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D4M and large array databases for management and analysis of large biomedical imaging data

Summary

Advances in medical imaging technologies have enabled the acquisition of increasingly large datasets. Current state-of-the-art confocal or multi-photon imaging technology can produce biomedical datasets in excess of 1 TB per dataset. Typical approaches for analyzing large datasets rely on downsampling the original datasets or leveraging distributed computing resources where small subsets of images are processed independently. These approaches require significant overhead on the part of the programmer to load the desired sub-volume from an array of image files into memory. Databases are well suited for indexing and retrieving components of very large datasets and show significant promise for the analysis of 3D volumetric images. In particular, array-based databases such as SciDB utilize an architecture that supports massive parallel processing while also providing database services such as data management and fast parallel queries. In this paper, we will present a new set of tools that leverage the D4M (Dynamic Distributed Dimensional Data Model) toolbox for analyzing giga-voxel biomedical datasets. By combining SciDB and the D4M toolbox, we demonstrate that we can access large volumetric data and perform large-scale bioinformatics analytics efficiently and interactively. We show that it is possible to achieve an ingest rate of 2.8 million entries per second for importing large datasets into SciDB. These tools provide more efficient ways to access random sub-volumes of massive datasets and to process the information that typically cannot be loaded into memory. This work describes the D4M and SciDB tools that we developed and presents the initial performance results.
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Summary

Advances in medical imaging technologies have enabled the acquisition of increasingly large datasets. Current state-of-the-art confocal or multi-photon imaging technology can produce biomedical datasets in excess of 1 TB per dataset. Typical approaches for analyzing large datasets rely on downsampling the original datasets or leveraging distributed computing resources where small...

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Recommender systems for the Department of Defense and intelligence community

Summary

Recommender systems, which selectively filter information for users, can hasten analysts' responses to complex events such as cyber attacks. Lincoln Laboratory's research on recommender systems may bring the capabilities of these systems to analysts in both the Department of Defense and intelligence community.
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Summary

Recommender systems, which selectively filter information for users, can hasten analysts' responses to complex events such as cyber attacks. Lincoln Laboratory's research on recommender systems may bring the capabilities of these systems to analysts in both the Department of Defense and intelligence community.

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Recommender systems for the Department of Defense and intelligence community

Summary

Recommender systems, which selectively filter information for users, can hasten analysts' responses to complex events such as cyber attacks. Lincoln Laboratory's research on recommender systems may bring the capabilities of these systems to analysts in both the Department of Defense and intelligence community.
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Summary

Recommender systems, which selectively filter information for users, can hasten analysts' responses to complex events such as cyber attacks. Lincoln Laboratory's research on recommender systems may bring the capabilities of these systems to analysts in both the Department of Defense and intelligence community.

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Secure and resilient cloud computing for the Department of Defense

Summary

Cloud computing offers substantial benefits to its users: the ability to store and access massive amounts of data, on-demand delivery of computing services, the capability to widely share information, and the scalability of resource usage. Lincoln Laboratory is developing technology that will strengthen the security and resilience of cloud computing so that the Department of Defense can confidently deploy cloud services for its critical missions.
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Summary

Cloud computing offers substantial benefits to its users: the ability to store and access massive amounts of data, on-demand delivery of computing services, the capability to widely share information, and the scalability of resource usage. Lincoln Laboratory is developing technology that will strengthen the security and resilience of cloud computing...

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Scalability of VM provisioning systems

Summary

Virtual machines and virtualized hardware have been around for over half a century. The commoditization of the x86 platform and its rapidly growing hardware capabilities have led to recent exponential growth in the use of virtualization both in the enterprise and high performance computing (HPC). The startup time of a virtualized environment is a key performance metric for high performance computing in which the runtime of any individual task is typically much shorter than the lifetime of a virtualized service in an enterprise context. In this paper, a methodology for accurately measuring the startup performance on an HPC system is described. The startup performance overhead of three of the most mature, widely deployed cloud management frameworks (OpenStack, OpenNebula, and Eucalyptus) is measured to determine their suitability for workloads typically seen in an HPC environment. A 10x performance difference is observed between the fastest (Eucalyptus) and the slowest (OpenNebula) framework. This time difference is primarily due to delays in waiting on networking in the cloud-init portion of the startup. The methodology and measurements presented should facilitate the optimization of startup across a variety of virtualization environments.
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Summary

Virtual machines and virtualized hardware have been around for over half a century. The commoditization of the x86 platform and its rapidly growing hardware capabilities have led to recent exponential growth in the use of virtualization both in the enterprise and high performance computing (HPC). The startup time of a...

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Sampling operations on big data

Published in:
2015 Asilomar Conf. on Signals, Systems and Computers, 8-11 November 2015.

Summary

The 3Vs -- Volume, Velocity and Variety -- of Big Data continues to be a large challenge for systems and algorithms designed to store, process and disseminate information for discovery and exploration under real-time constraints. Common signal processing operations such as sampling and filtering, which have been used for decades to compress signals are often undefined in data that is characterized by heterogeneity, high dimensionality, and lack of known structure. In this article, we describe and demonstrate an approach to sample large datasets such as social media data. We evaluate the effect of sampling on a common predictive analytic: link prediction. Our results indicate that greatly sampling a dataset can still yield meaningful link prediction results.
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Summary

The 3Vs -- Volume, Velocity and Variety -- of Big Data continues to be a large challenge for systems and algorithms designed to store, process and disseminate information for discovery and exploration under real-time constraints. Common signal processing operations such as sampling and filtering, which have been used for decades...

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Percolation model of insider threats to assess the optimum number of rules

Published in:
Environ. Syst. Decis., Vol. 35, 2015, pp. 504-10.

Summary

Rules, regulations, and policies are the basis of civilized society and are used to coordinate the activities of individuals who have a variety of goals and purposes. History has taught that over-regulation (too many rules) makes it difficult to compete and under-regulation (too few rules) can lead to crisis. This implies an optimal number of rules that avoids these two extremes. Rules create boundaries that define the latitude at which an individual has to perform their activities. This paper creates a Toy Model of a work environment and examines it with respect to the latitude provided to a normal individual and the latitude provided to an insider threat. Simulations with the Toy Model illustrate four regimes with respect to an insider threat: under-regulated, possibly optimal, tipping point, and over-regulated. These regimes depend upon the number of rules (N) and the minimum latitude (Lmin) required by a normal individual to carry out their activities. The Toy Model is then mapped onto the standard 1D Percolation Model from theoretical physics, and the same behavior is observed. This allows the Toy Model to be generalized to a wide array of more complex models that have been well studied by the theoretical physics community and also show the same behavior. Finally, by estimating N and Lmin, it should be possible to determine the regime of any particular environment.
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Summary

Rules, regulations, and policies are the basis of civilized society and are used to coordinate the activities of individuals who have a variety of goals and purposes. History has taught that over-regulation (too many rules) makes it difficult to compete and under-regulation (too few rules) can lead to crisis. This...

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Sampling large graphs for anticipatory analytics

Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.

Summary

The characteristics of Big Data - often dubbed the 3V's for volume, velocity, and variety - will continue to outpace the ability of computational systems to process, store, and transmit meaningful results. Traditional techniques for dealing with large datasets often include the purchase of larger systems, greater human-in-the-loop involvement, or more complex algorithms. We are investigating the use of sampling to mitigate these challenges, specifically sampling large graphs. Often, large datasets can be represented as graphs where data entries may be edges, and vertices may be attributes of the data. In particular, we present the results of sampling for the task of link prediction. Link prediction is a process to estimate the probability of a new edge forming between two vertices of a graph, and it has numerous application areas in understanding social or biological networks. In this paper we propose a series of techniques for the sampling of large datasets. In order to quantify the effect of these techniques, we present the quality of link prediction tasks on sampled graphs, and the time saved in calculating link prediction statistics on these sampled graphs.
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Summary

The characteristics of Big Data - often dubbed the 3V's for volume, velocity, and variety - will continue to outpace the ability of computational systems to process, store, and transmit meaningful results. Traditional techniques for dealing with large datasets often include the purchase of larger systems, greater human-in-the-loop involvement, or...

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Improving big data visual analytics with interactive virtual reality

Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.

Summary

For decades, the growth and volume of digital data collection has made it challenging to digest large volumes of information and extract underlying structure. Coined 'Big Data', massive amounts of information has quite often been gathered inconsistently (e.g from many sources, of various forms, at different rates, etc.). These factors impede the practices of not only processing data, but also analyzing and displaying it in an efficient manner to the user. Many efforts have been completed in the data mining and visual analytics community to create effective ways to further improve analysis and achieve the knowledge desired for better understanding. Our approach for improved big data visual analytics is two-fold, focusing on both visualization and interaction. Given geo-tagged information, we are exploring the benefits of visualizing datasets in the original geospatial domain by utilizing a virtual reality platform. After running proven analytics on the data, we intend to represent the information in a more realistic 3D setting, where analysts can achieve an enhanced situational awareness and rely on familiar perceptions to draw in-depth conclusions on the dataset. In addition, developing a human-computer interface that responds to natural user actions and inputs creates a more intuitive environment. Tasks can be performed to manipulate the dataset and allow users to dive deeper upon request, adhering to desired demands and intentions. Due to the volume and popularity of social media, we developed a 3D tool visualizing Twitter on MIT's campus for analysis. Utilizing emerging technologies of today to create a fully immersive tool that promotes visualization and interaction can help ease the process of understanding and representing big data.
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Summary

For decades, the growth and volume of digital data collection has made it challenging to digest large volumes of information and extract underlying structure. Coined 'Big Data', massive amounts of information has quite often been gathered inconsistently (e.g from many sources, of various forms, at different rates, etc.). These factors...

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Enabling on-demand database computing with MIT SuperCloud database management system

Summary

The MIT SuperCloud database management system allows for rapid creation and flexible execution of a variety of the latest scientific databases, including Apache Accumulo and SciDB. It is designed to permit these databases to run on a High Performance Computing Cluster (HPCC) platform as seamlessly as any other HPCC job. It ensures the seamless migration of the databases to the resources assigned by the HPCC scheduler and centralized storage of the database files when not running. It also permits snapshotting of databases to allow researchers to experiment and push the limits of the technology without concerns for data or productivity loss if the database becomes unstable.
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Summary

The MIT SuperCloud database management system allows for rapid creation and flexible execution of a variety of the latest scientific databases, including Apache Accumulo and SciDB. It is designed to permit these databases to run on a High Performance Computing Cluster (HPCC) platform as seamlessly as any other HPCC job...

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