Visual Analytics Techniques for Large scale Multimedia Datasets: Interactive Visualization and Decision Support in Creative Industries and Big Data Applications
Keywords:
Visual analytics, Decision-making, Multimedia datasets, Creative industries, Data visualizationAbstract
This study explores the role of visual analytics in enhancing decision-making processes within creative industries, focusing on its application to large-scale multimedia datasets. Visual analytics integrates interactive visualization techniques with computational algorithms, enabling users to explore complex datasets intuitively and derive actionable insights. The research centers on the design and implementation of interactive dashboards tailored to the creative sector, particularly film, music, and advertising industries, to facilitate real-time data exploration. The study also investigates the usability of these tools through expert-based evaluations, aiming to assess their effectiveness in supporting informed and timely decision-making. The findings reveal that interactive visualizations significantly improve insight discovery and pattern recognition, enabling decision-makers to uncover hidden trends in large multimedia datasets. However, challenges related to scalability, user acceptance, and real-time processing were encountered during the implementation phase. The research highlights the practical benefits of integrating visual analytics into industry workflows, which include enhanced content creation, audience engagement, and strategic planning. Furthermore, the study identifies key visual analytics techniques such as dynamic dashboards, pattern recognition, data mining, and clustering, which are essential for analyzing multimedia data. The study concludes by emphasizing the potential for wider applications of visual analytics in other sectors, suggesting future research directions to improve tool performance, scalability, and user accessibility, as well as exploring the integration of emerging technologies like artificial intelligence and virtual reality.
References
[1] D. Arora and P. Malik, “Analytics: Key to go from generating big data to deriving business value,” in Proceedings - 2015 IEEE 1st International Conference on Big Data Computing Service and Applications, BigDataService 2015, 2015, pp. 446 – 452. doi: 10.1109/BigDataService.2015.62.
[2] B. Thomee, D. Borth, and J. Bernd, “Multimedia COMMONS Workshop 2016 (MMCommons’16): Datasets, Evaluation, and Reproducibility,” in MM 2016 - Proceedings of the 2016 ACM Multimedia Conference, 2016, pp. 1485 – 1486. doi: 10.1145/2964284.2980533.
[3] C. Sharma, G. Poornalatha, and A. Shenoy, “A Comprehensive Review of Recent Advances in Multimodal Multimedia Indexing and Retrieval,” IEEE Access, vol. 13, pp. 143688 – 143712, 2025, doi: 10.1109/ACCESS.2025.3597396.
[4] J. Zhang et al., “Guest Editorial Learning From Noisy Multimedia Data,” IEEE Trans. Multimed., vol. 24, pp. 1247 – 1252, 2022, doi: 10.1109/TMM.2022.3159014.
[5] J. Song, H. Jegou, C. Snoek, Q. Tian, and N. Sebe, “Guest Editorial: Large-Scale Multimedia Data Retrieval, Classification, and Understanding,” IEEE Trans. Multimed., vol. 19, no. 9, pp. 1965 – 1967, 2017, doi: 10.1109/TMM.2017.2733638.
[6] V. K. Singh, S. Pongpaichet, and R. Jain, “Situation recognition from multimodal data,” in MM 2016 - Proceedings of the 2016 ACM Multimedia Conference, 2016, pp. 1475 – 1476. doi: 10.1145/2964284.2986913.
[7] D. Prabhu, M. Dhivya, A. Gowridurga, M. Dinesh Babu, T. P. Anish, and R. Siva Subramanian, “Visual Analytics for Smart Decision-Making: Techniques, Applications, and Future Directions,” in Proceedings of International Conference on Visual Analytics and Data Visualization, ICVADV 2025, 2025, pp. 37 – 43. doi: 10.1109/ICVADV63329.2025.10961125.
[8] G. Wang et al., “A client-based visual analytics framework for large spatiotemporal data under architectural constraints,” in 2017 IEEE Workshop on Data Systems for Interactive Analysis, DSIA 2017, 2017, pp. 1 – 5. doi: 10.1109/DSIA.2017.8339088.
[9] C. M. O. Alves, M. P. Cota, and M. R. G. Castro, “MLV-Viewer: Data Mining Visualizations in Decision Support System; [MLV-Viewer: Visualizacões com Mineracão de Dados em Sistemas de Suporte à Decisão],” in Iberian Conference on Information Systems and Technologies, CISTI, 2020. doi: 10.23919/CISTI49556.2020.9140825.
[10] M. Sharma, C. Puri, and P. Kumar, “A Review: Transformative Impact of Data Visualization Across Various Industries,” in 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024 - Proceedings, 2024, pp. 799 – 804. doi: 10.1109/I-SMAC61858.2024.10714609.
[11] J. Zahalka and M. Worring, “Towards interactive, intelligent, and integrated multimedia analytics,” in 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings, 2015, pp. 3 – 12. doi: 10.1109/VAST.2014.7042476.
[12] J. Lowe and M. Matthee, “Requirements of Data Visualisation Tools to Analyse Big Data: A Structured Literature Review,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12066 LNCS, pp. 469 – 480, 2020, doi: 10.1007/978-3-030-44999-5_39.
[13] E. I. Walsh, Y. Chung, N. Cherbuin, and L. Salvador-Carulla, “Experts’ perceptions on the use of visual analytics for complex mental healthcare planning: An exploratory study,” BMC Med. Res. Methodol., vol. 20, no. 1, 2020, doi: 10.1186/s12874-020-00986-0.
[14] K. A. L. Raustiala and C. J. Sprigman, “The second digital disruption: Streaming and the dawn of data-driven creativity,” New York Univ. Law Rev., vol. 94, no. 6, pp. 1555 – 1622, 2019, [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078629192&partnerID=40&md5=a98aedfd56b16f45a5ef6e88f0cb9e8e
[15] I. M. Tolstad, “The Numbers Don’t Lie!”: Metrics as tools for decision making and strategic planning in music industry organizations. 2024. doi: 10.1007/978-3-031-64013-1_13.
[16] G. Safavi and S. Ghazinoory, “Creative bits: challenges of applying industry 4.0 in digital creative industries,” Multimed. Tools Appl., vol. 84, no. 16, pp. 15897 – 15923, 2025, doi: 10.1007/s11042-024-19701-y.
[17] T. Nguyen, A. M. Storås, V. Thambawita, S. A. Hicks, P. Halvorsen, and M. A. Riegler, “Multimedia Datasets: Challenges and Future Possibilities,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 13834 LNCS, pp. 711 – 717, 2023, doi: 10.1007/978-3-031-27818-1_58.
[18] D. Vaghani, Technical challenges in scaling data infrastructure. 2025. doi: 10.4018/979-8-3693-8472-5.ch004.
[19] S. Mittal and O. P. Sangwan, “Big data analytics using machine learning techniques,” in Proceedings of the 9th International Conference On Cloud Computing, Data Science and Engineering, Confluence 2019, 2019, pp. 203 – 207. doi: 10.1109/CONFLUENCE.2019.8776614.
[20] A. K. Pasayat, P. Upadhyay, and S. Behera, Information processing and analysis through data visualization and data mining. 2025. doi: 10.1201/9781003621102-2.
[21] G. R. Gonzales and F. Horita, “Supporting visual analytics in decision support system: A systematic mapping study,” in IHC 2020 - Proceedings of the 19th Brazilian Symposium on Human Factors in Computing Systems, 2020. doi: 10.1145/3424953.3426483.
[22] B. Pajila, V. Venkadapathi, D. Veeriaya, L. Ravichandran, and G. Sheena, Big data analytics for smart healthcare system. 2025. doi: 10.1016/B978-0-443-36434-1.00018-5.
[23] C. Mehrotra, N. Chitransh, and A. S. Singh, “Building blocks for visualanalytics,” in Proceeding - IEEE International Conference on Computing, Communication and Automation, ICCCA 2017, 2017, pp. 1224 – 1228. doi: 10.1109/CCAA.2017.8229986.
[24] W. Zhang, T. Yao, S. Zhu, and A. El Saddik, “Deep learning-based multimedia analytics: A review,” ACM Trans. Multimed. Comput. Commun. Appl., vol. 15, no. 1s, 2019, doi: 10.1145/3279952.
[25] A. Khan, Visual Analytics for Dashboards: A Step-by-Step Guide to Principles and Practical Techniques. 2024. doi: 10.1007/979-8-8688-0119-8.
[26] S. Lara Priyadharshini and A. Kumar, Empowering business insights: Harnessing TABLEAU’s power in data visualization. 2024. doi: 10.4018/979-8-3693-6537-3.ch008.
[27] J. C. Alvarado-Pérez, H. Bolaños-Ramírez, D. H. Peluffo-Ordóñez, and S. Murillo, “Knowledge discovery in databases from a perspective of intelligent information visualization,” in 2015 20th Symposium on Signal Processing, Images and Computer Vision, STSIVA 2015 - Conference Proceedings, 2015. doi: 10.1109/STSIVA.2015.7330438.
[28] K. Rajakumar and D. Sudheer, “A review of visual information retrieval on massive image data using hadoop,” Int. J. Control Theory Appl., vol. 9, no. 28, pp. 425 – 430, 2016, [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007506870&partnerID=40&md5=14baa5a60547fcf8a11996c0c1f56c6b
