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HybridTune: spatio-temporal Performance Data correlation for Performance Diagnosis of Big Data Systems

Journal of Computer Science & Technology 2019年第6期34卷 1167-1184页

作者： Rui Ren Jiechao Cheng Xi-Wen He Lei Wang Jian-Feng Zhan Wan-Ling Gao Chun-Jie Luo Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Computing National University of SingaporeSingapore 117417Singapore

With tremendous growing interests in Big Data, the performance improvement of Big Data systems becomes more and more important. Among many steps, the first one is to analyze and diagnose performance bottlenecks of the Big Data systems. Currently, there are two major solutions. One is the pure data-driven diagnosis approach, which may be very time-consuming;the other is the rule-based analysis method, which usually requires prior knowledge. For Big Data applications like Spark workloads, we observe that the tasks in the same stages normally execute the same or similar codes on each data partition. On basis of the stage similarity and distributed characteristics of Big Data systems, we analyze the behaviors of the Big Data applications in terms of both system and micro-architectural metrics of each stage. Furthermore, for different performance problems, we propose a hybrid approach that combines prior rules and machine learning algorithms to detect performance anomalies, such as straggler tasks, task assignment imbalance, data skew, abnormal nodes and outlier metrics. Following this methodology, we design and implement a lightweight, extensible tool, named HybridTune, and measure the overhead and anomaly detection effectiveness of HybridTune using the BigDataBench benchmarks. Our experiments show that the overhead of HybridTune is only 5%, and the accuracy of outlier detection algorithm reaches up to 93%. Finally, we report several use cases diagnosing Spark and Hadoop workloads using BigDataBench, which demonstrates the potential use of HybridTune.

关键词： Big Data system spatio-temporal correlation rule-based diagnosis machine learning

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B-Spline Method for spatio-temporal Inverse Model

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Journal of Systems Science & Complexity 2022年第6期35卷 2336-2360页

作者： WANG Hongxia ZHAO Zihan WU Yuehua LUO Xuehong School of Statistics and Data Science Nanjing Audit UniversityNanjing 211815China Department of Mathematics and Statistics York UniversityTorontoOntarioCanada Department of Statistics and Data Science School of EconomicsXiamen UniversityXiamen 361005China

Inverse models can be used to estimate surface fluxes in terms of the observed atmospheric concentration measurement data.This paper proposes a new nonparametric spatio-temporal inverse model and provides the global expressions for the estimates by employing the B-spline method.The authors establish the asymptotic normality of the estimators under mild conditions.The authors also conduct numerical studies to evaluate the finite sample performance of the proposed methodologies.Finally,the authors apply the method to anthropogenic carbon dioxide(CO_(2))emission data from different provinces of Canada to illustrate the validity of the proposed techniques.

关键词： Inverse model spatio-temporal heterogeneity spatio-temporal correlation

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spatio-temporal Convolutional Network Based Power Forecasting of Multiple Wind Farms

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Journal of Modern Power Systems and Clean Energy 2022年第2期10卷 388-398页

作者： Xiaochong Dong Yingyun Sun Ye Li Xinying Wang Tianjiao Pu the School of Electrical and Electronic Engineering North China Electric Power UniversityBeijing 102206China he China Electric Power Research Institute Beijing 100192China

The rapidly increasing wind power penetration presents new challenges to the operation of power systems.Improving the accuracy of wind power forecasting is a possible solution under this circumstance.In the power forecasting of mul-tiple wind farms,determining the spatio-temporal correlation of multiple wind farms is critical for improving the forecasting accuracy.This paper proposes a spatio-temporal convolutional network(STCN)that utilizes a directed graph convolutional structure.A temporal convolutional network is also adopted to characterize the temporal features of wind power.Historical data from 15 wind farms in Australia are used in the case study.The forecasting results show that the proposed model has higher accuracy than the existing methods.Based on the structure of the STCN,asymmetric spatial correlation at different temporal scales can be observed,which shows the effectiveness of the proposed model.

关键词： Deep learning spatio-temporal correlation wind power forecasting graph conventional network(GCN).

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天山北坡人类活动强度与地表温度的时空关联性

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Journal of Geographical Sciences 2022年第10期32卷 1935-1955页

作者：陈泓瑾刘琳张正勇刘亚田浩康紫薇王统霞张雪莹 School of Science Shihezi UniversityShihezi 832000XinjiangChina Key Laboratory of Oasis Town and Mountain-basin System Ecology of Xinjiang Bingtuan Shihezi 832003XinjiangChina

Research on the spatio-temporal correlation between the intensity of human activities and the temperature of earth surfaces is of great significance in many aspects,including fully understanding the causes and mechanisms of climate change,actively adapting to climate change,pursuing rational development,and protecting the ecological environment.Taking the north slope of Tianshan Mountains,located in the arid area of northwestern China and extremely sensitive to climate change,as the research area,this study retrieves the surface temperature of the mountain based on MODIS data,while characterizing the intensity of human activities thereby data on the night light,population distribution and land use.The evolution characteristics of human activity intensity and surface temperature in the study area from 2000 to 2018 were analyzed,and the spatio-temporal correlation between them was further explored.It is found that:(1)The average human activity intensity(0.11)in the research area has kept relatively low since this century,and the overall trend has been slowly rising in a stepwise manner(0.0024·a-1);in addition,the increase in human activity intensity has lagged behind that in construction land and population by 1-2 years.(2)The annual average surface temperature in the area is 7.18℃with a pronounced growth.The rate of change(0.02℃·a-1)is about 2.33 times that of the world.The striking boost in spring(0.068℃·a-1)contributes the most to the overall warming trend.Spatially,the surface temperature is low in the south and high in the north,due to the prominent influence of the underlying surface characteristics,such as elevation and vegetation coverage.(3)The intensity of human activity and the surface temperature are remarkably positively correlated in the human activity areas there,showing a strong distribution in the east section and a weak one in the west section.The expression of its spatial differentiation and correlation is comprehensively affected by such factors as scopes of human activities,manifestations,and l

关键词： human activity intensity surface temperature nighttime light data spatio-temporal correlation north slope of Tianshan Mountains

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面向书画著录的文献循证与时空关联构建研究

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图书馆学情报学 2023年第1期

作者：高劲松付家炜

书画著录是中国古代书画研究领域的基础性资料，对于传世书画作品的考据、赏析和鉴别具有重要参考价值。随着数字人文研究范式的兴起，在新技术条件下依托人文数据基础设施开展面向书画著录的文献循证实践，将为古代书画作品的时空分析... 详细信息

书画著录是中国古代书画研究领域的基础性资料，对于传世书画作品的考据、赏析和鉴别具有重要参考价值。随着数字人文研究范式的兴起，在新技术条件下依托人文数据基础设施开展面向书画著录的文献循证实践，将为古代书画作品的时空分析与历史传承研究提供新的思路。文章在梳理相关研究现状的基础上，首先研究书画著录的时空语义描述与概念分层注释方法，提出面向书画著录的文献循证与时空关联构建过程，并以《石渠宝笈》书画著录为例开展案例实验。实验结果表明，文章提出的文献循证过程模型能够满足书画著录时空关联构建的需求，有助于为专业人士与社会公众提供多场景的书画著录知识服务。

关键词：数字人文文献循证时空关联书画著录知识组织 Digital Humanities Evidence-based Literature Research spatio-temporal correlation Catalogue Literature of Calligraphy and Painting Knowledge Organization

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