Entity

Time filter

Source Type

Cambridge, MA, United States

Chen S.,Carnegie Mellon University | Sandryhaila A.,HP Vertica | Moura J.M.F.,Carnegie Mellon University | Kovacevic J.,Carnegie Mellon University
2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 | Year: 2014

Signal recovery from noisy measurements is an important task that arises in many areas of signal processing. In this paper, we consider this problem for signals represented with graphs using a recently developed framework of discrete signal processing on graphs. We formulate graph signal denoising as an optimization problem and derive an exact closed-form solution expressed by an inverse graph filter, as well as an approximate iterative solution expressed by a standard graph filter. We evaluate the obtained algorithms by applying them to measurement denoising for temperature sensors and opinion combination for multiple experts. © 2014 IEEE. Source


Chen S.,Carnegie Mellon University | Varma R.,Carnegie Mellon University | Sandryhaila A.,HP Vertica | Kovacevic J.,Carnegie Mellon University
IEEE Transactions on Signal Processing | Year: 2015

We propose a sampling theory for signals that are supported on either directed or undirected graphs. The theory follows the same paradigm as classical sampling theory. We show that perfect recovery is possible for graph signals bandlimited under the graph Fourier transform. The sampled signal coefficients form a new graph signal, whose corresponding graph structure preserves the first-order difference of the original graph signal. For general graphs, an optimal sampling operator based on experimentally designed sampling is proposed to guarantee perfect recovery and robustness to noise; for graphs whose graph Fourier transforms are frames with maximal robustness to erasures as well as for Erdo{combining double acute accent}s-Rényi graphs, random sampling leads to perfect recovery with high probability. We further establish the connection to the sampling theory of finite discrete-time signal processing and previous work on signal recovery on graphs. To handle full-band graph signals, we propose a graph filter bank based on sampling theory on graphs. Finally, we apply the proposed sampling theory to semi-supervised classification of online blogs and digit images, where we achieve similar or better performance with fewer labeled samples compared to previous work. © 1991-2012 IEEE. Source


Chen S.,Carnegie Mellon University | Sandryhaila A.,HP Vertica | Kovacevic J.,Carnegie Mellon University
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | Year: 2015

We present a distributed and decentralized algorithm for graph signal inpainting. The previous work obtained a closed-form solution with matrix inversion. In this paper, we ease the computation by using a distributed algorithm, which solves graph signal inpainting by restricting each node to communicate only with its local nodes. We show that the solution of the distributed algorithm converges to the closed-form solution with the corresponding convergence speed. Experiments on online blog classification and temperature prediction suggest that the convergence speed of the proposed distributed algorithm is competitive with that of the centralized algorithm, especially when a graph tends to be regular. Since a distributed algorithm does not require to collect data to a center, it is more practical and efficient. © 2015 IEEE. Source


Pedersen T.B.,University of Aalborg | Castellanos M.,HP Vertica | Dayal U.,Hitachi Labs
SIGMOD Record | Year: 2014

The article reports on the 7th International Workshop on Business Intelligence for the Real Time Enterprise (BIRTE 2013), co-located with the VLDB 2013 conference. The BIRTE workshop series aims at providing a forum for presentation of the latest research results, new technology developments, and new applications in the areas of business intelligence and real time enterprises. The compelling applications discussed included CRM, brand sentiment, predictive maintenance, network optimization, security, fraud detection, text analytics and smart content navigation, the last two in an SAP paper. Major issues are discovering trends early and finding outliers. A new type of applications concern cyber-physical systems producing huge amounts of data and events. One type of cyber-physical system is the emerging smart grid. Source


Prasad S.,HP Vertica | Fard A.,HP Vertica | Gupta V.,HP Vertica | Martine J.,HP Vertica | And 4 more authors.
Proceedings of the ACM SIGMOD International Conference on Management of Data | Year: 2015

A typical predictive analytics workow will pre-process data in a database, transfer the resulting data to an external statistical tool such as R, create machine learning models in R, and then apply the model on newly arriving data. Today, this workow is slow and cumbersome. Extracting data from databases, using ODBC connectors, can take hours on multi-gigabyte datasets. Building models on single-threaded R does not scale. Finally, it is nearly impossible to use R or other common tools, to apply models on terabytes of newly arriving data. We solve all the above challenges by integrating HP Vertica with Distributed R, a distributed framework for R. This paper presents the design of a high performance data transfer mechanism, new data-structures in Distributed R to maintain data locality with database table segments, and extensions to Vertica for saving and deploying R models. Our experiments show that data transfers from Vertica are 6× faster than using ODBC connections. Even complex predictive analysis on 100s of gigabytes of database tables can complete in minutes, and is as fast as in-memory systems like Spark running directly on a distributed file system. Source

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