533 research outputs found
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Accelerating Iterative Computations for Large-Scale Data Processing
Recent advances in sensing, storage, and networking technologies are creating massive amounts of data at an unprecedented scale and pace. Large-scale data processing is commonly leveraged to make sense of these data, which will enable companies, governments, and organizations, to make better decisions and bring convenience to our daily life. However, the massive amount of data involved makes it challenging to perform data processing in a timely manner. On the one hand, huge volumes of data might not even fit into the disk of a single machine. On the other hand, data mining and machine learning algorithms, which are usually involved in large-scale data processing, typically require time-consuming iterative computations. Therefore, it is imperative to efficiently perform iterative computations on large computer clusters or cloud using highly-parallel and shared-nothing distributed systems.
This research aims to explore new forms of iterative computations that reduce unnecessary computations so as to accelerate large-scale data processing in a distributed environment. We propose the iterative computation transformation for well-known data mining and machine learning algorithms, such as expectation-maximization, nonnegative matrix factorization, belief propagation, and graph algorithms (e.g., PageRank). These algorithms have been used in a wide range of application domains. First, we show how to accelerate expectation-maximization algorithms with frequent updates in a distributed environment. Then, we illustrate the way of efficiently scaling distributed nonnegative matrix factorization with block-wise updates. Next, our approach of scaling distributed belief propagation with prioritized block updates is presented. Last, we illustrate how to efficiently perform distributed incremental computation on evolving graphs.
We will elaborate how to implement these transformed iterative computations on existing distributed programming models such as the MapReduce-based model, as well as develop new scalable and efficient distributed programming models and frameworks when necessary. The goal of these supporting distributed frameworks is to lift the burden of the programmers in specifying transformation of iterative computations and communication mechanisms, and automatically optimize the execution of the computation. Our techniques are evaluated extensively to demonstrate their efficiency. While the techniques we propose are in the context of specific algorithms, they address the challenges commonly faced in many other algorithms
Fast Real-Time DC State Estimation in Electric Power Systems Using Belief Propagation
We propose a fast real-time state estimator based on the belief propagation
algorithm for the power system state estimation. The proposed estimator is easy
to distribute and parallelize, thus alleviating computational limitations and
allowing for processing measurements in real time. The presented algorithm may
run as a continuous process, with each new measurement being seamlessly
processed by the distributed state estimator. In contrast to the matrix-based
state estimation methods, the belief propagation approach is robust to
ill-conditioned scenarios caused by significant differences between measurement
variances, thus resulting in a solution that eliminates observability analysis.
Using the DC model, we numerically demonstrate the performance of the state
estimator in a realistic real-time system model with asynchronous measurements.
We note that the extension to the AC state estimation is possible within the
same framework.Comment: 6 pages; 7 figures; submitted in the IEEE International Conference on
Smart Grid Communications (SmartGridComm 2017
Cooperative Simultaneous Localization and Synchronization in Mobile Agent Networks
Cooperative localization in agent networks based on interagent time-of-flight
measurements is closely related to synchronization. To leverage this relation,
we propose a Bayesian factor graph framework for cooperative simultaneous
localization and synchronization (CoSLAS). This framework is suited to mobile
agents and time-varying local clock parameters. Building on the CoSLAS factor
graph, we develop a distributed (decentralized) belief propagation algorithm
for CoSLAS in the practically important case of an affine clock model and
asymmetric time stamping. Our algorithm allows for real-time operation and is
suitable for a time-varying network connectivity. To achieve high accuracy at
reduced complexity and communication cost, the algorithm combines particle
implementations with parametric message representations and takes advantage of
a conditional independence property. Simulation results demonstrate the good
performance of the proposed algorithm in a challenging scenario with
time-varying network connectivity.Comment: 13 pages, 6 figures, 3 tables; manuscript submitted to IEEE
Transaction on Signal Processin
Dynamic Compressive Sensing of Time-Varying Signals via Approximate Message Passing
In this work the dynamic compressive sensing (CS) problem of recovering
sparse, correlated, time-varying signals from sub-Nyquist, non-adaptive, linear
measurements is explored from a Bayesian perspective. While there has been a
handful of previously proposed Bayesian dynamic CS algorithms in the
literature, the ability to perform inference on high-dimensional problems in a
computationally efficient manner remains elusive. In response, we propose a
probabilistic dynamic CS signal model that captures both amplitude and support
correlation structure, and describe an approximate message passing algorithm
that performs soft signal estimation and support detection with a computational
complexity that is linear in all problem dimensions. The algorithm, DCS-AMP,
can perform either causal filtering or non-causal smoothing, and is capable of
learning model parameters adaptively from the data through an
expectation-maximization learning procedure. We provide numerical evidence that
DCS-AMP performs within 3 dB of oracle bounds on synthetic data under a variety
of operating conditions. We further describe the result of applying DCS-AMP to
two real dynamic CS datasets, as well as a frequency estimation task, to
bolster our claim that DCS-AMP is capable of offering state-of-the-art
performance and speed on real-world high-dimensional problems.Comment: 32 pages, 7 figure
Distributed Inference over Linear Models using Alternating Gaussian Belief Propagation
We consider the problem of maximum likelihood estimation in linear models
represented by factor graphs and solved via the Gaussian belief propagation
algorithm. Motivated by massive internet of things (IoT) networks and edge
computing, we set the above problem in a clustered scenario, where the factor
graph is divided into clusters and assigned for processing in a distributed
fashion across a number of edge computing nodes. For these scenarios, we show
that an alternating Gaussian belief propagation (AGBP) algorithm that
alternates between inter- and intra-cluster iterations, demonstrates superior
performance in terms of convergence properties compared to the existing
solutions in the literature. We present a comprehensive framework and introduce
appropriate metrics to analyse AGBP algorithm across a wide range of linear
models characterised by symmetric and non-symmetric, square, and rectangular
matrices. We extend the analysis to the case of dynamic linear models by
introducing dynamic arrival of new data over time. Using a combination of
analytical and extensive numerical results, we show the efficiency and
scalability of AGBP algorithm, making it a suitable solution for large-scale
inference in massive IoT networks.Comment: 14 pages, 18 figure
Distributed field estimation in wireless sensor networks
This work takes into account the problem of distributed estimation of a physical field of interest through a wireless sesnor networks
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