Abstract
The increased use of multiple types of smart devices in several application domains, opens the pathways for the collection of humongous volumes of data. At the same time, the need for processing of only a subset of these data by applications in order to quickly conclude tasks execution and knowledge extraction, has resulted in the adoption of a very high number of queries set into distributed datasets. As a result, a significant process is the efficient response to these queries both in terms of time and the appropriate data. In this paper, we present a hierarchical query-driven clustering approach, for performing efficient data mapping in remote datasets for the management of future queries. Our work differs from other current methods in the sense that it combines a Query-Based Learning (QBL) model with a hierarchical clustering in the same methodology. The performance of the proposed model is assessed by a set of experimental scenarios while we present the relevant numerical outcomes.
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References
Kolomvatsos, K., Anagnostopoulos, C.: A probabilistic model for assigning queries at the edge. Computing 102(4), 865–892 (2019). https://doi.org/10.1007/s00607-019-00767-8
Kolomvatsos, K., Anagnostopoulos, C.: Reinforcement learning for predictive analytics in smart cities. Informatics 4(3), 16 (2017). https://doi.org/10.3390/informatics4030016
Savva, F., Anagnostopoulos, C., Triantafillou, P.: Adaptive learning of aggregate analytics under dynamic workloads. Futur. Gener. Comput. Syst. 109, 317–330 (2020)
Wasay, A., Wei, X., Dayan, N., Idreos, S.: Data canopy. In: Proceedings of the 2017 ACM International Conference on Management of Data (2017)
Vulimiri, A., et al.: WANalytics. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (2015)
Na, S., Xumin, L., Yong, G.: Research on k-means clustering algorithm: an improved k-means clustering algorithm. In: 2010 3rd International Symposium on Intelligent Information Technology and Security Informatics (2010)
Schwämmle, V., Jensen, O.: A simple and fast method to determine the parameters for fuzzy c-means cluster analysis. Bioinformatics 26, 2841–2848 (2010)
Etehadtavakol, M., Sadri, S., Ng, E.: Application of K- and fuzzy c-means for color segmentation of thermal infrared breast images. J. Med. Syst. 34, 35–42 (2008)
Jamel, A.M., Akay, B.: A survey and systematic categorization of parallel k-means and fuzzy-c-means algorithms. Comput. Syst. Sci. Eng. 34, 259–281 (2019)
Gupta, R., Muttoo, S.K., Pal, S.K.: Fuzzy c-means clustering and particle swarm optimization based scheme for common service center location allocation. Appl. Intell. 47(3), 624–643 (2017). https://doi.org/10.1007/s10489-017-0917-0
Stetco, A., Zeng, X., Keane, J.: Fuzzy c-means++: fuzzy c-means with effective seeding initialization. Exp. Syst. Appl. 42, 7541–7548 (2015)
Chang, R., Hsu, H., Lin, S., Chang, C., Ho, J.: Query-based learning for dynamic particle swarm optimization. IEEE Access. 5, 7648–7658 (2017)
Albishre, K., Li, Y., Xu, Y., Huang, W.: Query-based unsupervised learning for improving social media search. World Wide Web 23(3), 1791–1809 (2019). https://doi.org/10.1007/s11280-019-00747-0
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Fountas, P., Papathanasaki, M., Kolomvatsos, K., Anagnostopoulos, C. (2022). Query Driven Data Subspace Mapping. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 647. Springer, Cham. https://doi.org/10.1007/978-3-031-08337-2_41
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DOI: https://doi.org/10.1007/978-3-031-08337-2_41
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