Publications
Big Data dimensional analysis
September 9, 2014
Conference Paper
Published in:
HPEC 2014: IEEE Conf. on High Performance Extreme Computing, 9-11 September 2014.
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Summary
The ability to collect and analyze large amounts of data is a growing problem within the scientific community. The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. One of the main challenges associated with big data variety is automatically understanding the underlying structures and patterns of the data. Such an understanding is required as a pre-requisite to the application of advanced analytics to the data. Further, big data sets often contain anomalies and errors that are difficult to know a priori. Current approaches to understanding data structure are drawn from the traditional database ontology design. These approaches are effective, but often require too much human involvement to be effective for the volume, velocity and variety of data encountered by big data systems. Dimensional Data Analysis (DDA) is a proposed technique that allows big data analysts to quickly understand the overall structure of a big dataset, determine anomalies. DDA exploits structures that exist in a wide class of data to quickly determine the nature of the data and its statistical anomalies. DDA leverages existing schemas that are employed in big data databases today. This paper presents DDA, applies it to a number of data sets, and measures its performance. The overhead of DDA is low and can be applied to existing big data systems without greatly impacting their computing requirements.
Summary
The ability to collect and analyze large amounts of data is a growing problem within the scientific community. The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. One of the main challenges associated with big data...
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A test-suite generator for database systems
September 9, 2014
Conference Paper
Published in:
HPEC 2014: IEEE Conf. on High Performance Extreme Computing, 9-11 September 2014.
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Summary
In this paper, we describe the SPAR Test Suite Generator (STSG), a new test-suite generator for SQL style database systems. This tool produced an entire test suite (data, queries, and ground-truth answers) as a unit and in response to a user's specification. Thus, database evaluators could use this tool to craft test suites for particular aspects of a specific database system. The inclusion of ground-truth answers in the produced test suite, furthermore, allowed this tool to support both benchmarking (at various scales) and correctness-checking in a repeatable way. Lastly, the test-suite generator of this document was extensively profiled and optimized, and was designed for test-time agility.
Summary
In this paper, we describe the SPAR Test Suite Generator (STSG), a new test-suite generator for SQL style database systems. This tool produced an entire test suite (data, queries, and ground-truth answers) as a unit and in response to a user's specification. Thus, database evaluators could use this tool to...
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Sparse matrix partitioning for parallel eigenanalysis of large static and dynamic graphs
September 9, 2014
Conference Paper
Published in:
HPEC 2014: IEEE Conf. on High Performance Extreme Computing, 9-11 September 2014.
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Summary
Numerous applications focus on the analysis of entities and the connections between them, and such data are naturally represented as graphs. In particular, the detection of a small subset of vertices with anomalous coordinated connectivity is of broad interest, for problems such as detecting strange traffic in a computer network or unknown communities in a social network. These problems become more difficult as the background graph grows larger and noisier and the coordination patterns become more subtle. In this paper, we discuss the computational challenges of a statistical framework designed to address this cross-mission challenge. The statistical framework is based on spectral analysis of the graph data, and three partitioning methods are evaluated for computing the principal eigenvector of the graph's residuals matrix. While a standard one-dimensional partitioning technique enables this computation for up to four billion vertices, the communication overhead prevents this method from being used for even larger graphs. Recent two-dimensional partitioning methods are shown to have much more favorable scaling properties. A data-dependent partitioning method, which has the best scaling performance, is also shown to improve computation time even as a graph changes over time, allowing amortization of the upfront cost.
Summary
Numerous applications focus on the analysis of entities and the connections between them, and such data are naturally represented as graphs. In particular, the detection of a small subset of vertices with anomalous coordinated connectivity is of broad interest, for problems such as detecting strange traffic in a computer network...
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Computing on masked data: a high performance method for improving big data veracity
September 9, 2014
Conference Paper
Published in:
HPEC 2014: IEEE Conf. on High Performance Extreme Computing, 9-11 September 2014.
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Summary
The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. Along with these standard three V's of big data, an emerging fourth "V" is veracity, which addresses the confidentiality, integrity, and availability of the data. Traditional cryptographic techniques that ensure the veracity of data can have overheads that are too large to apply to big data. This work introduces a new technique called Computing on Masked Data (CMD), which improves data veracity by allowing computations to be performed directly on masked data and ensuring that only authorized recipients can unmask the data. Using the sparse linear algebra of associative arrays, CMD can be performed with significantly less overhead than other approaches while still supporting a wide range of linear algebraic operations on the masked data. Databases with strong support of sparse operations, such as SciDB or Apache Accumulo, are ideally suited to this technique. Examples are shown for the application of CMD to a complex DNA matching algorithm and to database operations over social media data.
Summary
The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. Along with these standard three V's of big data, an emerging fourth "V" is veracity, which addresses the confidentiality, integrity, and availability of the data. Traditional cryptographic...
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Genetic sequence matching using D4M big data approaches
September 9, 2014
Conference Paper
Published in:
HPEC 2014: IEEE Conf. on High Performance Extreme Computing, 9-11 September 2014.
Summary
Recent technological advances in Next Generation Sequencing tools have led to increasing speeds of DNA sample collection, preparation, and sequencing. One instrument can produce over 600 Gb of genetic sequence data in a single run. This creates new opportunities to efficiently handle the increasing workload. We propose a new method of fast genetic sequence analysis using the Dynamic Distributed Dimensional Data Model (D4M) - an associative array environment for MATLAB developed at MIT Lincoln Laboratory. Based on mathematical and statistical properties, the method leverages big data techniques and the implementation of an Apache Acculumo database to accelerate computations one-hundred fold over other methods. Comparisons of the D4M method with the current gold-standard for sequence analysis, BLAST, show the two are comparable in the alignments they find. This paper will present an overview of the D4M genetic sequence algorithm and statistical comparisons with BLAST.
Summary
Recent technological advances in Next Generation Sequencing tools have led to increasing speeds of DNA sample collection, preparation, and sequencing. One instrument can produce over 600 Gb of genetic sequence data in a single run. This creates new opportunities to efficiently handle the increasing workload. We propose a new method...
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Adaptive optics program at TMT
September 5, 2014
Conference Paper
Published in:
SPIE, Vol. 9148, Adaptive Optics Systems IV, 5 September 2014, 914801.
Summary
The TMT first light Adaptive Optics (AO) facility consists of the Narrow Field Infra-Red AO System (NFIRAOS) and the associated Laser Guide Star Facility (LGSF). NFIRAOS is a 60 x 60 laser guide star (LGS) multi-conjugate AO (MCAO) system, which provides uniform, diffraction-limited performance in the J, H, and K bands over 17-30 arc sec diameter fields with 50 per cent sky coverage at the galactive pole, as required to support the TMT science cases. NFIRAOS includes two deformable mirrors, six laser guide star wavefront sensors, and three low-order, infrared, natural guide star wavefront sensors within each client instument. The first light LGSF system includes six sodium lasers required to generate the NFIRAOS laser guide stars. In this paper, we will provide an update on the progress in designing, modeling, and validating the TMT first light AO systems and their components over the last two years. This will include pre-final design and prototyping for the deformable mirrors, fabrication and tests for the visible detectors, benchmarking and comparison of different algorithms and processing architecture for the Real Time Controller (RTC) and development tests of prototype candidate lasers. Comprehensive and detailed AO modeling is continuing to support the design and development of the first light AO facility. Main modeling topics studied during the last two years include further studies in the area of wavefront error budget, sky coverage, high precision astrometry for the galactic center and other observations, high contrast imaging with NFIRAOS and its first light instruments, Point Spread Function (PSF) reconstruction for LGS MCAO, LGS photon return and sophisticated low order mode temporal filtering.
Summary
The TMT first light Adaptive Optics (AO) facility consists of the Narrow Field Infra-Red AO System (NFIRAOS) and the associated Laser Guide Star Facility (LGSF). NFIRAOS is a 60 x 60 laser guide star (LGS) multi-conjugate AO (MCAO) system, which provides uniform, diffraction-limited performance in the J, H, and K...
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Detecting small asteroids with the Space Surveillance Telescope
September 1, 2014
Journal Article
Published in:
Icarus, Vol. 239, 2014, pp. 253-9.
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Summary
The ability of the Space Surveillance Telescope (SST) to find small (2-15 m diameter) NEAs suitable for the NASA asteroid retrieval mission is investigated. Orbits from a simulated population of targetable small asteroids were propagated and observations with the SST were simulated. Different search patterns and telescope time allocation cases were considered, as well as losses due to FOV gaps and weather. It is concluded that a full-time, dedicated survey at the SST is likely necessary to find a useful population of these NEAs within the mission launch timeframe, especially if an object must be observed on >1 night at SST to qualify as a detection. The simulations were also performed for an identical telescope in the southern hemisphere, which is found to produce results very similar to the SST in New Mexico due to significant (~80%) overlap in the population of objects detected at each site. In addition to considering the SST's ability to detect small NEAs, a parallel study was performed focusing on >100 m diameter objects. This work shows that even with limited telescope time (3 nights per month) a substantial number of these larger objects would be detected.
Summary
The ability of the Space Surveillance Telescope (SST) to find small (2-15 m diameter) NEAs suitable for the NASA asteroid retrieval mission is investigated. Orbits from a simulated population of targetable small asteroids were propagated and observations with the SST were simulated. Different search patterns and telescope time allocation cases...
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Comparisons between the extended Kalman filter and the state-dependent Riccati estimator
September 1, 2014
Journal Article
Published in:
J. Guid. Control Dyn., Vol. 37, No. 5, September-October 2014, pp. 1556-67.
Summary
The state-dependent Riccati equation-based estimator is becoming a popular estimation tool for nonlinear systems since it does not use system linearization. In this paper, the state-dependent Riccati equation-based estimator is compared with the widely used extended Kalman filter for three simple examples that appear in the open literature. It is demonstrated that, by simulation, the state-dependent Riccati equation-based estimator at best has comparable results to the extended Kalman filter but is often worse than the extended Kalman filter. In some cases, the state-dependent Riccati equation-based estimator does not converge, even though the system considered satisfies all the mathematical constraints on controllability and observability. Sufficient detail is presented in the paper so that the interested reader cannot only duplicate the results but perhaps make suggestions on how to get the state-dependent Riccati equation-based estimator to perform better.
Summary
The state-dependent Riccati equation-based estimator is becoming a popular estimation tool for nonlinear systems since it does not use system linearization. In this paper, the state-dependent Riccati equation-based estimator is compared with the widely used extended Kalman filter for three simple examples that appear in the open literature. It is...
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VizLinc: integrating information extraction, search, graph analysis, and geo-location for the visual exploration of large data sets
August 24, 2014
Conference Paper
Published in:
Proc. KDD 2014 Workshop on Interactive Data Exploration and Analytics, IDEA, 24 August 2014, pp. 10-18.
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In this demo paper we introduce VizLinc; an open-source software suite that integrates automatic information extraction, search, graph analysis, and geo-location for interactive visualization and exploration of large data sets. VizLinc helps users in: 1) understanding the type of information the data set under study might contain, 2) finding patterns and connections between entities, and 3) narrowing down the corpus to a small fraction of relevant documents that users can quickly read. We apply the tools offered by VizLinc to a subset of the New York Times Annotated Corpus and present use cases that demonstrate VizLinc's search and visualization features.
Summary
In this demo paper we introduce VizLinc; an open-source software suite that integrates automatic information extraction, search, graph analysis, and geo-location for interactive visualization and exploration of large data sets. VizLinc helps users in: 1) understanding the type of information the data set under study might contain, 2) finding patterns...
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Content+context=classification: examining the roles of social interactions and linguist content in Twitter user classification
August 24, 2014
Conference Paper
Published in:
Proc. Second Workshop on Natural Language Processing for Social Media, SocialNLP, 24 August 2014, pp. 59-65.
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Twitter users demonstrate many characteristics via their online presence. Connections, community memberships, and communication patterns reveal both idiosyncratic and general properties of users. In addition, the content of tweets can be critical for distinguishing the role and importance of a user. In this work, we explore Twitter user classification using context and content cues. We construct a rich graph structure induced by hashtags and social communications in Twitter. We derive features from this graph structure - centrality, communities, and local flow of information. In addition, we perform detailed content analysis on tweets looking at offensiveness and topics. We then examine user classification and the role of feature types (context, content) and learning methods (propositional, relational) through a series of experiments on annotated data. Our work contrasts with prior approaches in that we use relational learning and alternative, non-specialized feature sets. Our goal is to understand how both content and context are predictive of user characteristics. Experiments demonstrate that the best performance for user classification uses relational learning with varying content and context features.
Summary
Twitter users demonstrate many characteristics via their online presence. Connections, community memberships, and communication patterns reveal both idiosyncratic and general properties of users. In addition, the content of tweets can be critical for distinguishing the role and importance of a user. In this work, we explore Twitter user classification using...
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