Analysis of large-scale distributed knowledge sources via autonomous cooperative graph mining

Georgiy Levchuk; Andres Ortiz; Xifeng Yan

doi:10.1117/12.2050836

22 May 2014 Analysis of large-scale distributed knowledge sources via autonomous cooperative graph mining

Georgiy Levchuk, Andres Ortiz, Xifeng Yan

Proceedings Volume 9119, Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII; 91190K (2014) https://doi.org/10.1117/12.2050836
Event: SPIE Sensing Technology + Applications, 2014, Baltimore, MD, United States

Abstract

In this paper, we present a model for processing distributed relational data across multiple autonomous heterogeneous computing resources in environments with limited control, resource failures, and communication bottlenecks. Our model exploits dependencies in the data to enable collaborative distributed querying in noisy data. The collaboration policy for computational resources is efficiently constructed from the belief propagation algorithm. To scale to large data sizes, we employ a combination of priority-based filtering, incremental processing, and communication compression techniques. Our solution achieved high accuracy of analysis results and orders of magnitude improvements in computation time compared to the centralized graph matching solution.

Citation Download Citation

Georgiy Levchuk, Andres Ortiz, and Xifeng Yan "Analysis of large-scale distributed knowledge sources via autonomous cooperative graph mining", Proc. SPIE 9119, Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII, 91190K (22 May 2014); https://doi.org/10.1117/12.2050836

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