Open Access Open Access  Restricted Access Subscription or Fee Access

A Distributed Storage Cluster Based Multi-Modal Content Retrieval by Machine Learning

T. Prathima, A. Govardhan, Y. Ramadevi


The multi modal data, the combined form of information with video, text, and audio, is at its of peak of usage as in the various domains such as education, business, and research due to availability of the higher-level infrastructure. The multi-modal data is generated by various content creators for various purposes and the gain in the volume of such data is clearly observable. Also, the significant growth in multi modal data storage, streaming and information management services are gaining popularity. Nonetheless, the primary challenge for such services are the information or content retrieval matched with the lower time complexity and higher accuracy. The primary challenges for these information management services are the highly distributed storage and computational architecture of these services and the extraction of the information from various storage clusters with a proper synchronization is always a challenge. In the recent times, a good number of research attempts can be seen to solve these problems. However, these attempts are criticized for lesser accuracy during the distributed content retrieval and for higher time complexity due to improper handling of the replicated data. The proposed algorithms produce nearly 99% accuracy during the content retrieval process and nearly 20% improvement on time complexity compared with the parallel best research outcomes.


Storage Cluster, Multimodal retrieval, Threshold, Knowledge Discovery.

Full Text:



R. Cowie, E. Douglas-Cowie, K. Karpouzis, G. Caridakis, M. Wallace, S. Kollias, "Recognition of emotional states in natural human-computer interaction" in Multimodal User Interfaces, Berlin, Germany: Springer, pp. 119-153, 2008.

M. Scheutz et al., "Toward affective cognitive robots for human-robot interaction", Proc. Nat. Conf. Artif. Intell., pp. 61-66, 2005.

P. Covington, J. Adams, E. Sargin, "Deep neural networks for YouTube recommendations", Proc. 10th ACM Conf. Recommender Syst., pp. 191-198, 2016.

C. A. Gomez-Uribe, N. Hunt, "The Netflix recommender system: Algorithms business value and innovation", ACM Trans. Manage. Inf. Syst., vol. 6, no. 4, 2016.

M. J. Duque, C. Turla, L. Evangelista, "Effects of emotional state on decision making time", Procedia-Social Behav. Sci., vol. 97, pp. 137-146, Nov. 2013.

D. A. Worthy, K. A. Byrne, S. Fields, "Effects of emotion on prospection during decision-making", Frontiers Psychol., vol. 5, pp. 591, Jun. 2014.

L. Ardissono, P. Torasso, "Dynamic user modeling in a Web store shell", Proc. 14th Eur. Conf. Artif. Intell., pp. 621-625, 2000.

A. Hawalah, M. Fasli, "A multi-agent system using ontological user profiles for dynamic user modelling", Proc. IEEE/WIC/ACM Int. Conf. Web Intell. Intell. Agent Technol., vol. 1, pp. 430-437, Aug. 2011.

J. Li, P. Ren, Z. Chen, Z. Ren, T. Lian, J. Ma, "Neural attentive session-based recommendation", Proc. ACM Conf. Inf. Knowl. Manage., pp. 1419-1428, 2017.

T. Donkers, B. Loepp, J. Ziegler, "Sequential user-based recurrent neural network recommendations", Proc. 11th ACM Conf. Recommender Syst., pp. 152-160, 2017.

Y. J. Ko, L. Maystre, M. Grossglauser, "Collaborative recurrent neural networks for dynamic recommender systems", Proc. J. Mach. Learn. Res. Workshop Conf. Proc., vol. 63, pp. 366-381, 2016.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.