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How good is query optimizer in Spark? 14th

14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Ren, Zujie Yun, Na Li, Youhuizi Wan, Jian Wang, Yuan Yu, Lihua Fan, Xinxin School of Computer Science Hangzhou Dianzi University Hangzhou China Department of Software Engineering Zhejiang University of Science and Technology Hangzhou China Key Enterprise Research Institute of NetEase Big Data of Zhejiang Province Netease Hangzhou Network Co. Ltd. Hangzhou China

ISBN: (纸本)9783030129804

In the big data community, Spark plays an important role and is used to process interactive queries. Spark employs a query optimizer, called Catalyst, to interpret SQL queries to optimized query execution plans. Catalyst contains a number of optimization rules and supports cost-based optimization. Although query optimization techniques have been well studied in the field of relational database systems, the effectiveness of Catalyst in Spark is still unclear. In this paper, we investigated the effectiveness of rule-based and cost-based optimization in Catalyst, meanwhile, we obtained a set of comparative experiments by varying the data volume and the number of nodes. It is found that even when applied query optimizations, the execution time of most TPC-H queries were slightly reduced. Some interesting observations were made on Catalyst, which can enable the community to have a better understanding and improvement of the query optimizer in Spark. © 2019, ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Query processing

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Mathematical model-based study of Scottish herring and mackerel migration

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IOP Conference Series: Earth and Environmental science 2021年第1期687卷

作者： Zhenzhen Yang Jiaxin Yue Yadan Yu Yuehua Zhang Lan Li Faculty of Science Jiamusi University Jiamusi City Heilongjiang Province 154007 China School of Data Science and Software Engineering Wuzhou University Wuzhou City Guangxi Province 543000 China

The global sea temperature continues to rise and reaches a new high in 2019. This phenomenon will force the migration of Scottish herring and mackerel to higher latitudes and severely inhibit the development of small-scale Scottish fishing companies. This paper establishes a grey prediction model, a regression analysis prediction model and a fuzzy comprehensive evaluation model to explore the migration locations of herring and mackerel in the next 50 years, and to formulate appropriate business strategies for small-scale Scottish fishing companies. The model results show that in the next 50 years, herring and mackerel will migrate to Greenland and Norwegian waters, and will migrate out of the fishing range of small fishing companies from 2046 to 2053. Therefore, reducing labour costs is the most appropriate business strategy.

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An advanced operating environment for mathematics education resources

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science China(Information sciences) 2018年第9期61卷 270-281页

作者： Yongsheng RAO Jingzhong ZHANG Yu ZOU Yanchun SUN Xiangping CHEN Songhua XU School of Data and Computer Science Sun Yat-Sen University Academy for Intelligent Software Guangzhou University Institute of Mathematics and Computer Science Guizhou Normal College School of Electronics Engineering and Computer Science Peking University Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing College of Computer Sciences New Jersey Institute of Technology

The potential of information technology(IT)in education modernization has been widely *** the broad efforts for advancing education IT,digital education resources continue to receive a high level of attention,with the... 详细信息

关键词： SSP OA An advanced operating environment for mathematics education resources OER Figure

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NERank+: a graph-based approach for entity ranking in document collections

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计算机科学前沿 2018年第3期12卷 504-517页

作者： Chengyu WANG Guomin ZHOU Xiaofeng HE Aoying ZHOU Shanghai Key Laboratory of Trustworthy Computing School of Computer Science and Software EngineeringEast China Normal University Shanghai 200062 China Department of Computer and Information Technology Zhejiang Police College Hangzhou 310053 China School of Data Science and Engineering East China Normal University Shanghai 200062 China

Most entity ranking research aims to retrieve a ranked list of entities from a Web corpus given a user *** rank order of entities is determined by the relevance between the query and contexts of ***,entities can be ranked directly based on their relative importance in a document collection,independent of any *** this paper,we introduce an entity ranking algorithm named NERank+.Given a document collection,NERank+ first constructs a graph model called Topical Tripartite Graph,consisting of document,topic and entity *** design separate ranking functions to compute the prior ranks of entities and topics,respectively.A meta-path constrained random walk algorithm is proposed to propagate prior entity and topic ranks based on the graph *** evaluate NERank+ over real-life datasets and compare it with *** results illustrate the effectiveness of our approach.

关键词： entity ranking Topical Tripartite Graph prior rank estimation meta-path constrained random walk

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Towards Automated Microfluidic-based Platforms: Optimizing Hydrogenation Efficiency of Nitrobenzene through π–π Interactions in Pd Nanoparticles on Covalent Organic Frameworks

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Angewandte Chemie 2023年第23期135卷

作者： Juan Deng Yueji Cai Jingsheng Chen Qing Wang Jun Lu Xiao Chen Wuxiang Zhang Kangjie Wu Dr. Weikang Wang Lingfei Wei Fu Yang Aihua Yuan Prof. Huan Pang Prof. Chao Yu School of Environmental and Chemical Engineering Jiangsu University of Science and Technology Zhenjiang Jiangsu 212003 China Contribution: Data curation (lead) Investigation (lead) Writing - original draft (lead) Writing - review & editing (lead) Contribution: Data curation (lead) Methodology (lead) Validation (lead) Writing - original draft (equal) Writing - review & editing (equal) Contribution: Data curation (equal) Methodology (equal) Contribution: Data curation (equal) Supervision (equal) Contribution: Conceptualization (equal) Software (lead) Writing - original draft (supporting) Writing - review & editing (supporting) Contribution: Conceptualization (equal) Supervision (equal) Writing - original draft (supporting) Writing - review & editing (supporting) Contribution: Methodology (equal) Contribution: Data curation (supporting) Investigation (equal) School of Materials Science and Engineering Jiangsu University Zhenjiang Jiangsu 212013 China Contribution: Conceptualization (equal) Data curation (supporting) Contribution: Conceptualization (equal) Contribution: Supervision (equal) Contribution: Supervision (supporting) School of Chemistry and Chemical Engineering Yangzhou University Yangzhou 225009 Jiangsu China Contribution: Conceptualization (lead) Supervision (lead)

Microplatform with timed automata has been leveraged for guiding the preparation of molecules, whereas the requirement of handling expertise and sophisticated instrument is inevitable in combination with heterogeneous catalysis. Here we report a microfluidic-based autolab with open structures, called Put & Play Automated Microplatform (PPAM). It shows the efficient hydrogenation performance of palladium nanoparticles on the triphenylene-based covalent organic frameworks (Pd/TP-COFs) in which the π–π interactions of TP rings in the vicinity of Pd is optimized by easy change-over of catalyst and simple tuning of reactor geometries in PPAM. Using experiment/simulation of the Pd/TP-COFs coating (PCC) and mixing (PCM) across PPAM with different channel sizes, the turnover frequencies are 60 times the commonly used batch reactor, and aniline productivity of 8.8 g h −1 is achieved in 0.09 cm 3 . This work will raise awareness about the benefits of the catalyst-loaded microplatform in future materials performance campaigns.

关键词： Covalent Organic Framework Heterogeneous Catalyst Microplatform

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STAN: Towards Describing Bytecodes of Smart Contract

arXiv

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arXiv 2020年

作者： Li, Xiaoqi Chen, Ting Luo, Xiapu Zhang, Tao Yu, Le Xu, Zhou Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong Center for Cybersecurity University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology Macau University of Science and Technology China School of Big Data and Software Engineering Chongqing University Chongqing China

More than eight million smart contracts have been deployed into Ethereum, which is the most popular blockchain that supports smart contract. However, less than 1% of deployed smart contracts are open-source, and it is difficult for users to understand the functionality and internal mechanism of those closed-source contracts. Although a few decompilers for smart contracts have been recently proposed, it is still not easy for users to grasp the semantic information of the contract, not to mention the potential misleading due to decompilation errors. In this paper, we propose the first system named STAN to generate descriptions for the bytecodes of smart contracts to help users comprehend them. In particular, for each interface in a smart contract, STAN can generate four categories of descriptions, including functionality description, usage description, behavior description, and payment description, by leveraging symbolic execution and NLP (Natural Language Processing) techniques. Extensive experiments show that STAN can generate adequate, accurate and readable descriptions for contract’s bytecodes, which have practical value for users. Copyright © 2020, The Authors. All rights reserved.

关键词： Smart contract

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Multi-Scale Hierarchical Context-Aware Survival Prediction Network based on whole slide images

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Biomedical Signal Processing and Control 2025年 110卷

作者： Jinmiao Song Yatong Hao Hui Zhai Shuang Zhao Tao Ning Shan Huang Xiaodong Duan School of Software Xinjiang University Urumqi 830046 Xinjiang China School of Computer Science and Engineering Dalian Minzu University Dalian 116650 Liaoning China State Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology Dalian Minzu University Dalian 116650 Liaoning China Dalian Key Laboratory of Digital Technology for Minzu Culture Dalian Minzu University Dalian 116650 Liaoning China The First Affiliated Hospital Xinjiang Medical University Urumqi 830011 Xinjiang China

In high-resolution whole slide images (WSIs), multi-scale information is crucial for survival prediction. However, due to the ultra-large sizes of WSIs, existing methods have not fully utilized the multi-scale information at the gigapixel scale. Additionally, WSI-based survival prediction, as a patient-level multiple instance learning (MIL) task, is far more complex than WSI-level MIL which presents a significant challenge. To address these challenges, we propose a Multi-Scale Hierarchical Context-Aware Survival Prediction Network (MSASurv). This network progressively explores the tumor microenvironment, tumor-associated tissue structures, and patient-level tumor heterogeneity in WSIs. We validated our approach using five types of cancer from The Cancer Genome Atlas (TCGA), including 3,068 H&E-stained WSIs. Experimental results demonstrate that our proposed MSASurv algorithm outperforms previous weakly supervised methods by 3.3% to 16.8%. The code and models are publicly available at https://***/yatonghao/MSASurv .

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BuildSenSys: Reusing building sensing data for traffic prediction with cross-domain learning

arXiv

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arXiv 2020年

作者： Fan, Xiaochen Xiang, Chaocan Chen, Chao Yang, Panlong Gong, Liangyi Song, Xudong Nanda, Priyadarsi He, Xiangjian School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China College of Computer Science Chongqing University Chongqing400044 China School of Computer Science and Technology University of Science and Technology of China Hefei Anhui230026 China School of Software BNRist Tsinghua University Beijing100084 China

With the rapid development of smart cities, smart buildings are generating a massive amount of building sensing data by the equipped sensors. Indeed, building sensing data provides a promising way to enrich a series of data-demanding and cost-expensive urban mobile applications. In this paper, as a preliminary exploration, we study how to reuse building sensing data to predict traffic volume on nearby roads. Compared with existing studies, reusing building sensing data has considerable merits of cost-efficiency and high-reliability. Nevertheless, it is non-trivial to achieve accurate prediction on such cross-domain data with two major challenges. First, relationships between building sensing data and traffic data are not unknown as prior, and the spatio-temporal complexities impose more difficulties to uncover the underlying reasons behind the above relationships. Second, it is even more daunting to accurately predict traffic volume with dynamic building-traffic correlations, which are cross-domain, non-linear, and time-varying. To address the above challenges, we design and implement BuildSenSys, a first-of-its-kind system for nearby traffic volume prediction by reusing building sensing data. Our work consists of two parts, i.e., Correlation Analysis and Cross-domain Learning. First, we conduct a comprehensive building-traffic analysis based on multi-source datasets, disclosing how and why building sensing data is correlated with nearby traffic volume. Second, we propose a novel recurrent neural network for traffic volume prediction based on cross-domain learning with two attention mechanisms. Specifically, a cross-domain attention mechanism captures the building-traffic correlations and adaptively extracts the most relevant building sensing data at each predicting step. Then, a temporal attention mechanism is employed to model the temporal dependencies of data across historical time intervals. The extensive experimental studies demonstrate that BuildSenSys outperf

关键词： Internet of things

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Rely-Guarantee Reasoning about Messaging System for Autonomous Vehicles

Rely-Guarantee Reasoning about Messaging System for Autonomo...

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Theoretical Aspects of software engineering (TASE)

作者： Wenjing Xu Yongwang Zhao Dianfu Ma YuXin Zhang Qian Xiao State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang Univerisity Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China State Key Laboratory of Automotive Simulation and Control College of Automotive Engineering Jilin University Jilin China School of Computer Science and Technology Guizhou University Guiyang China

Messaging system as a communicating infrastructure is a safety-critical component of autonomous vehicles. For the purpose of safety certification of Level 4 autonomous driving systems, its necessary to provide a formally verified specification of messaging systems. This paper presents a realistic case study using the PiCore rely-guarantee framework to formally verify the DGPS (Differential Global Positioning System) of UISEE autonomous driving systems. We first create an axiom model of messaging systems by extending PiCore, which is reusable for concrete applications. The model supports dynamic configuration of message buffers as well as the automatic generation of the rely and guarantee conditions for compositional reasoning. It is instantiated when developing the formal specification of DGPS. Then, we use the rely-guarantee proof system of PiCore to verify the functional correctness and invariant of DGPS. The specification and its proof provide a strong evidence for the ongoing safety certification of DGPS.

关键词： Cognition Safety Formal specifications Vehicle dynamics Autonomous vehicles Certification Global Positioning System

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What is the focus of energy supply chain relationship management during geopolitical risks? Evidence from the stock market based on transaction cost economics

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Energy Economics 2025年 148卷

作者： Shizhen Bai Hao He Chunjia Han Mu Yang Wen-Long Shang Weijia Fan School of Management Harbin University of Commerce Harbin 150028 PR China Business School Birkbeck University of London Malet Street Bloomsbury London WC1E 7HX United Kingdom Centre for Transport Studies Imperial College London London SW7 2AZ United Kingdom Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 PR China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 518060 PR China

This study examines B2B relationship management practices in energy supply chains under geopolitical risks and their impact on stock market volatility. We analyze U.S. energy firms' earnings conference calls across three dimensions: intra-firm managerial behavior, inter-firm relationship management, and relationship evaluation. Employing Word2Vec and structural topic modeling, we identify specific B2B relationship management strategies deployed during geopolitical uncertainties. Our findings reveal varied effects of these strategies on market volatility. Controls on energy prices, carbon-neutral technological alliances, and ESG assessments contribute to increased volatility. Conversely, strategies focused on maintaining demand in changing markets, long-term shareholder return management, strategic industry communication, and enhanced technological R&D partnerships reduce volatility. Notably, R&D cooperation in LNG technology, customer interaction improvements, and operational efficiency initiatives show no significant effect. Furthermore, market attention moderates the relationship between B2B relationship management and market volatility. Disclosures emphasizing long-term investments correlate with decreased market share and reduced volatility, while short-term investment communications increase both market share and volatility. It suggests energy firms must balance short-term performance with long-term stability in their disclosure strategies. This research advances the application of artificial intelligence in B2B relationship management under geopolitical uncertainty, enriching theoretical foundations in energy supply chain management while providing practical guidance for firms navigating complex global risks.

关键词： Business-to-business (B2B) relationship management Energy supply chain Geopolitical risks Stock market volatility Stock market attention Disruptive technologies Earning conference call

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