標(biāo)題: Titlebook: Big Data Benchmarking; 6th International Wo Tilmann Rabl,Raghunath Nambiar,Saumyadipta Pyne Conference proceedings 2016 Springer Internatio [打印本頁(yè)] 作者: Recovery 時(shí)間: 2025-3-21 17:06
書目名稱Big Data Benchmarking影響因子(影響力)
書目名稱Big Data Benchmarking影響因子(影響力)學(xué)科排名
書目名稱Big Data Benchmarking網(wǎng)絡(luò)公開度
書目名稱Big Data Benchmarking網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Big Data Benchmarking被引頻次
書目名稱Big Data Benchmarking被引頻次學(xué)科排名
書目名稱Big Data Benchmarking年度引用
書目名稱Big Data Benchmarking年度引用學(xué)科排名
書目名稱Big Data Benchmarking讀者反饋
書目名稱Big Data Benchmarking讀者反饋學(xué)科排名
作者: 過(guò)濾 時(shí)間: 2025-3-21 23:47 作者: NADIR 時(shí)間: 2025-3-22 03:52 作者: 食草 時(shí)間: 2025-3-22 05:28
Conference proceedings 2016n Toronto, ON, Canada, in June 2015 and the 7th International Workshop, WBDB 2015, held in New Delhi, India, in December 2015. ..The 8 full papers presented in this book were carefully reviewed and selected from 22 submissions. They deal with recent trends in big data and HPC convergence, new propos作者: 沙草紙 時(shí)間: 2025-3-22 11:08 作者: humectant 時(shí)間: 2025-3-22 15:17
0302-9743 on Big Data Benchmarking, WBDB 2015, held in Toronto, ON, Canada, in June 2015 and the 7th International Workshop, WBDB 2015, held in New Delhi, India, in December 2015. ..The 8 full papers presented in this book were carefully reviewed and selected from 22 submissions. They deal with recent trends 作者: anchor 時(shí)間: 2025-3-22 20:55
Asymptotically Autonomous Vector Fields,ve ones. Our experiments show that: (1) for both Hive and Spark SQL, BigBench queries perform with the increase of the data size on average better than the linear scaling behavior and (2) pure HiveQL queries perform faster on Spark SQL than on Hive.作者: Pedagogy 時(shí)間: 2025-3-22 23:46
,The Poincaré-Bendixson Theorem,n keeping our collaboration with Apache Open Source Community to work on performance tuning and optimization for Hadoop ecosystem. In this paper, we share our contributions to BigBench, and present our tuning and optimization experience along with the benchmark results.作者: 思想上升 時(shí)間: 2025-3-23 02:29
Performance Evaluation of Spark SQL Using BigBenchve ones. Our experiments show that: (1) for both Hive and Spark SQL, BigBench queries perform with the increase of the data size on average better than the linear scaling behavior and (2) pure HiveQL queries perform faster on Spark SQL than on Hive.作者: Ruptured-Disk 時(shí)間: 2025-3-23 08:42 作者: 閃光東本 時(shí)間: 2025-3-23 09:43 作者: 無(wú)效 時(shí)間: 2025-3-23 15:48
Vector Fields Possessing an Integral, and data rate and size distributions, based on real observations. We also validate this benchmark on Apache Storm using synthetic streams and simulated application logic. This paper offers a unique glimpse into an . national identity infrastructure, and proposes a benchmark for “fast data” platforms to support such eGovernance applications.作者: nuclear-tests 時(shí)間: 2025-3-23 19:01
Asymptotically Autonomous Vector Fields,e produced regardless of any specific architecture and tuning. We apply this benchmark, which is available in the public domain, to three main proponents: rasdaman, SciQL, and SciDB. We present the benchmark and its design rationales, show the benchmark results, and comment on them.作者: 長(zhǎng)矛 時(shí)間: 2025-3-24 02:05
,The Poincaré-Bendixson Theorem,for different workloads. Our results show that . is well suited to achieve high availability while preserving table response times in case of a node failure. Especially for read intensive applications that require high availability, . is a good choice.作者: Cardiac-Output 時(shí)間: 2025-3-24 02:49 作者: 偽造者 時(shí)間: 2025-3-24 09:16 作者: 清真寺 時(shí)間: 2025-3-24 12:06
Towards a General Array Database Benchmark: Measuring Storage Accesse produced regardless of any specific architecture and tuning. We apply this benchmark, which is available in the public domain, to three main proponents: rasdaman, SciQL, and SciDB. We present the benchmark and its design rationales, show the benchmark results, and comment on them.作者: SEED 時(shí)間: 2025-3-24 17:50 作者: Graves’-disease 時(shí)間: 2025-3-24 22:34 作者: 就職 時(shí)間: 2025-3-24 23:34
Benchmarking Fast-Data Platforms for the , Biometric DatabaseIndia. . processes streams of biometric data as residents are enrolled and updated. Besides .1 million enrollments and updates per day, up?to 100?million daily biometric authentications are expected during delivery of various public services. These form critical Big Data applications, with large vol作者: Anemia 時(shí)間: 2025-3-25 03:22 作者: 精美食品 時(shí)間: 2025-3-25 10:28
ALOJA: A Benchmarking and Predictive Platform for Big Data Performance Analysisnts. The development of the project over its first year, has resulted in a open source benchmarking platform, an online public repository of results with over 42,000 Hadoop job runs, and web-based analytic tools to gather insights about system’s cost-performance (ALOJA’s Web application, tools, and 作者: Enteropathic 時(shí)間: 2025-3-25 13:54 作者: 透明 時(shí)間: 2025-3-25 17:13 作者: GORGE 時(shí)間: 2025-3-25 20:39
Accelerating BigBench on Hadoop To solve this issue, a new towards industry standard benchmark for Big Data Analytics called BigBench has been proposed. And with BigBench, we’ve been keeping our collaboration with Apache Open Source Community to work on performance tuning and optimization for Hadoop ecosystem. In this paper, we s作者: ARBOR 時(shí)間: 2025-3-26 03:22
Tilmann Rabl,Raghunath Nambiar,Saumyadipta PyneIncludes supplementary material: 作者: 同音 時(shí)間: 2025-3-26 06:48 作者: Indecisive 時(shí)間: 2025-3-26 12:12
Asymptotically Autonomous Vector Fields,big data with an accompanying cloud infrastructure of dramatic and increasing size and sophistication. In this paper, we study an approach to convergence for software and applications/algorithms and show what hardware architectures it suggests. We start by dividing applications into data plus model 作者: 容易做 時(shí)間: 2025-3-26 13:13 作者: 易彎曲 時(shí)間: 2025-3-26 19:18 作者: Adenocarcinoma 時(shí)間: 2025-3-26 23:26 作者: rectum 時(shí)間: 2025-3-27 03:03
,The Poincaré-Bendixson Theorem,each additional machine in a cluster, the likelihood for hardware failure increases. In order to still achieve high availability and fault tolerance, the data needs to be replicated within the cluster. Predictable and stable response times are required by many applications even in the case of a node作者: SOB 時(shí)間: 2025-3-27 08:05 作者: arrhythmic 時(shí)間: 2025-3-27 09:26 作者: ARCHE 時(shí)間: 2025-3-27 14:56
Big Data Benchmarking978-3-319-49748-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: conduct 時(shí)間: 2025-3-27 18:21 作者: intricacy 時(shí)間: 2025-3-27 23:22
