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big data analytics:a literature review

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Journal of Management analytics 2015年第3期2卷 175-201页

作者： Dazhi Chong Hui Shi Department of Information Technology&Decision Sciences Old Dominion UniversityNorfolkUSA Department of Management and Information Sciences University of Southern IndianaEvansvilleUSA

AbstractWith more and more data generated,it has become a big challenge for traditional architectures and infrastructures to process large amounts of data within an acceptable time and resources.In order to efficiently extract value from these data,organizations need to find new tools and methods specialized for big data processing.For this reason,big data analytics has become a key factor for companies to reveal hidden information and achieve competitive advantages in the market.Currently,enormous publications of big data analytics make it difficult for practitioners and researchers to find topics they are interested in and track up to date.This paper aims to present an overview of big data analytics’content,scope and findings as well as opportunities provided by the application of big data analytics.

关键词： big data analytics programming model storage application

big data analytics for Improving Financial Performance and Sustainability

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Journal of Systems Science and Information 2021年第2期9卷 175-191页

作者： Xiangyu ZHU Yang YANG School of Economics and Management Beijing University of TechnologyBeijing 100124China

In this study,the key drivers of sustainability commitment,green supply chain management,big data integration and green human resource practice are explored,and the impact of these sustainable capabilities on the environmental and financial performance of banks is also elaborated.In addition,the influence of green management practices on integrating big data technology into operations is presented.As for the concept of dynamic ability,it has been used to recommend and empirically test conceptual models.data were collected through a self-administrated survey questionnaire on 317 people working in 37 banks in six Asian countries.Research suggests that big data analytics strategies have an impact on internal processes and on the stability and financial performance of banks.Besides,it is indicated that banks are committed to proper data monitoring of their customers to complete operational efficiency and sustainability goals.Furthermore,our result proved that banks practicing Green Innovation strategies experience better environmental and economic performance because their employees are already trained in Green HR.Finally,from our study,it was found that internal and external green supply chain management practices have a positive effect on the environmental and financial performance of banks,thus ensuring that the bank of Association of Southeast Asian Nations(ASEAN)mitigates the environmental impact through its operations and ultimately experiences an increase in financial performance.

关键词： financial performance sustainability big data analytics banks green management

Apollo: Rapidly Picking the Optimal Cloud Configurations for big data analytics Using a data-Driven Approach

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Journal of Computer Science & Technology 2021年第5期36卷 1184-1199页

作者： Yue-Wen Wu Yuan-Jia Xu Heng Wu Lin-Gang Su Wen-Bo Zhang Hua Zhong University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

big data analytics applications are increasingly deployed on cloud computing infrastructures,and it is still a big challenge to pick the optimal cloud configurations in a cost-effective way.In this paper,we address this problem with a high accuracy and a low overhead.We propose Apollo,a data-driven approach that can rapidly pick the optimal cloud configurations by reusing data from similar workloads.We first classify 12 typical workloads in bigdataBench by characterizing pairwise correlations in our offline benchmarks.When a new workload comes,we run it with several small datasets to rank its key characteristics and get its similar workloads.Based on the rank,we then limit the search space of cloud configurations through a classification mechanism.At last,we leverage a hierarchical regression model to measure which cluster is more suitable and use a local search strategy to pick the optimal cloud configurations in a few extra tests.Our evaluation on 12 typical workloads in HiBench shows that compared with state-of-the-art approaches,Apollo can improve up to 30%search accuracy,while reducing as much as 50%overhead for picking the optimal cloud configurations.

关键词： big data analytics cloud configuration data driven

An Improved War Strategy Optimization Algorithm for big data analytics

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国际计算机前沿大会会议论文集 2023年第1期 37-48页

作者： Longjie Han Hui Xu Yain Hu School of Computer Science Hubei University of TechnologyWuhan 430068China

big data analysis is confronted with the obstacle of high dimensionality in data samples.To address this issue,researchers have devised a multitude of intel-ligent optimization algorithms aimed at enhancing big data analysis techniques.Among these algorithms is the War Strategy Optimization(WSO)proposed in 2022,which distinguishes itself from other intelligence algorithms through its potent optimization capabilities.Nevertheless,the WSO exhibits limitations in its global search capacity and is susceptible to becoming trapped in local optima when dealing with high-dimensional problems.To surmount these shortcomings and improve the performance of WSO in handling the challenges posed by high dimensionality in big data,this paper introduces an enhanced version of the WSO based on the carnivorous plant algorithm(CPA)and shared niche.The grouping concept and update strategy of CPA are incorporated into WSO,and its update strategy is modiﬁed through the introduction of a shared small habitat approach combined with an elite strategy to create a novel improved algorithm.Simula-tion experiments were conducted to compare this new War Strategy Optimization(CSWSO)with WSO,RKWSO,I-GWO,NCHHO and FDB-SDO using 16 test functions.Experimental results demonstrate that the proposed enhanced algorithm exhibits superior optimization accuracy and stability,providing a novel approach to addressing the challenges posed by high dimensionality in big data.

关键词： big data analytics war strategy optimization carnivorous plant algorithm shared niche

analytics,challenges and applications in big data environment:a survey

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Journal of Management analytics 2016年第3期3卷 206-239页

作者： Mininath R.Bendre Vijaya R.Thool Department of Information Technology SGGSIE&TVishnupuriNandedIndia Department of Instrumentation Engineering SGGSIE&TVishnupuriNandedIndia

big data refer to the massive amounts and varieties of information in the structured and unstructured form,generated by social networking sites,biomedical equipment,financial companies,internet and websites,scientific sensors,agriculture engineering sources,and so on.This huge amount of data cannot be processed using traditional data processing systems and technologies.big data analytics is a process of examining information and patterns from huge data.Hence,the process needs a system architecture for data collection,transmission,storage,processing and analysis,and visualization mechanisms.In this paper,we review the background and futuristic aspects of big data.We first introduce the history,background and related technologies of big data.We focus on big data system architecture,phases and classes of big data analytics.Then we present an open source big data framework to address some of the big data challenges.Finally,we discuss different applications of big data with some examples.

关键词： unstructured data big data big data analytics cloud computing apache hadoop

Stocks Analysis and Prediction Using big data analytics

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Stocks Analysis and Prediction Using Big Data Analytics

Application of big data analytics for Project Planning in Construction Environment in China

作者： Zhihao PENG Department of Computer Science Dalian Neusoft Institute of Information

big data analytics are used primarily in various sectors for accurate prediction and analysis of the large data sets.They allow the discovery of significant information from large data sets,otherwise,it is hidden.In this paper,an approach of robust Cloudera-Hadoop based data pipeline is proposed to perform analyses for any scale and type of data,in which selected US stocks are analysed to predict daily gains based on real time data from Yahoo Finance.The Apache Hadoop big-data framework is provided to handle large data sets through distributed storage and processing,stocks from the US stock market are picked and their daily gain data are divided into training and test data set to predict the stocks with high daily gains using Machine Learning module of Spark.

关键词： big data analytics big data stock market machine learning

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Application of Big Data Analytics for Project Planning in Co...

作者： Numan Khan Afridi 山东大学

学位级别：硕士

In recent years’huge number of technological advancement occurred in business developments,planning and decision making structures.Firms tends to be driven by data and information to make any possible changes and application of certain concepts in their structural domains.Health,defense,business,IT are considered to be the earlier adopters of advance analytical platforms to develop their business by getting useful insights.According to 2009 survey by TDWI（Transforming data with Intelligence）38%of firms worldwide use advance analytical techniques to gain useful insights and plans accordingly while 85%are considering to apply in the coming years.Architecture,Engineering and Construction industries（AEC）are also relying on these analytical technologies such as big data analytics because of its overwhelming impact in better management of projects.Olsson et.***.in 2015 said that big data is very useful in evaluation of projects and over project management framework.big data analytics is a platform of analysis in which huge amount and variety of data is gathered from projects,that can be structured and unstructured in form of pictures,real time information such as text messages,scientific data and so on.After gathering the data through different platforms such as Hadoop,else the data is analyzed and insights are taken for planning and decision making.This miscellaneous dynamic nature of big data analytics provides assistance in planning’s of projects.To comprehend the overall scenario of big data analytics the study was conducted to find out the challenges firms faces in application big data analytics and what they things about its impact and usefulness and last how they see the future of big data analytics in construction industries.They study recorded 72 responses from several construction companies,building,road and plants construction firms.The recorded result was analyzed through smart PLS by establishing relation between several factors that represents,the challenges,benefits,and the way forward for big data analyt

关键词： Project Planning Construction Environment big data analytics Application

User behaviour analysis using data analytics and machine learning to predict malicious user versus legitimate user

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High-Confidence Computing

High-Confidence Computing 2022年第1期2卷 9-18页

作者： Rohit Ranjan Shashi Shekhar Kumar Thapar Institute of Engineering and Technology Thapar UniversityPatialaIndia Indian Institute of Information Technology Allahabad IIIT AllahabadPrayarajIndia

Research-based on user behavior analysis for authentication is the motivation for this research.We move ahead using a behavioral approach to identify malicious users and legitimate users.In this paper,we have explained how we have applied big data analytics to application-layer logs and predicted malicious users by employing a Machine Learning algorithm based on certain metrics explained later in the paper.Machine Learning would present a list of IP addresses or user identification tokens(UIT),deduced from live data which would be performing a malicious activity or are suspected of malicious activity based on their browsing behavior.We have created an e-commerce web application and induced vulnerabilities intentionally for this purpose.We have hosted our setup on LAMP[1]stack based on AWS cloud[2].This method has a huge potential as any organization can imply this to monitor probable attackers thus narrowing down on their efforts to safeguard their infrastructure.The idea is based on the fact that the browsing pattern,as well as the access pattern of a genuine user,varies widely with that of a hacker.These patterns would be used to sort out the incoming traffic from and list out IP addresses and UIT that are the most probable cases of hack attempts.

关键词： Application security big data analytics Machine learning Random forest Behavioral analysis Prediction

SOCA-DOM:A Mobile System-on-Chip Array System for Analyzing big data on the Move

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Journal of Computer Science & Technology 2022年第6期37卷 1271-1289页

作者：李乐乐刘江佾樊建平钱学海黄铠钟叶青喻之斌 Center for Heterogeneous and Intelligent Computing Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen 518055China School of Science and Engineering The Chinese University of Hong KongShenzhenShenzhen 518172China Center for High Performance Computing Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Shenzhen 518055China Ming Hsieh Department of Electrical and Computer Engineering University of Southern CaliforniaLos Angeles CA 90089-0001U.S.A. Department of Computer Science University of Southern CaliforniaLos AngelesCA 90089-0001U.S.A. School of data and Science The Chinese University of Hong KongShenzhenShenzhen 518172China

Recently,analyzing big data on the move is booming.It requires that the hardware resource should be low volume,low power,light in weight,high-performance,and highly scalable whereas the management software should be flexible and consume little hardware resource.To meet these requirements,we present a system named SOCA-DOM that encompasses a mobile system-on-chip array architecture and a two-tier“software-defined”resource manager named Chameleon.First,we design an Ethernet communication board to support an array of mobile system-on-chips.Second,we propose a two-tier software architecture for Chameleon to make it flexible.Third,we devise data,configuration,and control planes for Chameleon to make it“software-defined”and in turn consume hardware resources on demand.Fourth,we design an accurate synthetic metric that represents the computational power of a computing node.We employ 12 Apache Spark benchmarks to evaluate SOCA-DOM.Surprisingly,SOCA-DOM consumes up to 9.4x less CPU resources and 13.5x less memory than Mesos which is an existing resource manager.In addition,we show that a 16-node SOCA-DOM consumes up to 4x less energy than two standard Xeon servers.Based on the results,we conclude that an array architecture with fine-grained hardware resources and a software-defined resource manager works well for analyzing big data on the move.

关键词： edge computing mobile architecture resource management big data analytics software-defined systems