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Detection theory for graphs

January 30, 2013
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Journal Article
Author:
Benjamin A. Miller
…
Published in:
Lincoln Laboratory Journal, Vol. 20, No. 1, 2013, pp. 10-30.
Topic:
signal processing
R&D area:
R&D group:

Summary

Graphs are fast emerging as a common data structure used in many scientific and engineering fields. While a wide variety of techniques exist to analyze graph datasets, practitioners currently lack a signal processing theory akin to that of detection and estimation in the classical setting of vector spaces with Gaussian noise. Using practical detection examples involving large, random "background" graphs and noisy real-world datasets, the authors present a novel graph analytics framework that allows for uncued analysis of very large datasets. This framework combines traditional computer science techniques with signal processing in the context of graph data, creating a new research area at the intersection of the two fields.
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Summary

Graphs are fast emerging as a common data structure used in many scientific and engineering fields. While a wide variety of techniques exist to analyze graph datasets, practitioners currently lack a signal processing theory akin to that of detection and estimation in the classical setting of vector spaces with Gaussian...

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Detection theory for graphs

Dynamic Distributed Dimensional Data Model (D4M) database and computation system

March 25, 2012
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Conference Paper
Author:
Jeremy Kepner
…
Published in:
ICASSP 2012, Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 25-30 March 2012, pp. 5349-52.
Topic:
big data
R&D area:
R&D group:

Summary

A crucial element of large web companies is their ability to collect and analyze massive amounts of data. Tuple store databases are a key enabling technology employed by many of these companies (e.g., Google Big Table and Amazon Dynamo). Tuple stores are highly scalable and run on commodity clusters, but lack interfaces to support efficient development of mathematically based analytics. D4M (Dynamic Distributed Dimensional Data Model) has been developed to provide a mathematically rich interface to tuple stores (and structured query language "SQL" databases). D4M allows linear algebra to be readily applied to databases. Using D4M, it is possible to create composable analytics with significantly less effort than using traditional approaches. This work describes the D4M technology and its application and performance.
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Summary

A crucial element of large web companies is their ability to collect and analyze massive amounts of data. Tuple store databases are a key enabling technology employed by many of these companies (e.g., Google Big Table and Amazon Dynamo). Tuple stores are highly scalable and run on commodity clusters, but...

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Dynamic Distributed Dimensional Data Model (D4M) database and computation system

Discrete optimization using decision-directed learning for distributed networked computing

January 1, 2002
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Conference Paper
Author:
Joel I. Goodman
…
Published in:
36th Asilomar Conf. on Signals, Systems and Computers, Vol. 2, 3-6 November 2002, pp. 1189-1196.
Topic:
high performance computing
R&D area:
R&D group:

Summary

Decision-directed learning (DDL) is an iterative discrete approach to finding a feasible solution for large-scale combinatorial optimization problems. DDL is capable of efficiently formulating a solution to network scheduling problems that involve load limiting device utilization, selecting parallel configurations for software applications and host hardware using a minimum set of resources, and meeting time-to-result performance requirements in a dynamic network environment. This paper quantifies the algorithms that constitute DDL and compares its performance to other popular combinatorial self-directed real-time networked resource configuration for dynamically building a mission specific signal-processor for real-time distributed and parallel applications.
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Summary

Decision-directed learning (DDL) is an iterative discrete approach to finding a feasible solution for large-scale combinatorial optimization problems. DDL is capable of efficiently formulating a solution to network scheduling problems that involve load limiting device utilization, selecting parallel configurations for software applications and host hardware using a minimum set of...

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Discrete optimization using decision-directed learning for distributed networked computing

ITWS microburst prediction algorithm performance, capabilities, and limitations

November 13, 1996
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Conference Paper
Author:
Robert G. Hallowell
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Published in:
Workshop on Wind Shear and Wind Shear Alert Systems,. Oklahoma City, 13-15 November, 1996.
Topic:
algorithms
R&D area:
R&D group:

Summary

Lincoln Laboratory, under funding from the Federal Aviation Administration (FAA) Terminal Doppler Weather Radar program, has developed algorithms for automatically detecting microbursts. While microburst detection algorithms provide highly reliable warnings of microbursts. there still remains a period of time between microburst onset and pilot reaction during which aircraft are at risk. This latency is due to the time needed for the automated algorithms to operate on the radar data, for air traffic controllers to relay any warnings and for pilots to react to the warnings. Lincoln Laboratory research and development has yielded an algorithm for accurately predicting when microburst outflows will occur. The Microburst Prediction Algorithm is part of a suite of weather detection algorithms within the Integrated Terminal Weather System. This paper details the performance of the Microburst Prediction Algorithm over a wide range of geographical and climatological environments. The paper also discusses the full range of the Microburst Prediction Algorithm's capabilities and limitations in varied weather environments. This paper does not discuss the overall rationale for a prediction algorithm or the detailed methodology used to generate predictions.
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Summary

Lincoln Laboratory, under funding from the Federal Aviation Administration (FAA) Terminal Doppler Weather Radar program, has developed algorithms for automatically detecting microbursts. While microburst detection algorithms provide highly reliable warnings of microbursts. there still remains a period of time between microburst onset and pilot reaction during which aircraft are at...

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ITWS microburst prediction algorithm performance, capabilities, and limitations
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