Discovering and reconciling value conflicts for numerical data integration
Information Systems - Data extraction, cleaning and reconciliation
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Traffic matrix estimation: existing techniques and new directions
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
A framework for multi-source data fusion
Information Sciences: an International Journal - Special issue: Soft computing data mining
The complexity of matrix completion
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Exact Matrix Completion via Convex Optimization
Foundations of Computational Mathematics
Automatically Estimating and Updating Input-Output Tables
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Solving low-rank matrix completion problems efficiently
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Random weighting estimation for fusion of multi-dimensional position data
Information Sciences: an International Journal
Discovering multi-label temporal patterns in sequence databases
Information Sciences: an International Journal
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Information Sciences: an International Journal
A block-iterative surrogate constraint splitting method for quadratic signal recovery
IEEE Transactions on Signal Processing
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Flow matrices are widely used in many disciplines, but few methods can estimate them. This paper presents a knowledge-based system as capable of estimating and updating large-size time-evolving flow matrix. The system in this paper consists of two major components with the purposes of matrix estimation and parallel optimization. The matrix estimation algorithm interprets and follows users' query scripts, retrieves data from various sources and integrates them for the matrix estimation. The parallel optimization component is built upon a supercomputing facility to utilize its computational power to efficiently process a large amount of data and estimate a large-size complex matrix. The experimental results demonstrate its outstanding performance and the acceptable accuracy by directly and indirectly comparing the estimation matrix with the actual matrix constructed by surveys.