کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6935282 | 868599 | 2014 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Towards unified secure on- and off-line analytics at scale
ترجمه فارسی عنوان
به سوی تجزیه و تحلیل امن و آفلاین در مقیاس
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
Data scientists have applied various analytic models and techniques to address the oft-cited problems of large volume, high velocity data rates and diversity in semantics. Such approaches have traditionally employed analytic techniques in a streaming or batch processing paradigm. This paper presents CRUCIBLE, a first-in-class framework for the analysis of large-scale datasets that exploits both streaming and batch paradigms in a unified manner. The CRUCIBLE framework includes a domain specific language for describing analyses as a set of communicating sequential processes, a common runtime model for analytic execution in multiple streamed and batch environments, and an approach to automating the management of cell-level security labelling that is applied uniformly across runtimes. This paper shows the applicability of CRUCIBLE to a variety of state-of-the-art analytic environments, and compares a range of runtime models for their scalability and performance against a series of native implementations. The work demonstrates the significant impact of runtime model selection, including improvements of between 2.3Ã and 480Ã between runtime models, with an average performance gap of just 14Ã between CRUCIBLE and a suite of equivalent native implementations.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Parallel Computing - Volume 40, Issue 10, December 2014, Pages 738-753
Journal: Parallel Computing - Volume 40, Issue 10, December 2014, Pages 738-753
نویسندگان
P. Coetzee, M. Leeke, S. Jarvis,