the operation of a specific data processing algorithm.
The IASC operates on a technical platform that includes big data processing servers, data storage systems, and a high-performance computing cluster, which together allow for timely processing and analysis of large-scale heterogeneous data.
Mathematical tools includes:
- Factor analysis;
- Entropy analysis;
- Correlation analysis;
- Cluster analysis;
- Regression analysis;
- Geospatial data analysis;
- Optimization methods;
- Machine learning;
- Network and graph analysis;
- Causal relationship discovery (Bayesian belief networks, Ishikawa method, cross-impact method, inductive methods, etc.);
- Expert evaluation methods (Delphi method, brainstorming, analytic hierarchy process);
- Strategic planning methods (SWOT analysis, GAP analysis, etc.).
Both widely known Data Mining, Machine Learning, and Text Mining methods and their modifications developed by ER IASA and WDC-Ukraine experts are used.
The IASC team provides consulting, methodological, and technological support services for collaborative decision-making in situation center mode, aimed at forecasting and/or predicting the development of the object (enterprise, city, territory, region, country, etc.) and building tactical and/or strategic action plans to achieve the desired future of the research object.
IASC services include:
- Support for foresight studies;
- Methodological and instrumental support for brainstorming sessions, panels, and meetings using methods such as futures wheel, horizon scanning, morphological and structural analysis, scenario building, SWOT analysis, causal analysis, Delphi method, STEEP analysis, etc.;
- Construction of short- and medium-term forecasts of key indicators using correlation-regression analysis, time series analysis, machine learning (decision trees, neural networks), system dynamics, and other methods;
- onstruction of composite indicators to assess the degree of achievement of key development goals of the object;
- Multidimensional statistical analysis of data about the object to identify hidden cause-and-effect relationships between phenomena, events, and characteristics;
- Monitoring and parsing of open information sources on specified topics, data collection from new sources, and providing access to text analytics tools;
- Modeling of natural and anthropogenic phenomena and processes based on satellite data, geospatial data analysis.
