Manufacturing big data ecosystem: A systematic literature review

Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business. Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware. Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security. Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to real-time big data analytics and cybersecurity.     

大数据分析中的计算智能研究现状与展望

随着产业界和科学界数据量的爆炸式增长,大数据技术和应用吸引了众多的关注.如何分析大数据,充分挖掘大数据的潜在价值,成为需要深入探讨的科学问题.计算智能是科学研究和工程实践中解决复杂问题的有效手段,是人工智能和信息科学的重要研究方向,应用计算智能方法进行大数据分析具有巨大的潜力.对大数据分析中的计算智能方法进行综述,结合大数据的特征,讨论了大数据分析中计算智能研究存在的问题和进一步的研究方向,阐述了数据源共享问题,并建议利用以天文学为代表的数据密集型基础科研领域的数据开展大数据分析研究.     

A survey on information visualization: recent advances and challenges

Information visualization (InfoVis), the study of transforming data, information, and knowledge into interactive visual representations, is very important to users because it provides mental models of information. The boom in big data analytics has triggered broad use of InfoVis in a variety of domains, ranging from finance to sports to politics. In this paper, we present a comprehensive survey and key insights into this fast-rising area. The research on InfoVis is organized into a taxonomy that contains four main categories, namely empirical methodologies, user interactions, visualization frameworks, and applications, which are each described in terms of their major goals, fundamental principles, recent trends, and state-of-the-art approaches. At the conclusion of this survey, we identify existing technical challenges and propose directions for future research.     

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

The analysis of ocean and atmospheric datasets offers a unique set of challenges to scientists working in different application areas. These challenges include dealing with extremely large volumes of multidimensional data, supporting interactive visual analysis, ensembles exploration and visualization, exploring model sensitivities to inputs, mesoscale ocean features analysis, predictive analytics, heterogeneity and complexity of observational data, representing uncertainty, and many more. Researchers across disciplines collaborate to address such challenges, which led to significant research and development advances in ocean and atmospheric sciences, and also in several relevant areas such as visualization and visual analytics, big data analytics, machine learning and statistics. In this report, we perform an extensive survey of research advances in the visual analysis of ocean and atmospheric datasets. First, we survey the task requirements by conducting interviews with researchers, domain experts, and end users working with these datasets on a spectrum of analytics problems in the domain of ocean and atmospheric sciences. We then discuss existing models and frameworks related to data analysis, sense‐making, and knowledge discovery for visual analytics applications. We categorize the techniques, systems, and tools presented in the literature based on the taxonomies of task requirements, interaction methods, visualization techniques, machine learning and statistical methods, evaluation methods, data types, data dimensions and size, spatial scale and application areas. We then evaluate the task requirements identified based on our interviews with domain experts in the context of categorized research based on our taxonomies, and existing models and frameworks of visual analytics to determine the extent to which they fulfill these task requirements, and identify the gaps in current research. In the last part of this report, we summarize the trends, challenges, and opportunities for future research in this area. (see http://www.acm.org/about/class/class/2012 )     

Big Data analytics for sustainability: Insight through technological innovation

How do information systems and big data analytics help to enable a sustainable future? This question is investigated in nine papers in this special issue that examine the issue of big data analytics for sustainability from a variety of perspectives. Broadly, these papers can be considered in four main areas: health, online behavior and consumption, safety and the environment, and methods to improve understanding of sustainability issues. Recent advances in data-driven decision-making analytics research focusing on different aspects of sustainability are discussed in these papers, including air pollution management, online health consultation services, gamification of exercise and health, sustainable urban mobility, the sustainable use of resources in hospitals, the design of anticrime information support systems, the interdependence effects among mobile social apps, networks of sustainable development goals, and the spillover effect of sustainable consumption.     

A survey on big data-driven digital phenotyping of mental health

The landscape of mental health has undergone tremendous changes within the last two decades, but the research on mental health is still at the initial stage with substantial knowledge gaps and the lack of precise diagnosis. Nowadays, big data and artificial intelligence offer new opportunities for the screening and prediction of mental problems. In this review paper, we outline the vision of digital phenotyping of mental health (DPMH) by fusing the enriched data from ubiquitous sensors, social media and healthcare systems, and present a broad overview of DPMH from sensing and computing perspectives. We first conduct a systematical literature review and propose the research framework, which highlights the key aspects related with mental health, and discuss the challenges elicited by the enriched data for digital phenotyping. Next, five key research strands including affect recognition, cognitive analytics, behavioral anomaly detection, social analytics, and biomarker analytics are unfolded in the psychiatric context. Finally, we discuss various open issues and the corresponding solutions to underpin the digital phenotyping of mental health.     

Big Data Analytics Services for Enhancing Business Intelligence

This article examines how to use big data analytics services to enhance business intelligence (BI). More specifically, this article proposes an ontology of big data analytics and presents a big data analytics service-oriented architecture (BASOA), and then applies BASOA to BI, where our surveyed data analysis shows that the proposed BASOA is viable for enhancing BI and enterprise information systems. This article also explores temporality, expectability, and relativity as the characteristics of intelligence in BI. These characteristics are what customers and decision makers expect from BI in terms of systems, products, and services of organizations. The proposed approach in this article might facilitate the research and development of business analytics, big data analytics, and BI as well as big data science and big data computing.     

VA + Embeddings STAR: A State-of-the-Art Report on the Use of Embeddings in Visual Analytics

Over the past years, an increasing number of publications in information visualization, especially within the field of visual analytics, have mentioned the term “embedding” when describing the computational approach. Within this context, embeddings are usually (relatively) low-dimensional, distributed representations of various data types (such as texts or graphs), and since they have proven to be extremely useful for a variety of data analysis tasks across various disciplines and fields, they have become widely used. Existing visualization approaches aim to either support exploration and interpretation of the embedding space through visual representation and interaction, or aim to use embeddings as part of the computational pipeline for addressing downstream analytical tasks. To the best of our knowledge, this is the first survey that takes a detailed look at embedding methods through the lens of visual analytics, and the purpose of our survey article is to provide a systematic overview of the state of the art within the emerging field of embedding visualization. We design a categorization scheme for our approach, analyze the current research frontier based on peer-reviewed publications, and discuss existing trends, challenges, and potential research directions for using embeddings in the context of visual analytics. Furthermore, we provide an interactive survey browser for the collected and categorized survey data, which currently includes 122 entries that appeared between 2007 and 2023.     

How big data analytics enables the alliance relationship stability of contract farming in the age of digital transformation

Notwithstanding the potential of big data analytics technology for alliance management, there is a lack of understanding of how such digital technology influences alliance relationship stability (ARS). Drawing on the information technology-enabled organizational capabilities (IT-enabled OCs) perspective, this study empirically verifies that big data analytics promotes ARS and risk management capability. Moreover, market risk management capability (MRM) enhances ARS, and data quality moderates the relationship between big data analytics usage (BDU) and MRM. This research reveals the impact mechanism of BDU on the ARS. Implications for management and future research are presented as well.     

Big data analytics capabilities: a systematic literature review and research agenda

With big data growing rapidly in importance over the past few years, academics and practitioners have been considering the means through which they can incorporate the shifts these technologies bring into their competitive strategies. To date, emphasis has been on the technical aspects of big data, with limited attention paid to the organizational changes they entail and how they should be leveraged strategically. As with any novel technology, it is important to understand the mechanisms and processes through which big data can add business value to companies, and to have a clear picture of the different elements and their interdependencies. To this end, the present paper aims to provide a systematic literature review that can help to explain the mechanisms through which big data analytics (BDA) lead to competitive performance gains. The research framework is grounded on past empirical work on IT business value research, and builds on the resource-based view and dynamic capabilities view of the firm. By identifying the main areas of focus for BDA and explaining the mechanisms through which they should be leveraged, this paper attempts to add to literature on how big data should be examined as a source of competitive advantage. To this end, we identify gaps in the extant literature and propose six future research themes.     

工业过程故障根源诊断与传播路径识别技术综述EI北大核心CSCD

故障根源诊断与传播路径识别是故障诊断框架下的关键核心问题,是保障工业过程安全生产及获得可靠产品质量的有效手段,是当前过程控制领域的研究热点.该技术的研究不仅丰富了故障诊断理论,而且对故障诊断技术在工程中的推广与应用具有重要意义.阐述了基于知识、数据及知识与数据联合驱动的故障根源诊断与传播路径识别方法的基本思想、适用条件和优劣特点,分类概述了相关方法的研究现状.探讨了该领域亟待解决的问题及未来的发展方向,包括:1)“三个维度”视角下的工业过程故障根源诊断与传播路径识别;2)基于制造大数据分析与因果关系挖掘的工业过程质量精准追溯;3)面向传播、耦合、多重并发特性的工业过程复合故障分布式诊断;4)基于多源异构动态信息融合的工业过程异常工况时空追溯可视化.     

Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies

The digitalization process and its outcomes in the 21st century accelerate transformation and the creation of sustainable societies. Our decisions, actions and even existence in the digital world generate data, which offer tremendous opportunities for revising current business methods and practices, thus there is a critical need for novel theories embracing big data analytics ecosystems. Building upon the rapidly developing research on digital technologies and the strengths that information systems discipline brings in the area, we conceptualize big data and business analytics ecosystems and propose a model that portraits how big data and business analytics ecosystems can pave the way towards digital transformation and sustainable societies, that is the Digital Transformation and Sustainability (DTS) model. This editorial discusses that in order to reach digital transformation and the creation of sustainable societies, first, none of the actors in the society can be seen in isolation, instead we need to improve our understanding of their interactions and interrelations that lead to knowledge, innovation, and value creation. Second, we gain deeper insight on which capabilities need to be developed to harness the potential of big data analytics. Our suggestions in this paper, coupled with the five research contributions included in the special issue, seek to offer a broader foundation for paving the way towards digital transformation and sustainable societies     

Business value of big data analytics: A systems-theoretic approach and empirical test

Although big data analytics have been widely considered a key driver of marketing and innovation processes, whether and how big data analytics create business value has not been fully understood and empirically validated at a large scale. Taking social media analytics as an example, this paper is among the first attempts to theoretically explain and empirically test the market performance impact of big data analytics. Drawing on the systems theory, we explain how and why social media analytics create super-additive value through the synergies in functional complementarity between social media diversity for gathering big data from diverse social media channels and big data analytics for analyzing the gathered big data. Furthermore, we deepen our theorizing by considering the difference between small and medium enterprises (SMEs) and large firms in the required integration effort that enables the synergies of social media diversity and big data analytics. In line with this theorizing, we empirically test the synergistic effect of social media diversity and big data analytics by using a recent large-scale survey data set from 18,816 firms in Italy. We find that social media diversity and big data analytics have a positive interaction effect on market performance, which is more salient for SMEs than for large firms.     

面向学科题目的文本分析方法与应用研究综述

分析学科题目含义、模拟人类解决问题,是当前“人工智能+教育”融合研究的重要方向之一。近年来,智能教育系统的快速发展积累了大量学科题目资源,为相关研究提供了数据支撑。为此,利用大数据分析与自然语言处理相关的技术,研究者提出了大量面向学科题目的文本分析方法,开展了许多重要的智能应用任务,对探索人类知识学习等认知能力具有重要意义。该文围绕智能教育与自然语言处理交叉领域,介绍了若干代表性研究任务,包括题目质量分析、机器阅读理解、数学题问答、文章自主评分等,并对相应研究进展进行阐述和总结;此外,对相关数据集和开源工具包进行了总结和介绍;最后,展望了多个未来研究方向。     

Business Analytics-Based Enterprise Information Systems

Big data analytics and business analytics are a disruptive technology and innovative solution for enterprise development. However, what is the relationship between business analytics, big data analytics, and enterprise information systems (EIS)? How can business analytics enhance the development of EIS? How can analytics be incorporated into EIS? These are still big issues. This article addresses these three issues by proposing ontology of business analytics, presenting an analytics service-oriented architecture (ASOA) and applying ASOA to EIS, where our surveyed data analysis showed that the proposed ASOA is viable for developing EIS. This article then examines incorporation of business analytics into EIS through proposing a model for business analytics service-based EIS, or ASEIS for short. The proposed approach in this article might facilitate the research and development of EIS, business analytics, big data analytics, and business intelligence.     

A survey on dynamic graph processing on GPUs: concepts,terminologies and systems

Graphs that are used to model real-world entities with vertices and relationships among entities with edges, have proven to be a powerful tool for describing real-world problems in applications. In most real-world scenarios, entities and their relationships are subject to constant changes. Graphs that record such changes are called dynamic graphs. In recent years, the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics results. As the scale of dynamic graphs becomes larger, higher performance requirements are demanded to dynamic graph processing systems. With the massive parallel processing power and high memory bandwidth, GPUs become mainstream vehicles to accelerate dynamic graph processing tasks. GPU-based dynamic graph processing systems mainly address two challenges: maintaining the graph data when updates occur (i.e., graph updating) and producing analytics results in time (i.e., graph computing). In this paper, we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph computing. To comprehensively discuss existing dynamic graph processing systems on GPUs, we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph computing. In addition, we discuss the challenges and future research directions of dynamic graph processing on GPUs.     

Big Data

“Big data” describes technologies that promise to fulfill a fundamental tenet of research in information systems, which is to provide the right information to the right receiver in the right volume and quality at the right time. For information systems research as an application-oriented research discipline, opportunities and risks arise from using big data. Risks arise primarily from the considerable number of resources used for the explanation and design of fads. Opportunities arise because these resources lead to substantial knowledge gains, which support scientific progress within the discipline and are of relevance to practice as well. From the authors’ perspective, information systems research is ideally positioned to support big data critically and use the knowledge gained to explain and design innovative information systems in business and administration – regardless of whether big data is in reality a disruptive technology or a cursory fad. The continuing development and adoption of big data will ultimately provide clarity on whether big data is a fad or if it represents substantial progress in information systems research. Three theses also show how future technological developments can be used to advance the discipline of information systems. Technological progress should be used for a cumulative supplement of existing models, tools, and methods. By contrast, scientific revolutions are independent of technological progress.     

学习分析技术在大数据环境中的应用

学习分析是大数据在教育应用中的焦点,本文对学习分析的核心环节进行技术剖析,梳理主要的学习分析工具,以实证研究的方式,从课程建设者、教学管理者和辅导教师这3种不同用户视角展示学习分析技术的应用过程。研究以某课程平台的学习行为数据作为研究样本,应用统计、可视化、聚类、关联规则等方法,采用Excel,SPSS,Weka等工具,分析课程模块访问频次,了解不同教学组对学生登录周数的影响,刻画学生的分类特征,发现隐含的内在规律。研究表明,学习分析技术充分发挥了教育大数据的价值,使数据成为教学干预、实施决策的重要依据。     

基于数据的流程工业生产过程指标预测方法综述

生产过程关键指标的预测对于流程工业生产调度,安全生产和节能环保有着重要作用.目前,已有多种基于工业生产数据提出的生产过程指标预测方法,主要涉及特征（变量）选择,预测模型构建及其模型参数优化这三方面.本文分别针对以上三方面论述了基于数据的工业生产过程指标预测国内外研究现状,分析了各种方法的优缺点.最后,指出了流程工业生产过程指标预测方法在工业大数据及知识自动化等方面的未来研究方向和前景.