Autors: Gancheva, V. S.
Title: SOA Based Multi-Agent Approach for Biological Data Searching and Integration
Keywords: biological database, data integration, heterogeneous data, m

Abstract: Major challenge in the analysis of biological data is to propose an integrated and modern access to the progressively increasing amounts of data in multiple formats, and efficient approaches for their processing. Models for extraction and integration of large amount of genomics data, as well as problems related to heterogeneity, distribution and compatibility of data are presented in this paper. SOA based multi-agent approach for biological data searching and integration is proposed. A conceptual architecture for integrating of distributed biological data based on SOA is designed. The architecture is aimed to automate the data integration and allows the rapid management of large volumes of diverse data sets represented in different formats-relational, NoSQL, flat files. The integration of different databases is solved by using multi-agent architecture.

References

    Issue

    International Journal of Biology and Biomedical Engineering, issue 13, pp. 32-37, 2019, United Kingdom, ISSN 1998-4510

    Цитирания (Citation/s):
    1. Vetova, S., Borovska, P. Medical imaging platforms and cloud solutions for the case study of breast cancer diagnostics, AIP Conference Proceedings, Volume 2172, Article number 020004, 45th International Conference on Application of Mathematics in Engineering and Economics, AMEE 2019; Code 154644 - 2019 - в издания, индексирани в Scopus или Web of Science
    2. Borovska, P., Marinova, M., Tsanov, V., Code optimization of multiple sequence alignment software tool MSA-BG on GPU-accelerated computing infrastructures AIP Conference Proceedings Volume 2172, 13 November 2019, Article number 020006, 45th International Conference on Application of Mathematics in Engineering and Economics, AMEE 2019; Code 154644 - 2019 - в издания, индексирани в Scopus или Web of Science
    3. Vetova, S., Big heterogeneous data integration and analysis, AIP Conference Proceedings 2333, 030007 (2021); https://doi.org/10.1063/5.0043627 - 2021 - в издания, индексирани в Scopus или Web of Science
    4. S. Vetova, Determination of accuracy and probability in the analysis of large-scale biomedical data, AIP Conference Proceedings 2505, 020010 (2022); https://doi.org/10.1063/5.0100689 - 2022 - в издания, индексирани в Scopus или Web of Science
    5. S. Vetova, Biomedical data integration and innovations concept, AIP Conference Proceedings 2505, 020008 (2022); https://doi.org/10.1063/5.0100686 - 2022 - в издания, индексирани в Scopus или Web of Science
    6. Guo S, Xie L. Adaptive Neural Consensus of Unknown Non-Linear Multi-Agent Systems with Communication Noises under Markov Switching Topologies. Mathematics. 2024; 12(1):133. https://doi.org/10.3390/math12010133 - 2024 - в издания, индексирани в Scopus или Web of Science

    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus