منوی کاربری
  • پشتیبانی: ۴۲۲۷۳۷۸۱ - ۰۴۱
  • سبد خرید

دانلود رایگان مقاله شناسایی عملی معیارهای اولویت پویا به نتایج انتقادی تولید

عنوان فارسی
شناسایی عملی معیارهای اولویت پویا به نتایج انتقادی تولید از جریانات کلان داده
عنوان انگلیسی
Practical Identification of Dynamic Precedence Criteria to Produce Critical Results from Big Data Streams
صفحات مقاله فارسی
0
صفحات مقاله انگلیسی
18
سال انتشار
20145
نشریه
الزویر - Elsevier
فرمت مقاله انگلیسی
PDF
کد محصول
E424
رشته های مرتبط با این مقاله
مهندسی کامپیوتر
گرایش های مرتبط با این مقاله
مهندسی نرم افزار و معماری سیستم های کامپیوتری
مجله
تحقیقات کلان داده - Big Data Research
دانشگاه
دانشگاه ایالتی وستفیلد، USA
کلمات کلیدی
جریانات کلان داده، تولید نتیجه انتقادی، انطباق آنلاین سریع
۰.۰ (بدون امتیاز)
امتیاز دهید
چکیده

Abstract


During periods of high volume, big data stream applications may not have enough resources to process all incoming tuples. To maximize the production of the most critical results under such resource shortages, a recent solution, PR (short for Preferential Result), utilizes both static criteria (defined at compile-time) and dynamic criteria (identified online at run-time) to prioritize the processing of tuples throughout the query pipeline. Unfortunately, locating the optimal criteria placement (i.e., where in the query pipeline to evaluate each prioritization criteria) is extremely compute-intensive and runs in exponential time. This makes PR impractical for complex big data stream systems. Our proposed criteria selection and placement approach, PR-Prune (short for Preferential Result-Pruning), is practical. PR-Prune prunes ineffective dynamic criteria and combines multiple criteria along the same pipeline. To achieve this, PR-Prune seeks to expand the duration in the query pipeline that tuples identified as critical are pulled forward. Our experiments use a real data stream from the S&P 500 stocks, synthetic data streams, and a diverse set of queries. The results substantiate that PR-Prune increases the production of the most critical results compared to the state-of-the-art approaches. In addition, PR-Prune significantly lowers the optimization search time compared to PR.

نتیجه گیری

6. Conclusions


Our innovative preferential resource allocation optimization strategy, PR-Prune, efficiently locates online dynamic criteria that are central for the production of critical query results by pruning ineffective dynamic criteria and combining multiple criteria along the same pipeline. Our experimental study confirms that for applications where priority resource allocation matters and promising tuples exist, PR-Prune consistently increases the throughput of the most critical query results compared to the state-of-the-art approaches. In addition, our experimental study confirms that the optimization search time of PR-Prune is significantly lower than its closest competitor, namely, PR.


بدون دیدگاه