Abstract
3D content streaming and rendering system has attracted a significant attention from both academia and industry. However, these systems struggle to provide comparable quality to that of locally stored and rendered 3D data. Since the rendered 3D content on to the client machine is controlled by the users, their interactions have a strong impact on the performance of 3D content streaming and rendering system. Thus, considering user behaviours in these systems could bring significant performance improvements. To achieve this, we propose a symbolic decision tree that captures all attributes that are part of user interactions. The symbolic decision trees are built by pre-processing the attribute values gathered when the user interacts with the 3D dynamic object. We validate our constructed symbolic tree through another set of interactions over the 3D dynamic object by the same user. The validation shows that our symbolic decision tree model can learn the user interactions and is able to predict several interactions with very limited set of summarized symbolic interval data and thus could help in optimizing the 3D content streaming and rendering system to achieve better performance.
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References
Blanc, A.L., Bunt, J., Petch, J. and Kwok, Y.: The virtual learning space: an interactive 3D environment. In Proceedings of the Tenth international Conference on 3D Web Technology, Web3D ‘05. ACM, New York, NY, pp. 93–102, (2005).
Vani, V., Pradeep Kumar, R. and Mohan, S.: Profiling User Interactions of 3D Complex Meshes for Predictive Streaming & Rendering. Lecture Notes in Electrical Engineering, Vol. 221(1), pp. 457–467, Springer ISSN 18761100, (2013).
Vani, V., Pradeep Kumar, R. and Mohan, S.: Predictive Modeling of User Interaction Patterns for 3D Mesh Streaming. International Journal of Information Technology and Web Engineering, Vol. 7(4), pp. 1–19, DOI:10.4018/jitwe.2012100101, (2012).
Vani, V., Mohan S.: Neural Network based Predictors for 3D content streaming and rendering. 18th ICSEC 2014, KKU, Thailand, IEEE pp. 452–457, ISBN 978-1-4799-4965-6, (2014).
Vani V., Pradeep Kumar R., Mohan S.: 3D Mesh Streaming based on Predictive Modeling, Journal of Computer Science 8 (7): 1123–1133, 2012, ISSN 1549-3636, Science Publications (2012).
Han, J., Kamber, M. and Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., p. 744, chapter 6, ISBN: 9780123814791, (2011).
Diday E.: An Introduction to Symbolic Data and Analysis and the SODAS software. Journal of Symbolic Data Analysis, Vol. 0, No. 0, ISSN 1723-5081, (2002).
Mballo, C. and Diday, E.: Decision trees on interval valued variables. Journal of Symbolic Data Analysis, Vol. 3, No. 1, ISSN 1723-5081, (2005).
Perner P., Belikova T.B., and Yashunskaya N.I.: Knowledge Acquisition by Decision Tree Induction for Interpretation of Digital Images in Radiology. Advances in Structural and Syntactical Pattern Recognition, P. Perner, P. Wang, and A. RosenFeld (Eds.), Springer Verlang LNC 1121, (1996).
Seck D., Billard L., Diday.E. and Afonso F.: A Decision Tree for Interval-valued Data with Modal Dependent Variable. COMSTAT, (2010).
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We thank the management of Al Yamamah University, KSA for supporting financially to publish our research work.
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Vani, V., Mohan, S. (2016). Symbolic Decision Tree for Interval Data—An Approach Towards Predictive Streaming and Rendering of 3D Models. In: Satapathy, S., Mandal, J., Udgata, S., Bhateja, V. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 433. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2755-7_15
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DOI: https://doi.org/10.1007/978-81-322-2755-7_15
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