Big Data

@article{Klein2013BigD,
  title={Big Data},
  author={Dominik Klein and Phuoc Tran-Gia and Matthias Hartmann},
  journal={Informatik-Spektrum},
  year={2013},
  volume={36},
  pages={319-323}
}
Morteza Mardani,∗, Gonzalo Mateos∗∗ and Georgios B. Giannakisa,† ∗Stanford University, Department of Electrical Engineering, 350 Serra Mall, Stanford, CA, 94305 ∗∗University of Rochester, Department of Electrical and Computer Engineering, 413 Hopeman, Rochester, NY, 14627 †University of Minnesota, Department of Electrical and Computer Engineering, 200 Union Street SE, Minneapolis, MN 55455 aCorresponding: georgios@umn.edu 
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ece.rochester.edu
15 Citations
Background Citations
3
Methods Citations
2

15 Citations

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BISHOP - Big Data Driven Self-Learning Support for High-performance Ontology Population
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Big Data in the Metal Processing Value Chain: A Systematic Digitalization Approach under Special Consideration of Standardization and SMEs
Within the rise of the fourth industrial revolution, the role of Big Data became increasingly important for a successful digital transformation in the manufacturing environment. The acquisition,…
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References

SHOWING 1-10 OF 68 REFERENCES
Modeling and Optimization for Big Data Analytics: (Statistical) learning tools for our era of data deluge
TLDR
This article contributes to the ongoing cross-disciplinary efforts in data science by putting forth encompassing models capturing a wide range of SP-relevant data analytic tasks, such as principal component analysis (PCA), dictionary learning (DL), compressive sampling (CS), and subspace clustering.
Parallel and Distributed Computation: Numerical Methods
TLDR
This work discusses parallel and distributed architectures, complexity measures, and communication and synchronization issues, and it presents both Jacobi and Gauss-Seidel iterations, which serve as algorithms of reference for many of the computational approaches addressed later.
On GROUSE and incremental SVD
  • L. Balzano, Stephen J. Wright
  • Computer Science
    2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
  • 2013
TLDR
This paper modifications the incremental SVD approach to handle missing data, and demonstrates that this modified approach is equivalent to GROUSE, for a certain choice of an algorithmic parameter.
Statistical Analysis of Network Data: Methods and Models
TLDR
This book provides an up-to-date treatment of the foundations common to the statistical analysis of network data across the disciplines, and is the first such resource to present material on all of these core topics in one place.
Parallel stochastic gradient algorithms for large-scale matrix completion
TLDR
Jellyfish, an algorithm for solving data-processing problems with matrix-valued decision variables regularized to have low rank, is developed, which is orders of magnitude more efficient than existing codes.
Classification of streaming big data with misses
TLDR
A novel approach that leverages the intrinsic low-dimensionality of (possibly large-scale) data to design a support-vector-machine (SVM) classifier from feature vectors with misses `on the fly' is advocated.
DMFSGD: A Decentralized Matrix Factorization Algorithm for Network Distance Prediction
TLDR
This paper formulates the prediction problem as matrix completion where the unknown entries in a pairwise distance matrix constructed from a network are to be predicted, and assumes that the distance matrix has low-rank characteristics, so the problem is solvable by low- rank approximation based on matrix factorization.
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
TLDR
It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.
Matrix Completion With Noise
TLDR
This paper surveys the novel literature on matrix completion and introduces novel results showing that matrix completion is provably accurate even when the few observed entries are corrupted with a small amount of noise, and shows that, in practice, nuclear-norm minimization accurately fills in the many missing entries of large low-rank matrices from just a few noisy samples.
Dynamic Anomalography: Tracking Network Anomalies Via Sparsity and Low Rank
TLDR
Comprehensive numerical tests with both synthetic and real network data corroborate the effectiveness of the proposed online algorithms and their tracking capabilities, and demonstrate that they outperform state-of-the-art approaches developed to diagnose traffic anomalies.
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