This paper focuses on facilitating state-of-the-art applications of big data analytics(BDA) architectures and infrastructures to telecommunications(telecom) industrial sector.Telecom companies are dealing with terabyt...This paper focuses on facilitating state-of-the-art applications of big data analytics(BDA) architectures and infrastructures to telecommunications(telecom) industrial sector.Telecom companies are dealing with terabytes to petabytes of data on a daily basis. Io T applications in telecom are further contributing to this data deluge. Recent advances in BDA have exposed new opportunities to get actionable insights from telecom big data. These benefits and the fast-changing BDA technology landscape make it important to investigate existing BDA applications to telecom sector. For this, we initially determine published research on BDA applications to telecom through a systematic literature review through which we filter 38 articles and categorize them in frameworks, use cases, literature reviews, white papers and experimental validations. We also discuss the benefits and challenges mentioned in these articles. We find that experiments are all proof of concepts(POC) on a severely limited BDA technology stack(as compared to the available technology stack), i.e.,we did not find any work focusing on full-fledged BDA implementation in an operational telecom environment. To facilitate these applications at research-level, we propose a state-of-the-art lambda architecture for BDA pipeline implementation(called Lambda Tel) based completely on open source BDA technologies and the standard Python language, along with relevant guidelines.We discovered only one research paper which presented a relatively-limited lambda architecture using the proprietary AWS cloud infrastructure. We believe Lambda Tel presents a clear roadmap for telecom industry practitioners to implement and enhance BDA applications in their enterprises.展开更多
The advent of healthcare information management systems(HIMSs)continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale.Analysis of this big da...The advent of healthcare information management systems(HIMSs)continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale.Analysis of this big data allows for boundless potential outcomes for discovering knowledge.Big data analytics(BDA)in healthcare can,for instance,help determine causes of diseases,generate effective diagnoses,enhance Qo S guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments,generate accurate predictions of readmissions,enhance clinical care,and pinpoint opportunities for cost savings.However,BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the practitioners.In this paper,we present a comprehensive roadmap to derive insights from BDA in the healthcare(patient care)domain,based on the results of a systematic literature review.We initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on No SQL databases.We also identify the limitations and challenges of these applications and justify the potential of No SQL databases to address these challenges and further enhance BDA healthcare research.We then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data paradigm.We also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to healthcare.Finally,we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our work.The aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators,practitioners and professionals to successfully implement BDA initiatives in their organizations.展开更多
Big data analytics(BDA)in e-commerce,which is an emerging field that started in 2006,deeply affects the development of global e-commerce,especially its layout and performance in the U.S.and China.This paper seeks to e...Big data analytics(BDA)in e-commerce,which is an emerging field that started in 2006,deeply affects the development of global e-commerce,especially its layout and performance in the U.S.and China.This paper seeks to examine the relative influence of theoretical research of BDA in e-commerce to explain the differences between the U.S.and China by adopting a statistical analysis method on the basis of samples collected from two main literature databases,Web of Science and CNKI,aimed at the U.S.and China.The results of this study help clarify doubts regarding the development of China's e-commerce,which exceeds that of the U.S.today,in view of the theoretical comparison of BDA in e-commerce between them.展开更多
中国是世界上水土流失最为严重的国家之一。准确掌握既有水土流失研究的空间分布格局是一项基础性工作。以中国知网学术期刊数据库作为数据源,应用自然语言处理方法,对1980-2017年中国水土流失研究地区进行了地名信息提取及研究热点建模...中国是世界上水土流失最为严重的国家之一。准确掌握既有水土流失研究的空间分布格局是一项基础性工作。以中国知网学术期刊数据库作为数据源,应用自然语言处理方法,对1980-2017年中国水土流失研究地区进行了地名信息提取及研究热点建模;继而应用RUSLE模型模拟,得到全国土壤侵蚀强度的空间分布;在上述研究基础上,对研究热点地区与侵蚀强度之间的空间耦合关系进行了对比分析。结果表明:(1)1980年以来,中国水土流失研究热点区主要分布在黄土高原及贵州高原,涉及陕西、宁夏、内蒙古、甘肃、贵州以及黑龙江等省区;中等及以上热度的县(区、市)共171个,占全国国土总面积的5.33%。(2)RUSLE模型模拟表明,严重的土壤侵蚀主要分布在黄土高原及云贵高原,涉及陕西、宁夏、甘肃、山西、贵州、云南、四川等省区;侵蚀模数大于20 t hm-2 a-1的县(区、市)共251个,占全国国土总面积的7.04%。(3)研究热点地图与水土流失强度模型模拟地图之间存在空间差异。对特定空间耦合模式的分析有助于判断科研资源配置的合理性。展开更多
基金supported in part by the Big Data Analytics Laboratory(BDALAB)at the Institute of Business Administration under the research grant approved by the Higher Education Commission of Pakistan(www.hec.gov.pk)the Darbi company(www.darbi.io)
文摘This paper focuses on facilitating state-of-the-art applications of big data analytics(BDA) architectures and infrastructures to telecommunications(telecom) industrial sector.Telecom companies are dealing with terabytes to petabytes of data on a daily basis. Io T applications in telecom are further contributing to this data deluge. Recent advances in BDA have exposed new opportunities to get actionable insights from telecom big data. These benefits and the fast-changing BDA technology landscape make it important to investigate existing BDA applications to telecom sector. For this, we initially determine published research on BDA applications to telecom through a systematic literature review through which we filter 38 articles and categorize them in frameworks, use cases, literature reviews, white papers and experimental validations. We also discuss the benefits and challenges mentioned in these articles. We find that experiments are all proof of concepts(POC) on a severely limited BDA technology stack(as compared to the available technology stack), i.e.,we did not find any work focusing on full-fledged BDA implementation in an operational telecom environment. To facilitate these applications at research-level, we propose a state-of-the-art lambda architecture for BDA pipeline implementation(called Lambda Tel) based completely on open source BDA technologies and the standard Python language, along with relevant guidelines.We discovered only one research paper which presented a relatively-limited lambda architecture using the proprietary AWS cloud infrastructure. We believe Lambda Tel presents a clear roadmap for telecom industry practitioners to implement and enhance BDA applications in their enterprises.
基金supported by two research grants provided by the Karachi Institute of Economics and Technology(KIET)the Big Data Analytics Laboratory at the Insitute of Business Administration(IBAKarachi)。
文摘The advent of healthcare information management systems(HIMSs)continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale.Analysis of this big data allows for boundless potential outcomes for discovering knowledge.Big data analytics(BDA)in healthcare can,for instance,help determine causes of diseases,generate effective diagnoses,enhance Qo S guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments,generate accurate predictions of readmissions,enhance clinical care,and pinpoint opportunities for cost savings.However,BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the practitioners.In this paper,we present a comprehensive roadmap to derive insights from BDA in the healthcare(patient care)domain,based on the results of a systematic literature review.We initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on No SQL databases.We also identify the limitations and challenges of these applications and justify the potential of No SQL databases to address these challenges and further enhance BDA healthcare research.We then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data paradigm.We also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to healthcare.Finally,we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our work.The aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators,practitioners and professionals to successfully implement BDA initiatives in their organizations.
基金Supported by the Ministry of Education’s Humanities and Social Sciences Research Project(18YJAZH153)Fujian Natural Science Foundation(2018J01648)+1 种基金Fujian Social Sciences Federation Planning Project(FJ2018B032)Development Fund of Scientific Research from Fujian University of Technology(GY-S18109)。
文摘Big data analytics(BDA)in e-commerce,which is an emerging field that started in 2006,deeply affects the development of global e-commerce,especially its layout and performance in the U.S.and China.This paper seeks to examine the relative influence of theoretical research of BDA in e-commerce to explain the differences between the U.S.and China by adopting a statistical analysis method on the basis of samples collected from two main literature databases,Web of Science and CNKI,aimed at the U.S.and China.The results of this study help clarify doubts regarding the development of China's e-commerce,which exceeds that of the U.S.today,in view of the theoretical comparison of BDA in e-commerce between them.
文摘中国是世界上水土流失最为严重的国家之一。准确掌握既有水土流失研究的空间分布格局是一项基础性工作。以中国知网学术期刊数据库作为数据源,应用自然语言处理方法,对1980-2017年中国水土流失研究地区进行了地名信息提取及研究热点建模;继而应用RUSLE模型模拟,得到全国土壤侵蚀强度的空间分布;在上述研究基础上,对研究热点地区与侵蚀强度之间的空间耦合关系进行了对比分析。结果表明:(1)1980年以来,中国水土流失研究热点区主要分布在黄土高原及贵州高原,涉及陕西、宁夏、内蒙古、甘肃、贵州以及黑龙江等省区;中等及以上热度的县(区、市)共171个,占全国国土总面积的5.33%。(2)RUSLE模型模拟表明,严重的土壤侵蚀主要分布在黄土高原及云贵高原,涉及陕西、宁夏、甘肃、山西、贵州、云南、四川等省区;侵蚀模数大于20 t hm-2 a-1的县(区、市)共251个,占全国国土总面积的7.04%。(3)研究热点地图与水土流失强度模型模拟地图之间存在空间差异。对特定空间耦合模式的分析有助于判断科研资源配置的合理性。
