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Information × Registration Number 0123U103045, ( 0223U005869  0225U000371  ) R & D request Title Developing of models of increasing usefulness and self-reinforcing for decentralized big data analysis systems Head Kuznietsov Vladyslav O., к.т.н. Registration Date 29-06-2023 Organization V.M. Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine popup.description1 The goal is to develop models of self-reinforcing effects, their study, in the context of manifestations of synergistic and network effects in big data processing systems, as well as research (analysis) of large decentralized big data processing systems. As part of the research, it is envisaged to study the influence of the actions of system users and the amount of information that forms big data and affect the general informativeness of the data stored in the big data system in the format of metadata, documents, facts and other indicators. The work will explore models of self-reinforcing effects and increasing usefulness in big data within the process of data extraction, processing and use; new models of self-reinforcing and utility-enhancing effects and a model of big digital data analysis will be built. The main tasks of the work are as follows: it is necessary to develop machine learning models on big data to identify network and synergistic effects that affect the value of data and, accordingly, demand through self-reinforcing effects. There is also a need to develop machine learning models that engage statistical metrics that are informative in the context of data value and demand growth. It is necessary to develop approaches for obtaining features from data, taking into account the hidden properties of data and their spatial location. Investigate the spatial location of features, assess anomalies and deviations in the data. Develop methods of visual data analysis and machine learning, taking into account user interactions and the big data platform. Develop big data models that take into account the criteria of value and usefulness of data obtained from various sources and entering the big data processing chain. popup.nrat_date 2024-12-09 Close