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big data analytics for retail industry using MapReduce-Apriori framework

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Journal of Management Analytics 2020年第3期7卷 424-442页

作者： Neha Verma Dheeraj Malhotra tinder Singh Vivekananda Institute of Professional Studies GGSIPUDelhiIndia Sant Baba Bhaag Singh niversity JalandharIndia

Presently,retailing has changed its face from unordered stacked traditional stores to beautifully decorated and appropriately managed merchandise stores or shopping malls with excellent ambiance and ***,these stores try to accommodate all needed items for daily use or rarely required items under the same ***,the primary challenge for today’s retailer is that the modern customer is quality and brands conscious as well as compare for services provided to them by different outlets at the comfort of home with a single ***,customers prefer to purchase from E-Commerce websites instead of physically visiting a retail store,which leads to the downfall in the sales of retailers which become a serious threat to ***,retailers are required to work sincerely towards their customer expectations by providing all their needed goods under the same ***,the objective of this paper is to assist retail business owners to recognize the purchasing needs of their customers and hence to entice customers to physical retail stores away from competitor E-Commerce *** paper employs a systematic research methodology based on association rule mining deployed over Map-Reduce based Apriori association mining and Hadoop based intelligent cloud architecture to determine useful buying patterns from purchase history of previous customers,in order to assist retail business *** finding acknowledges that the traditional mining algorithms have not progressed to support big data analysis as required by current retail businesses *** job of finding unknown association rules from big data requires a lot of resources such as memory and processing ***,traditional mining systems are inadequate to provide support for partial failure support,extensibility,scalability ***,this study aims to implement and develop MapReduce based Apriori(MR-Apriori)algorithm in the form of Intelligent Retail Mining Tool *** Tool

关键词： big data retail analytics MR-Apriori algorithm map-reduce market basket analysis association mining IRM tool

big data with Cloud Computing:Discussions and Challenges

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big data Mining and Analytics 2022年第1期5卷 32-40页

作者： Amanpreet Kaur Sandhu University Institute of Computing Chandigarh University

With the recent advancements in computer technologies,the amount of data available is increasing day by ***,excessive amounts of data create great challenges for ***,cloud computing services provide a powerful environment to store large volumes of *** eliminate various requirements,such as dedicated space and maintenance of expensive computer hardware and *** big data is a time-consuming task that requires large computational clusters to ensure successful data storage and *** this work,the definition,classification,and characteristics of big data are discussed,along with various cloud services,such as Microsoft Azure,Google Cloud,Amazon Web Services,International Business Machine cloud,Hortonworks,and MapR.A comparative analysis of various cloud-based big data frameworks is also *** research challenges are defined in terms of distributed database storage,data security,heterogeneity,and data visualization.

关键词： big data data analysis cloud computing Hadoop

Density estimation-based method to determine sample size for random sample partition of big data

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Frontiers of Computer Science 2024年第5期18卷 57-70页

作者： Yulin HE Jiaqi CHEN Jiaxing SHEN Philippe FOURNIER-VIGER Joshua Zhexue HUANG Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Computing and Decision Sciences Lingnan UniversityHong Kong 999077China

Random sample partition(RSP)is a newly developed big data representation and management model to deal with big data approximate computation *** research and practical applications have confirmed that RSP is an efficient solution for big data processing and ***,a challenge for implementing RSP is determining an appropriate sample size for RSP data *** a large sample size increases the burden of big data computation,a small size will lead to insufficient distribution information for RSP data *** address this problem,this paper presents a novel density estimation-based method(DEM)to determine the optimal sample size for RSP data ***,a theoretical sample size is calculated based on the multivariate Dvoretzky-Kiefer-Wolfowitz(DKW)inequality by using the fixed-point iteration(FPI)***,a practical sample size is determined by minimizing the validation error of a kernel density estimator(KDE)constructed on RSP data blocks for an increasing sample ***,a series of persuasive experiments are conducted to validate the feasibility,rationality,and effectiveness of *** results show that(1)the iteration function of the FPI method is convergent for calculating the theoretical sample size from the multivariate DKW inequality;(2)the KDE constructed on RSP data blocks with sample size determined by DEM can yield a good approximation of the probability density function(p.d.f);and(3)DEM provides more accurate sample sizes than the existing sample size determination methods from the perspective of *** demonstrates that DEM is a viable approach to deal with the sample size determination problem for big data RSP implementation.

关键词： random sample partition big data sample size Dvoretzky-Kiefer-Wolfowitz inequality kerneldensity estimator probability density function

Machine learning accelerated carbon neutrality research using big data-from predictive models to interatomic potentials

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Science China(Technological Sciences) 2022年第10期65卷 2274-2296页

作者： WU LingJun XU ZhenMing WANG ZiXuan CHEN ZiJian HUANG ZhiChao PENG Chao PEI XiangDong LI XiangGuo MAILOA Jonathan P HSIEH Chang-Yu WU Tao YU Xue-Feng ZHAO HaiTao Materials Interfaces Center Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China College of Materials Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 210016China Multiscale Crystal Materials Research Center Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China College of Computer National University of Defense TechnologyChangsha 410073China School of Materials Shenzhen Campus of Sun Yat-sen UniversityShenzhen 518107China Quantum Laboratory TencentShenzhen 518057China China Beacons Institute The University of Nottingham Ningbo ChinaNingbo 315100China

Carbon neutrality has been proposed as a solution for the current severe energy and climate crisis caused by the overuse of fossil fuels, and machine learning(ML) has exhibited excellent performance in accelerating related research owing to its powerful capacity for big data processing. This review presents a detailed overview of ML accelerated carbon neutrality research with a focus on energy management, screening of novel energy materials, and ML interatomic potentials(MLIPs), with illustrations of two selected MLIP algorithms: moment tensor potential(MTP) and neural equivariant interatomic potential(NequIP). We conclude by outlining the important role of ML in accelerating the achievement of carbon neutrality from global-scale energy management, unprecedented screening of advanced energy materials in massive chemical space, to the revolution of atomicscale simulations of MLIPs, which has the bright prospect of applications.

关键词： carbon neutrality machine learning big data molecular dynamics interatomic potentials

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big data analytics in oil and gas industry: An emerging trend

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Petroleum 2020年第4期6卷 321-328页

作者： Mehdi Mohammadpoor Farshid Torabi Faculty of Engineering and Applied Sciences University of ReginaReginaS4S 0A2SKCanada

This paper reviews the utilization of big data analytics,as an emerging trend,in the upstream and downstream oil and gas *** data or big data analytics refers to a new technology which can be employed to handle large datasets which include six main characteristics of volume,variety,velocity,veracity,value,and *** the recent advent of data recording sensors in exploration,drilling,and production operations,oil and gas industry has become a massive data intensive *** seismic and micro-seismic data,improving reservoir characterization and simulation,reducing drilling time and increasing drilling safety,optimization of the performance of production pumps,improved petrochemical asset management,improved shipping and transportation,and improved occupational safety are among some of the applications of big data in oil and gas *** the oil and gas industry has become more interested in utilizing big data analytics recently,but,there are still challenges mainly due to lack of business support and awareness about the big data within the ***,quality of the data and understanding the complexity of the problem are also among the challenging parameters facing the application of big data.

关键词： big data Hadoop R Oil and gas industry

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Querying big data: Bridging Theory and Practice

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Journal of Computer Science & Technology 2014年第5期29卷 849-869页

作者：樊文飞怀进鹏 School of Informatics University of EdinburghEdinburgh EH8 9AB U.K. International Research Center on big data Beihang University School of Computer Science and Engineering Beihang University

big data introduces challenges to query answering, from theory to practice. A number of questions arise. What queries are ＂tractable＂ on big data？ How can we make big data ＂small＂ so that it is feasible to find exact query answers？When exact answers are beyond reach in practice, what approximation theory can help us strike a balance between the quality of approximate query answers and the costs of computing such answers？ To get sensible query answers in big data,what else do we necessarily do in addition to coping with the size of the data？ This position paper aims to provide an overview of recent advances in the study of querying big data. We propose approaches to tackling these challenging issues,and identify open problems for future research.

关键词： big data query answering tractability approximation data quality

Chemical Empiricism 2.0 at Age of big data: Large-scale Prediction of Reaction Pathways Based on Bond Dissociation Energies

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Chinese Journal of Chemical Physics 2015年第6期28卷 -页

作者： Shi-lu Chen Key Laboratory of Cluster Science of Ministry of Education School of Chemistry Beijing Institute of Technology Beijing 100081 China

A programmable algorithm using bonddissociation energies has been proposed for the prediction of reaction pathways. It has been successfully applied to a gas-phase reaction of F2+CH3Cl, with the accurate revelation of the most favorable product CF4 and its corresponding reaction pathway. This acts as an inspiring example of chemical empiricism 2.0, and may open the door for the large-scale prediction of reaction pathways at the age of big data.

关键词： big data Bond dissociation energy Reaction pathway Prediction

Slope stability analysis based on big data and convolutional neural network

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Frontiers of Structural and Civil Engineering 2022年第7期16卷 882-895页

作者： Yangpan FU Mansheng LIN You ZHANG Gongfa CHEN Yongjian LIU School of Civil and Transportation Engineering Guangdong University of TechnologyGuangzhou 510006China

The Limit Equilibrium Method(LEM)is commonly used in traditional slope stability analyses,but it is time-consuming and *** to its complexity and nonlinearity involved in the evaluation process,it cannot provide a quick stability estimation when facing a large number of *** this case,the convolutional neural network(CNN)provides a better alternative.A CNN model can process data quickly and complete a large amount of data analysis in a specific situation,while it needs a large number of training *** is difficult to get enough slope data samples in practical *** study proposes a slope database generation method based on the *** were amplified from 40 typical slopes,and a sample database consisting of 20000 slope samples was *** sample database for slopes covered a wide range of slope geometries and soil layers’physical and mechanical *** CNN trained with this sample database was then applied to the stability prediction of 15 real slopes to test the accuracy of the CNN *** results show that the slope stability prediction method based on the CNN does not need complex calculation but only needs to provide the slope coordinate information and physical and mechanical parameters of the soil layers,and it can quickly obtain the safety factor and stability state of the ***,the prediction accuracy of the CNN trained by the sample database for slope stability analysis reaches more than 99%,and the comparisons with the BP neural network show that the CNN has significant superiority in slope stability ***,the CNN can predict the safety factor of real *** particular,the combination of typical actual slopes and generated slope data provides enough training and testing samples for the CNN,which improves the prediction speed and practicability of the CNN-based evaluation method in engineering practice.

关键词： slope stability limit equilibrium method convolutional neural network database for slopes big data

Towards Cost-Effective Cloud Downloading with Tencent big data

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Journal of Computer Science & Technology 2015年第6期30卷 1163-1174页

作者：李振华刘刚嵇智源 Roger Zimmermann School of Software Tsinghua University Beijing 100084 China Tsinghua National Laboratory for Information Science and Technology Beijing 100084 China QQXuanfeng System Group Tencent Co. Ltd Shanghai 200233 China High Technology Research and Development Center Ministry of Science and Technology Beijing 10004 China Department of Computer Science National University of Singapore Singapore 117417 Singapore

The cloud downloading scheme, first proposed by us in 2011, has effectively optimized hundreds of millions of users＇ downloading experiences. Also, people start to build a variety of useful Internet services on top of cloud downloading. In brief, by using cloud facilities to download （and cache） the requested file from the ＂best-effort＂ Internet on behalf of the user, cloud downloading ensures the data availability and remarkably enhances the data delivery speed. Although this scheme seems simple and straightforward, designing a real-world cloud downloading system involves complicated and subtle trade-offs （between deployment cost and user experience） when serving a large number of users： 1） how to plan the cloud cache capacity to achieve a high and affordable cache hit ratio, 2） how to accelerate the data delivery from the cloud to numerous users, 3） how to handle the dense user requests for highly popular files, and 4） how to judge a potential downloading failure of the cloud. This paper addresses these design trade-offs from a practical perspective, based on big data from a nationwide commercial cloud downloading system, i.e., Tencent QQXuanfeng. Its running traces help us find reasonable design strategies and parameters, and its real performances confirm the efficacy of our design. Our study provides solid experiences and valuable heuristics for the developers of similar and relevant systems.

关键词： cloud downloading cost-effective design trade-off big data