Publications
Sampling large graphs for anticipatory analytics
September 15, 2015
Conference Paper
Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.
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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 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.
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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Using a power law distribution to describe big data
September 15, 2015
Conference Paper
Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.
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Summary
The gap between data production and user ability to access, compute and produce meaningful results calls for tools that address the challenges associated with big data volume, velocity and variety. One of the key hurdles is the inability to methodically remove expected or uninteresting elements from large data sets. This difficulty often wastes valuable researcher and computational time by expending resources on uninteresting parts of data. Social sensors, or sensors which produce data based on human activity, such as Wikipedia, Twitter, and Facebook have an underlying structure which can be thought of as having a Power Law distribution. Such a distribution implies that few nodes generate large amounts of data. In this article, we propose a technique to take an arbitrary dataset and compute a power law distributed background model that bases its parameters on observed statistics. This model can be used to determine the suitability of using a power law or automatically identify high degree nodes for filtering and can be scaled to work with big data.
Summary
The gap between data production and user ability to access, compute and produce meaningful results calls for tools that address the challenges associated with big data volume, velocity and variety. One of the key hurdles is the inability to methodically remove expected or uninteresting elements from large data sets. This...
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Parallel vectorized algebraic AES in MATLAB for rapid prototyping of encrypted sensor processing algorithms and database analytics
September 15, 2015
Conference Paper
Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.
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Summary
The increasing use of networked sensor systems and networked databases has led to an increased interest in incorporating encryption directly into sensor algorithms and database analytics. MATLAB is the dominant tool for rapid prototyping of sensor algorithms and has extensive database analytics capabilities. The advent of high level and high performance Galois Field mathematical environments allows encryption algorithms to be expressed succinctly and efficiently. This work leverages the Galois Field primitives found the MATLAB Communication Toolbox to implement a mode of the Advanced Encrypted Standard (AES) based on first principals mathematics. The resulting implementation requires 100x less code than standard AES implementations and delivers speed that is effective for many design purposes. The parallel version achieves speed comparable to native OpenSSL on a single node and is sufficient for real-time prototyping of many sensor processing algorithms and database analytics.
Summary
The increasing use of networked sensor systems and networked databases has led to an increased interest in incorporating encryption directly into sensor algorithms and database analytics. MATLAB is the dominant tool for rapid prototyping of sensor algorithms and has extensive database analytics capabilities. The advent of high level and high...
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Improving big data visual analytics with interactive virtual reality
September 15, 2015
Conference Paper
Published in:
HPEC 2015: IEEE Conf. on High Performance Extreme Computing, 15-17 September 2015.
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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 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.
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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Computing on Masked Data to improve the security of big data
April 14, 2015
Conference Paper
Published in:
HST 2015, IEEE Int. Conf. on Technologies for Homeland Security, 14-16 April 2015.
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Summary
Organizations that make use of large quantities of information require the ability to store and process data from central locations so that the product can be shared or distributed across a heterogeneous group of users. However, recent events underscore the need for improving the security of data stored in such untrusted servers or databases. Advances in cryptographic techniques and database technologies provide the necessary security functionality but rely on a computational model in which the cloud is used solely for storage and retrieval. Much of big data computation and analytics make use of signal processing fundamentals for computation. As the trend of moving data storage and computation to the cloud increases, homeland security missions should understand the impact of security on key signal processing kernels such as correlation or thresholding. In this article, we propose a tool called Computing on Masked Data (CMD), which combines advances in database technologies and cryptographic tools to provide a low overhead mechanism to offload certain mathematical operations securely to the cloud. This article describes the design and development of the CMD tool.
Summary
Organizations that make use of large quantities of information require the ability to store and process data from central locations so that the product can be shared or distributed across a heterogeneous group of users. However, recent events underscore the need for improving the security of data stored in such...
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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 survey of cryptographic approaches to securing big-data analytics in the cloud
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 demand for cloud computing motivates the need to study the security of data received, stored, processed, and transmitted by a cloud. In this paper, we present a framework for such a study. We introduce a cloud computing model that captures a rich class of big-data use-cases and allows reasoning about relevant threats and security goals. We then survey three cryptographic techniques - homomorphic encryption, verifiable computation, and multi-party computation - that can be used to achieve these goals. We describe the cryptographic techniques in the context of our cloud model and highlight the differences in performance cost associated with each.
Summary
The growing demand for cloud computing motivates the need to study the security of data received, stored, processed, and transmitted by a cloud. In this paper, we present a framework for such a study. We introduce a cloud computing model that captures a rich class of big-data use-cases and allows...
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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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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.
Topic:
R&D area:
R&D group:
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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Achieving 100,000,000 database inserts per second using Accumulo and D4M
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 Apache Accumulo database is an open source relaxed consistency database that is widely used for government applications. Accumulo is designed to deliver high performance on unstructured data such as graphs of network data. This paper tests the performance of Accumulo using data from the Graph500 benchmark. The Dynamic Distributed Dimensional Data Model (D4M) software is used to implement the benchmark on a 216-node cluster running the MIT SuperCloud software stack. A peak performance of over 100,000,000 database inserts per second was achieved which is 100x larger than the highest previously published value for any other database. The performance scales linearly with the number of ingest clients, number of database servers, and data size. The performance was achieved by adapting several supercomputing techniques to this application: distributed arrays, domain decomposition, adaptive load balancing, and single-program-multiple-data programming.
Summary
The Apache Accumulo database is an open source relaxed consistency database that is widely used for government applications. Accumulo is designed to deliver high performance on unstructured data such as graphs of network data. This paper tests the performance of Accumulo using data from the Graph500 benchmark. The Dynamic Distributed...
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