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  1. Proceedings of the 2013 International Workshop on Data-Intensive Scalable Computing Systems (DISCS-2013)
  2. CRUCIBLE: towards unified secure on- and off-line analytics at scale
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SDAFT: a novel scalable data access framework for parallel BLAST
On the core affinity and file upload performance of Hadoop
CRUCIBLE: towards unified secure on- and off-line analytics at scale
Large data and computation in a hazard map workflow using Hadoop and Neteeza architectures
Enhancing both fairness and performance using rate-aware dynamic storage cache partitioning
Novel parallel method for mining frequent patterns on multi-core shared memory systems
A framework for an in-depth comparison of scale-up and scale-out
Design of an active storage cluster file system for DAG workflows
Toward a data scalable solution for facilitating discovery of scientific data resources
BDMPI: conquering BigData with small clusters using MPI

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CRUCIBLE: towards unified secure on- and off-line analytics at scale

Content Provider ACM Digital Library
Author Coetzee, Peter Jarvis, Stephen
Abstract The burgeoning field of data science benefits from the application of a variety of analytic models and techniques to the oft-cited problems of large volume, high velocity data rates, and significant variety in data structure and semantics. Many approaches make use of common analytic techniques in either a streaming or batch processing paradigm. This paper presents progress in developing a framework for the analysis of large-scale datasets using both of these pools of techniques in a unified manner. This includes: (1) a Domain Specific Language (DSL) for describing analyses as a set of Communicating Sequential Processes, fully integrated with the Java type system, including an Integrated Development Environment (IDE) and a compiler which builds idiomatic Java; (2) a runtime model for execution of an analytic in both streaming and batch environments; and (3) a novel approach to automated management of cell-level security labels, applied uniformly across all runtimes. The paper concludes with a demonstration of the successful use of this system with a sample workload developed in (1), and an analysis of the performance characteristics of each of the runtimes described in (2).
Starting Page 43
Ending Page 48
Page Count 6
File Format PDF
ISBN 9781450325066
DOI 10.1145/2534645.2534649
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-11-18
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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