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LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spark SQL Applications 22

LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spar...

引用

international conference on Management of data (SIGMOD)

作者： Xin, Jinhan Hwang, Kai Yu, Zhibin Chinese Acad Sci Shenzhen Inst Adv Technol SIAT Shenzhen Guangdong Peoples R China Univ Chinese Acad Sci UCAS Beijing Peoples R China Chinese Univ Hong Kong Shenzhen Guangdong Peoples R China Shenzhen Huawei Cloud Comp Co Ltd Shuhai Lab Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781450392495

Spark SQL has been widely deployed in industry but it is challenging to tune its performance. Recent studies try to employ machine learning (ML) to solve this problem, but suffer from two drawbacks. First, it takes a long time (high overhead) to collect training samples. Second, the optimal configuration for one input data size of the same application might not be optimal for others. To address these issues, we propose a novel Bayesian Optimization (BO) based approach named LOCAT to automatically tune the configurations of Spark SQL applications online. LOCAT innovates three techniques. The first technique, named QCSA, eliminates the configuration-insensitive queries by Query Configuration Sensitivity Analysis (QCSA) when collecting training samples. The second technique, dubbed DAGP, is a datasize-Aware Gaussian Process (DAGP) which models the performance of an application as a distribution of functions of configuration parameters as well as input data size. The third technique, called IICP, Identifies Important Configuration Parameters (IICP) with respect to performance and only tunes the important ones. As such, LOCAT can tune the configurations of a Spark SQL application with low overhead and adapt to different input data sizes. We employ Spark SQL applications from benchmark suites TPC-DS, TPC - H, and HiBench running on two significantly different clusters, a four-node ARM cluster and an eight-node x86 cluster, to evaluate LOCAT. The experimental results on the ARM cluster show that LOCAT accelerates the optimization procedures of the state-of-the-art approaches by at least 4.1x and up to 9.7x;moreover, LOCAT improves the application performance by at least 1.9x and up to 2.4x. On the x86 cluster, LOCAT shows similar results to those on the ARM cluster.

关键词： big data in-memory computing Spark Spark SQL

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Preface

Preface

引用

computing and Machine intelligence (ICMI), international conference on

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Research on the Development strategy of Vocational Undergraduate Education in the Context of big data and Internet of Things 5

Research on the Development strategy of Vocational Undergrad...

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5th international conference on Computer Network, Electronic and Automation, ICCNEA 2022

作者： Tang, Wei Zhang, Jianhong Wu, Shuai Zhao, Qiulan School of Accounting and Finance Shaanxi Business College Xi'an China Xi'an International Studies University Business School Xi'an China

ISBN: (数字)9781665491099

ISBN: (纸本)9781665491099

With the deep integration of big data, artificial intelligence, cloud computing, Internet of Things and education, higher vocational education, especially at the undergraduate level, is facing important internal and external changes. China's vocational undergraduate education is still in the early stage of development, the practice of running schools has not made breakthrough, there are some key problems such as the supply of vocational undergraduate education system 'lag', the development path 'lost', the goal 'fuzzy', the concept of prejudice 'generalization'. Therefore, it is urgent to formulate the strategy of niche separation in higher vocational education in China. Adhere to the combination of 'crossing the river by feeling the stones' and top-level design, improve the timeliness of institutional supply;To establish a 'two-dimensional progressive' promotion mechanism for the linkage of ministries and provinces, and scientifically establish the main channel of development;Correctly handle the dialectical relationship between 'change' and 'constant', make clear the direction of running school and orientation of training;Give full play to the role of 'leading goose' of vocational undergraduate education, enhance the attraction of vocational education. Comprehensively promote the steady and long-term development of vocational undergraduate education. © 2022 IEEE.

关键词： big data

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LowFreqAttack: An Frequency Attack Method in Time Series Prediction 7th

LowFreqAttack: An Frequency Attack Method in Time Series Pr...

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7th international conference on Smart computing and Communication, SmartCom 2022

作者： Xiong, Neal N. He, Wenyong Lu, Mingming National Engineering Laboratory for Educational Big Data Central China Normal University Wuhan China School of Computer Science and Engineering Central South University HuNan ChangSha410083 China

ISBN: (纸本)9783031281235

Time series prediction has become an important research direction in data mining because of the time-varying pattern of data in various fields. However, time series prediction suffers from the problem of vulnerability to adversarial example attack, which leads to models making wrong decisions in critical application scenarios and causing great losses to people’s lives and properties. In addition, there is relatively little attacks research on time series prediction, and the existing attack methods simply migrate classical-attack methods in the image to time series prediction. On the one hand, it not only without fully considering the characteristics of temporal data but also without comparing and analyzing the effects of those classical-attack methods on time series prediction models. On the other hand, there is no comparative analysis of the effectiveness of these classical attack methods in different time series prediction methods. To address the above problems, this paper firstly compares the effectiveness of the attack methods on some time series prediction models and analyzes the inner mechanism of these time series prediction models. In addition, this paper finds that the defense ability of those models is related to their ability to portray the overall trend of time series data. Therefore, this paper further propose the new attack method, LowFreqAttack. The experimental results show that LowFreqAttack can attack the three existing time series prediction models better than the existing attach methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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Exploration of Multiple Fusion Digital intelligences Talent Training Mode under the Background of "Artificial intelligence + New Engineering" 14

Exploration of Multiple Fusion Digital Intelligences Talent ...

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14th international conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2022

作者： Xu, Caiyun Wu, Qinglin Wuhan Institute of Bioengineering School of Computer and Information Engineering Wuhan China Wuhan Business University School of Information Engineering Wuhan China

ISBN: (数字)9781665461696

ISBN: (纸本)9781665461696

With the rapid development of new generation technologies such as artificial intelligence, cloud computing, 5G and big data, it is deeply integrated with the real economy. "Digital enterprise"is accelerating the transformation to "digital intelligent enterprise".Great changes have taken place in the demand for talents in the development of digital intelligence in modern industries, which promotes the deepening reform of education. Such as university personnel training target positioning, curriculum system, teaching model and so on. In order to meet the talent demand of the development of digital intelligence industry, the talent training mode under the background of "artificial intelligence + new engineering"is explored, the concept of multi-fusion training is proposed to achieve the integration of multi-party high-quality resources, Deepen school enterprise cooperation and jointly create an integrated experimental platform for "industry university research innovation",and improve the quality of digital intelligence talent training. © 2022 IEEE.

关键词： Personnel training

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Application and Trend Analysis of Artificial intelligence in Weighing Apparatus

Application and Trend Analysis of Artificial Intelligence in...

引用

Cloud computing, big data and Internet of Things (3CBIT), international conference on

作者： Lili Zhang Kai Xu Yuan Li Kun Jiang Shanghai Institute of Measurement and Testing Technology Shanghai China

ISBN: (纸本)9781665492263

This paper introduces the new discipline of artificial intelligence technology in *** with the development history of weighing instruments, it expounds that the intelligent development of the industrial chain of weighing instruments industry is inseparable from artificial intelligence. By introducing the penetration of artificial intelligence in the weighing instrument field, this paper describes the speech recognition technology and machine vision system technology, and gives a detailed example of the application of the two technologies in the weighing instrument intelligence, which leads to the problems and analysis of artificial intelligence in the weighing instrument industry. Finally, a predictive analysis of the future integration and development trend of artificial intelligence and weighing instruments is made.

关键词： Industries Manufacturing industries Instruments Speech recognition Market research Power grids Internet of Things

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Smart Farming based on Deep Learning Approaches 2

Smart Farming based on Deep Learning Approaches

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2nd Odisha international conference on Electrical Power Engineering, Communication and computing Technology, ODICON 2022

作者： Tripathy, Subhranshu Sekhar Tripathy, Niva Rath, Mamata Pati, Abhilash Panigrahi, Amrutanshu Dash, Manoranjan Driems Autonomous College Dept. of Computer Science and Engg. Odisha Cuttack India Siksha 'o' Anusandhan University Dept. of Computer Science and Engg. Odisha Bhubaneswar India Siksha 'o' Anusandhan University Faculty of Management Sciences Odisha Bhubaneswar India

ISBN: (纸本)9781665478397

A novel idea called 'smart farming' aims to increase the productivity and effectiveness of agriculture by utilizing cutting-edge information technologies. Using the most recent developments in automation, artificial intelligence, and networking, farmers are better able to keep an eye on every step of the process and apply exact treatments selected by machines with superhuman accuracy. With an expanding global population comes a rise in the need for both employment and food. The farmers' traditional practices fell short of meeting these demands. New automated methods were consequently proposed. These innovative techniques satisfied the world's food needs while also creating jobs for billions of people. For effective farm management, a variety of techniques are utilized, including IoT, cloud computing, AI, machine learning, deep learning, big data, etc. Deep learning is one of these, and it is a developing field of study for crop yield forecasting. Engineers, data scientists, and farmers are still developing methods to optimize the amount of human labor needed in agriculture. As key information sources improve, smart farming transforms into a learning system that grows smarter every day. Artificial neural network principles are used in a machine learning method known as deep learning. The depth of deep learning networks, which also allows them to detect latent structures in unlabeled, unstructured data, is what sets them apart from neural networks. Compared to ML techniques, deep learning networks that automatically extract features without human involvement have a considerable advantage. In this research, we suggest a recommendation model and an algorithm to assess crop yield in the upcoming year. We have contrasted the deep learning algorithm with the random forest machine learning algorithm. These algorithms have been the subject of a brief comparison. Python has been used to implement the suggested model. © 2022 IEEE.

关键词： Cloud computing

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Semantic Reasoning Technology on Temporal Knowledge Graph 19th

Semantic Reasoning Technology on Temporal Knowledge Graph

引用

19th international conference on Web Information Systems and Applications, WISA 2022

作者： Li, Jianuo Zhao, Feng Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031203084

Semantic reasoning techniques based on knowledge graphs have been widely studied since they were proposed. Previous studies are mostly based on closed-world assumptions, which cannot reason about unknown facts. To this end, we propose the Two-Stage Temporal Reasoning Model (TSTR) for reasoning about future facts. In the first stage, probability of future facts occurring is reasoned using repeated information in history. In the second stage, the semantics of the neighborhood nodes are aggregated using the structural encoder and the temporal information is captured using the temporal encoder. The predicted probabilities are obtained by the decoder. Finally, the candidate entity probabilities of the two-stage reasoning are weighted to achieve the prediction of the two-stage fusion. We tested the performance of the TSTR on public datasets and the results demonstrated the effectiveness of the TSTR. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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Research on long and short-term social recommendation based on convolutional and gated recurrent units 29

Research on long and short-term social recommendation based ...

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29th IEEE international conference on Parallel and Distributed Systems, ICPADS 2023

作者： Jia, Zihe Xue, Peng Dai, Zhiqiang Gao, Qian Zhang, Xiaomeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350330717

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users' long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data;secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier;finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved. © 2023 IEEE.

关键词： convolutional gated recurrent unit graph attention networks neighborhood recommendation systems social networks

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C²-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System 15

C<SUP>2</SUP>-CRS: Coarse-to-Fine Contrastive Learning for C...

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15th ACM international conference on Web Search and data Mining (WSDM)

作者： Zhou, Yuanhang Zhou, Kun Zhao, Wayne Xin Wang, Cheng Jiang, Peng Hu, He Renmin Univ China Sch Informat Beijing Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Kuaishou Inc Beijing Peoples R China

ISBN: (纸本)9781450391320

Conversational recommender systems (CRS) aim to recommend suitable items to users through natural language conversations. For developing effective CRSs, a major technical issue is how to accurately infer user preference from very limited conversation context. To address issue, a promising solution is to incorporate external data for enriching the context information. However, prior studies mainly focus on designing fusion models tailored for some specific type of external data, which is not general to model and utilize multi-type external data. To effectively leverage multi-type external data, we propose a novel coarse-to-fine contrastive learning framework to improve data semantic fusion for CRS. In our approach, we first extract and represent multi-grained semantic units from different data signals, and then align the associated multi-type semantic units in a coarse-to-fine way. To implement this framework, we design both coarse-grained and fine-grained procedures for modeling user preference, where the former focuses on more general, coarse-grained semantic fusion and the latter focuses on more specific, fine-grained semantic fusion. Such an approach can be extended to incorporate more kinds of external data. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach in both recommendation and conversation tasks.

关键词： Conversational Recommender System Contrastive Learning

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