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1.
Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an enormous amount of data and provides a new opportunity that is helpful in relevant decision-making. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven V's characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and technologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future directions of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-creation; and (iii) the current challenges of Big Data Analytics as well as possible future directions. 相似文献
2.
Over recent years, organizations have started to capitalize on the significant use of Big Data and emerging technologies to analyze, and gain valuable insights linked to, decision-making processes. The process of Competitive Intelligence (CI) includes monitoring competitors with a view to delivering both actionable and meaningful intelligence to organizations. In this regard, the capacity to leverage and unleash the potential of big data tools and techniques is one of various significant components of successfully steering CI and ultimately infusing such valuable knowledge into CI strategies. In this paper, the authors aim to examine Big Data applications in CI processes within organizations by exploring how organizations deal with Big Data analytics, and this study provides a context for developing Big Data frameworks and process models for CI in organizations. Overall, research findings have indicated a preference for a rather centralized informal process as opposed to a clear formal structure for CI; the use of basic tools for queries, as opposed to reliance on dedicated methods such as advanced machine learning; and the existence of multiple challenges that companies currently face regarding the use of big data analytics in building organizational CI. 相似文献
3.
《The Journal of High Technology Management Research》2001,12(2):167-181
This paper presents a study that found no support for the claim: “Employees with multiple skills enable organizations to thrive in dynamically changing and unpredictable environments.” The study showed that the “multi-skilled worker” (MSW) was a non-significant predictor of financial performance in a statistical analysis of companies that operated in these environments. A sample of companies drawn from three high-technology industries (suppliers to the automotive industry, electronic instrumentation, and semiconductor manufacturers) showed no relationship between employee skill diversity and financial performance. As a result, it appears that the benefits of a multi-skilled workforce may be overstated in terms of its contribution to the organization's financial performance. Or, it may simply suggest that the additional profits generated by responsive, multi-skilled employees are insufficient to offset the additional costs associated with training and hiring them. Companies should be aware of these issues when considering plans to expand employee skill sets as part of a strategy for improving responsiveness. 相似文献
4.
The Skills Framework for the Information Age (SFIA) is a popular international skills framework for the Information and Communications Technology (ICT) sector for which version 7 was released in June 2018. This paper provides an overview of this most recent version of the framework and compares it to the previous version, version 6. Some potential issues with the framework are then discussed, perhaps the most important of which is that version 7 is not backwards compatible with version 6, which can lead to undesirable results when two users of the framework (e.g. an employer and job applicant) interact with one using version 6 and the other using version 7. Other issues examined are the lack of universal certification criteria for objective assessment of skills (which affects the portability of the framework between users), the complexity of the framework in terms of skill/proficiency mapping, the representation of soft or transferable skills and the limited scope for automating skill management tasks. Some solutions to these issues are offered, including structuring the skill definitions to include mappings between different SFIA versions and creating mappings to recognised formal qualifications, industry certifications and job experience. 相似文献
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6.
数据挖掘技术中的分类和回归树（classmcationAndRegressionTree,CART）节点是一种基于树的分类预测方法,使用递归分区来将训练记录分割类似的输出字段值。文章将C＆R树应用于市场营销研究,目标是寻找意愿接受互动新闻服务并购买的潜在客户。通过使用已有的客P数据作为训练样本,建立了一个分类回归模型,可以用于未来数据的预测,从而有助于企业更好地针对不同类型的客户进行不同的营销策略。 相似文献
7.
《Information processing & management》2020,57(1):102122
Big data generated by social media stands for a valuable source of information, which offers an excellent opportunity to mine valuable insights. Particularly, User-generated contents such as reviews, recommendations, and users’ behavior data are useful for supporting several marketing activities of many companies. Knowing what users are saying about the products they bought or the services they used through reviews in social media represents a key factor for making decisions. Sentiment analysis is one of the fundamental tasks in Natural Language Processing. Although deep learning for sentiment analysis has achieved great success and allowed several firms to analyze and extract relevant information from their textual data, but as the volume of data grows, a model that runs in a traditional environment cannot be effective, which implies the importance of efficient distributed deep learning models for social Big Data analytics. Besides, it is known that social media analysis is a complex process, which involves a set of complex tasks. Therefore, it is important to address the challenges and issues of social big data analytics and enhance the performance of deep learning techniques in terms of classification accuracy to obtain better decisions.In this paper, we propose an approach for sentiment analysis, which is devoted to adopting fastText with Recurrent neural network variants to represent textual data efficiently. Then, it employs the new representations to perform the classification task. Its main objective is to enhance the performance of well-known Recurrent Neural Network (RNN) variants in terms of classification accuracy and handle large scale data. In addition, we propose a distributed intelligent system for real-time social big data analytics. It is designed to ingest, store, process, index, and visualize the huge amount of information in real-time. The proposed system adopts distributed machine learning with our proposed method for enhancing decision-making processes. Extensive experiments conducted on two benchmark data sets demonstrate that our proposal for sentiment analysis outperforms well-known distributed recurrent neural network variants (i.e., Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)). Specifically, we tested the efficiency of our approach using the three different deep learning models. The results show that our proposed approach is able to enhance the performance of the three models. The current work can provide several benefits for researchers and practitioners who want to collect, handle, analyze and visualize several sources of information in real-time. Also, it can contribute to a better understanding of public opinion and user behaviors using our proposed system with the improved variants of the most powerful distributed deep learning and machine learning algorithms. Furthermore, it is able to increase the classification accuracy of several existing works based on RNN models for sentiment analysis. 相似文献
8.
本文通过对招聘HR从业者的招聘广告进行统计,对从事不同岗位工作的HR从业者的要求性指标进行分析,结果显示出,HR从业者都要求具备较高的学历,经验,专业知识,沟通能力及个性要求。但不同的岗位从业者有个自的特点,招聘专员要求较高的渠道拓展能力,薪酬福利专员要求较高的统计知识,绩效考核专员要求较高的分析判断能力,培训专员要求较高的表达能力和课程开发能力。由此,可以为有志于从事不同岗位的HR从业者指明能力培养的方向,为企业招聘不同岗位的HR从业者提供一定的借鉴,为在职者完善自身能力提供一定的参考,同时,也希望能在人力资源管理的专业化道路上起到抛砖引玉的作用。 相似文献
9.
《Information processing & management》2022,59(5):103009
The rapid development of online social media makes Abusive Language Detection (ALD) a hot topic in the field of affective computing. However, most methods for ALD in social networks do not take into account the interactive relationships among user posts, which simply regard ALD as a task of text context representation learning. To solve this problem, we propose a pipeline approach that considers both the context of a post and the characteristics of interaction network in which it is posted. Specifically, our method is divided into pre-training and downstream tasks. First, to capture fine contextual features of the posts, we use Bidirectional Encoder Representation from Transformers (BERT) as Encoder to generate sentence representations. Later, we build a Relation-Special Network according to the semantic similarity between posts as well as the interaction network structural information. On this basis, we design a Relation-Special Graph Neural Network (RSGNN) to spread effective information in the interaction network and learn the classification of texts. The experiment proves that our method can effectively improve the detection effect of abusive posts over three public datasets. The results demonstrate that injecting interaction network structure into the abusive language detection task can significantly improve the detection results. 相似文献
10.
Pasquale Del Vecchio Gioconda Mele Valentina Ndou Giustina Secundo 《Information processing & management》2018,54(5):847-860
This paper aims to demonstrate how the huge amount of Social Big Data available from tourists can nurture the value creation process for a Smart Tourism Destination. Applying a multiple-case study analysis, the paper explores a set of regional tourist experiences related to a Southern European region and destination, to derive patterns and opportunities of value creation generated by Big Data in tourism. Findings present and discuss evidence in terms of improving decision-making, creating marketing strategies with more personalized offerings, transparency and trust in dialogue with customers and stakeholders, and emergence of new business models. Finally, implications are presented for researchers and practitioners interested in the managerial exploitation of Big Data in the context of information-intensive industries and mainly in Tourism. 相似文献
11.
大数据即网络社会中的巨量资料,在经过技术处理之后能为人们创造价值提供分析和预测的生产要素和信息资源。大数据时代推动着教育的理念革命、方法革命和行为革命,推动着思想政治教育的大数据化。思想政治教育的大数据化,是人们运用信息网络技术来把大数据与思想政治教育相结合,获取、处理和利用思想政治教育领域以及与之相关的巨量资料,分析和预测思想政治教育的现状和态势,推动思想政治教育网络的创新和应用,产生巨大的思想政治教育价值。这需要构建思想政治教育网络,其实质就在于从价值理性、“互联网+”、应用结构、战略管理的具体层面来构建思联网。 相似文献
12.
海洋生态系统的健康和安全,直接关系到全人类的健康和福祉。有效监测数据不足、科学决策信息缺失等因素一定程度上影响了海洋可持续发展目标(SDG 14)的顺利实施。地球大数据具备宏观、动态、客观监测能力,可在支撑SDG 14实现中起到重要作用。在中国科学院战略性先导科技专项(A类)的支持下,基于地球大数据相关技术和方法,我国已有效开展了海洋缺失数据集生产、目标本地化模型构建等具体实践。在以上分析基础上,文章提出了积极参与国际社会地球大数据共享,加强科技创新对SDG 14实现的驱动,深度参与联合国海洋治理计划等建议。 相似文献
13.
关系型求职对农民工工作不安全感的影响:社会资本的调节作用 总被引：2，自引：0，他引：2
基于社会网络理论,研究在缺乏有效劳动力市场机制的情况下,农民工的关系型求职对其工作不安全感的影响关系,重点关注农民工个体社会资本的调节作用。采用问卷调查的方法,共收集到14家建筑施工企业305个有效样本。研究发现:农民工的关系型求职对其工作不安全感具有显著的负向影响关系;农民工个体社会资本(网络规模、网络成分和网络资源)对其关系型求职与工作不安全感之间的关系具有显著的调节作用,与拥有较低社会资本的农民工相比,拥有较高社会资本的农民工其关系型求职对工作不安全感才具有显著影响关系。最后,针对研究结论的启示和意义进行了探讨。 相似文献
14.
《The Journal of High Technology Management Research》2023,34(2):100465
Businesses have begun using IT apps for a variety of reasons in recent years. The rapid advancement of new technologies has opened up vast prospects for businesses to digitise their operations, enhance their use of information systems, and compete more effectively in the global marketplace. Information technology (IT) businesses can benefit greatly from Big Data analytics due to the depth and breadth of their data analysis. Big data can be used to examine IT departments in the following ways: performance analysis, forecast maintenance, security analysis, and resource analysis. When it comes to boosting their business's dependability, speed, quality, and effectiveness, most companies rely on big data. Companies can gain a competitive edge thanks to the massive amounts of data that big data is able to collect, store, and manage. Big data analytics is being used by a growing number of businesses to make sense of their mountain of data. In this paper, we examine the ways in which IBM, TCS, and Cognizant use big data within their operations. Long-term planning strategies and business intelligence practises are also suggested in this research as means of protecting personal information. 相似文献
15.
This paper addresses the blog distillation problem, that is, given a user query find the blogs that are most related to the query topic. We model each post as evidence of the relevance of a blog to the query, and use aggregation methods like Ordered Weighted Averaging (OWA) operators to combine the evidence. We show that using only highly relevant evidence (posts) for each blog can result in an effective retrieval system. We also take into account the importance of the posts in a query-based cluster and investigate its effect in the aggregation results. We use prioritized OWA operators and show that considering the importance is effective when the number of aggregated posts from each blog is high. We carry out our experiments on three different data sets (TREC07, TREC08 and TREC09) and show statistically significant improvements over state of the art model called voting model. 相似文献
16.
大数据在地球科学各个学科中的应用越来越受到关注,数据驱动地球科学发现的案例不断出现,有关地球数据信息中心、地球大数据平台及相关学术会议数量逐渐增加,地球大数据正在科学研究上表现出巨大的潜力。科学家对地球大数据的科学方法和工具的需求很大,然而目前地球大数据的理论基础、储存管理和分析方法等仍处于发展之中,对地球大数据的研究和讨论有限。文章通过文献计量学的方法,对科学引文索引(SCI)和社会科学引文索引(SSCI)收录的地球大数据相关文献进行分析,从全球论文的产出数量、国家与机构领域研究影响力、研究主题分布、研究热点变迁和国际合作等多角度,分析揭示了地球大数据研究现状;最后,建议未来重点加强跨学科的地球大数据共享与融合,完善地球科学大数据深度挖掘理论和方法,实现对复杂地球系统的分析、建模与预测,支持和服务全球变化与可持续发展。 相似文献
17.
在社会化媒体时代,如何在创新社区的海量数据环境中识别出领先用户,是企业从创新社区中获取价值的关键问题。本文从语言风格的视角出发,首先通过内容分析法探索了创新社区中领先用户表达的典型语言风格特征,即成就需求、未来导向、积极情绪和集体主义。其次,收集了355名创新社区用户所发表的47310篇帖子,利用自动文本分析方法对其中包含的积极情绪、集体主义和未来导向语言风格进行了测量,并验证了这四种语言风格与用户领先性的关系。研究结果表明,创新社区用户生成内容中所表现的语言风格起到了“信号”的作用：除了集体主义之外,成就需求、未来导向和积极情绪的语言风格都与用户领先性有显著的正向关系,可以作为识别领先用户的有效指标。最后,讨论了基于语言风格的领先用户识别机制的理论意义和实践价值。 相似文献
18.
【目的/意义】在线社交平台是用户自我表露健康相关信息的重要渠道,通过内容分析,能了解其在面对重
大疾病压力时,倾向表露的信息需求,有针对性地为其提供帮助。【方法/过程】以 2019-2020 年新浪微博中肺癌患
者及家属在肺癌超话和个人微博发布博文为数据源,对筛选后数据进行编码,并对编码结果向量化,通过聚类计
算得到肺癌患者及家属的不同自我表露模式。【结果/结论】男女用户表露个人信息存在差异;患者及家属自我表
露不同类型的信息存在差异;患者及家属的自我表露模式：单描述性自我表露和描述性-评价性自我表露两种;患
者及家属在癌症发展不同阶段自我表露目的不同。【创新/局限】研究探索自我表露健康相关信息的类型、模式、目
的、情感倾向等,为健康信息行为研究提供新视域。受到数据量限制,同时模式归纳仅从内容角度出发,有效性需
进一步验证。 相似文献
19.
保障粮食安全是全球可持续发展的基础及重要议题。粮食可持续生产作为实现粮食安全的基础,同时是应对气候变化、土地退化、生态退化等全球挑战的有效手段。当前,对粮食生产可持续性的监测与评估存在着数据鸿沟,而地球大数据的支撑作用日益凸显。文章总结了地球大数据支撑粮食可持续生产研究的当前实践,包括对地观测技术在粮食生产系统各要素监测中发挥的作用,以及多源数据融合在粮食生产系统综合监测及粮食生产可持续性评估中的应用。在上述实践归纳的基础上,依循实现联合国可持续发展目标(SDGs)的四大杠杆框架,提出了地球大数据支撑粮食可持续生产的2个未来发展方向:多学科模型凝聚地球大数据推动知识发现支撑政府治理;技术创新集成地球大数据搭建产农户智慧生产决策体系。 相似文献
20.
针对层次分析法(AHP)和变异系数法(CV)的单一赋权方法在高新技术企业竞争力评估中的不足,提出一套综合权重赋值方法称为AHP-CV。首先从广东省科技大数据平台中获取高新技术企业的登记数据集,根据预设维度分类规则分类至预设企业维度,然后基于AHP-CV设计指标权重算法模型计算各企业维度对应的评分值,最后从3个企业维度对不同领域高新技术企业进行归类分析,根据归类结果对高新技术企业的发展状况进行评估,设计能够实时监测高新技术企业综合竞争力的监控系统。实证分析表明此监测算法模型能够很好地对高新技术企业进行监测。基于该监测算法的监控系统已经成功运用于广东省科技大数据平台,将实时对广东省内高新技术企业的综合竞争力进行监测,为各级有关监管部门决策提供参考。 相似文献