Establishment-level occupational safety analytics: Challenges and opportunities

In occupational safety and health, big data and analytics show promise for the prediction and prevention of workplace injuries. Advances in computing power and analytical methods have allowed companies to reveal insights from the “big” data that previously would have gone undetected. Despite the promise, occupational safety has lagged behind other industries, such as supply chain management and healthcare, in terms of exploiting the potential of analytics and much of the data collected by organizations goes unanalyzed. The purpose of the present paper is to argue for the broader application of establishment-level safety analytics. This is accomplished by defining the terms, describing previous research, outlining the necessary components required, and describing knowledge gaps and future directions. The knowledge gaps and future directions for research in establishment-level analytics are categorized into readiness for analytics, analytics methods, technology integration, data culture, and impact of analytics.     

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.     

生物医疗健康大数据应用支撑平台与关键技术

随着生物信息学的不断发展,生物医学领域积累了大量的数据,大数据已经贯穿基础研究、临床诊断、医药开发、健康管理等生物医学领域的各个环节。如何有效存储、管理、分析这些海量数据面临严峻的而挑战。基于超级计算机的计算分析和存储能力,在生物医学大数据处理的异构融合架构,面向生物医学大数据的层次式存储系统,生物医学大数据处理的异构并行计算和多源数据的汇聚机制与分析方法,突破生物医学大数据的汇聚、存储、分析等方面的关键技术,构建一个计算、分析处理和存储融合平台,以满足多种类型生物医学大数据应用的不同需求。     

基于MOOC的大数据分析技术

MOOC近几年发展迅猛,在使用过程中,大规模的学习者和海量的教学资源积累了庞大的学习行为数据。因此,基于MOOC的大数据分析成为了一个新兴的研究热点,其分析框架中涉及的四大核心是：大数据从哪里获取（Where）、MOOC大数据的类型（What）、如何进行大数据分析（How）和大数据分析应用（Do）。本文通过对MOOC现状的分析、特征及分类的梳理,提出一种Where-What-How-Do大数据分析框架,并对上述的四大核心进行阐述和回答。最后,结合Canvas Network数据集进行聚类分析和多元回归分析,得出关于MOOC数据的一些启示和应用。      

Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities

A central question for information systems (IS) researchers and practitioners is if, and how, big data can help attain a competitive advantage. To address this question, this study draws on the resource-based view, dynamic capabilities view, and on recent literature on big data analytics, and examines the indirect relationship between a firm’s big data analytics capability (BDAC) and competitive performance. The study extends existing research by proposing that BDACs enable firms to generate insight that can help strengthen their dynamic capabilities, which, in turn, positively impact marketing and technological capabilities. To test our proposed research model, we used survey data from 202 chief information officers and IT managers working in Norwegian firms. By means of partial least squares structural equation modeling, results show that a strong BDAC can help firms build a competitive advantage. This effect is not direct but fully mediated by dynamic capabilities, which exerts a positive and significant effect on two types of operational capabilities: marketing and technological capabilities. The findings suggest that IS researchers should look beyond direct effects of big data investments and shift their attention on how a BDAC can be leveraged to enable and support organizational capabilities.     

Toward the development of a big data analytics capability

The era of big data has begun such that organizations in all industries have been heavily investing in big data initiatives. We know from prior studies that investments alone do not generate competitive advantage; instead, firms need to create capabilities that rival firms find hard to match. Drawing on the resource-based theory of the firm and recent work in big data, this study (1) identifies various resources that in combination build a big data analytics (BDA) capability, (2) creates an instrument to measure BDA capability of the firm, and (3) tests the relationship between BDA capability and firm performance. Results empirically validate the proposed theoretical framework of this study and provide evidence that BDA capability leads to superior firm performance.     

基于Hadoop技术框架的组织知识管理系统设计与实现

利用大数据技术来增强组织中的知识管理,使随着大数据技术发展的必然产物。目前,基于新的大数据体系架构,通过高级数据分析手段来存储和分析知识数据,为组织决策生成至关重要的实时知识。具体来说,必须具有一个单一的基础结构,该基础结构提供知识管理的通用功能,并且足够灵活以处理不同类型的大数据和大数据分析任务。本文提出利用可存储和处理大量数据的云计算基础架构实现大数据的高效处理,最大程度地降低了大数据分析所需的各类成本,该框架可以实时分析大数据,以促进增强竞争优势的决策。     

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.     

Analytics Capabilities and the Decision to Invest in Analytics

The increasing use of data-driven decision making and big data is leading organizations to invest in analytics software and services. However, little is known about the type of analytics capabilities within IT that are required and whether there is a common progression or development model of analytics capabilities. Also unknown is how the level of analytics capabilities and other factors influence a firm’s decision to invest in analytics. The purpose of this research is to explore the relationships between levels of distinct analytics capabilities and to understand how they and other factors influence the analytics investment decision. The findings suggest that there is a distinct progression in the development of analytics capabilities, and that firm size is associated with increased capability. The results suggest that firms more likely to invest in analytics have higher current levels of specific analytics capabilities, are larger, and are located in less-competitive industries.     

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.     

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.     

基于用户视角下学习分析技术的应用与挑战

In the big data environment, with the rapid development of education information technology, learning analytics has been a hot research topic in recent years. In order to provide references for the follow-up study, based on the relevant literatures, this paper expounds the concept and characteristics of the learning analytics, from the user point of view of learners, teachers and teaching managers, and discusses the application of learning analytics in network learning and the problems and challenges faced by them.     

大数据分析平台建设与应用综述

大数据分析平台是开展大数据处理与分析应用所必需的基础设施。文章基于课题组开展大数据分析平台建设的科研成果与实践经验,结合大型企业实施行业应用项目的切身感受,从大数据分析平台设计、主流热点技术、行业应用案例三个方面进行介绍。文章首先分析了大数据分析平台的主要功能和体系架构,然后介绍了大数据分析平台的关键技术,重点介绍了 Spark技术的体系架构及核心组件,最后介绍了大数据技术在大规模制造业、零售业和智能电网三个领域的应用案例。     

Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: A dynamic capabilities view

This study examines the antecedents and influence of big data decision-making capabilities on decision-making quality among Chinese firms. We propose that such capabilities are influenced by big data management challenges such as leadership, talent management, technology, and organisational culture. By using primary data from 108 Chinese firms and utilising partial least squares, we tested the antecedents of big data decision-making capability and its impact on decision-making quality. Findings suggest that big data management challenges are the key antecedents of big data decision-making capability. Furthermore, the latter is vital for big data decision-making quality.     

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.     

Using resource-based view theory to assess the value of ERP commercial-packages in SMEs

This study explores the enterprise resource planning (ERP) variations in value on small and medium enterprises (SMEs) across four commercial-packages (Microsoft NAV, SAP All-in-one, ORACLE JDE, and SAGE X3). Grounded on the resource-based view (RBV) theory of the firm, we assess a research model linking three determinants; ERP use, collaboration, and analytics to explain the ERP value in three effects (individual productivity, management control, and customer satisfaction). Using a survey data set of 883 firms across European SMEs we test the theoretical model through structural equation modelling. This study provides empirical evidence on how European SMEs find value from the top four commercial-packaged ERPs. Whereas for Dynamics and ORACLE the most important factor is analytics system capability, for SAP and SAGE it is greater collaboration system capability. Furthermore, for SAP and ORACLE greater ERP use is perceived as an important factor, but not for Dynamics and SAGE. In addition, the study finds that both collaboration and analytics capabilities are the greatest differentiators to ERP value, which is consistent with the RBV. The finding provide guidance to business implementation strategies and to software development. The limitations and future work of the study are noted.     

Business analytics and business value: A comparative case study

With growing adoption of business analytics, it is important for investing firms to understand how business value is created from investments. Studies in IT domain have highlighted how higher investment in technology may not bring more returns, rather how IT as an organizational capability acts as a key mediator in value creation. This research extends the model to business analytics, to identify elements of analytics technology assets and business analytics capability and to understand the mechanism of business value creation using multiple case studies. We capture how analytics resources contribute to business performance by developing operational and organizational performance measures.     

Preface

It is our great honor to announce the publication of this special section on AI and big data analytics in biology and medicine in the Journal of Computing Science and Technology (JCST). As more and more modern biological and medical data are produced,artificial intelligence (AI) and big data analytics are playing an increasingly important role in helping to draw meaningful and logical conclusions about biology and medicine.Understanding biological and medical data will help answer important life questions on Earth,find solutions to global health problems,and even help solve tough problems such as drug design and disease diagnosis.The information obtained from biology and medicine is not only very detailed,but also has unique properties such as low quality data,big data sizes,different complex formats,high dimensions,many duplications and much noise,and so on.They all require special skills or unique tools for analysis and interpretation.Thus,a lot of studies using AI and big data analytics on biological and medical data are becoming very popular and hot topics in the computer science research field.     

The role of information governance in big data analytics driven innovation

The age of big data analytics is now here, with companies increasingly investing in big data initiatives to foster innovation and outperform competition. Nevertheless, while researchers and practitioners started to examine the shifts that these technologies entail and their overall business value, it is still unclear whether and under what conditions they drive innovation. To address this gap, this study draws on the resource-based view (RBV) of the firm and information governance theory to explore the interplay between a firm’s big data analytics capabilities (BDACs) and their information governance practices in shaping innovation capabilities. We argue that a firm’s BDAC helps enhance two distinct types of innovative capabilities, incremental and radical capabilities, and that information governance positively moderates this relationship. To examine our research model, we analyzed survey data collected from 175 IT and business managers. Results from partial least squares structural equation modelling analysis reveal that BDACs have a positive and significant effect on both incremental and radical innovative capabilities. Our analysis also highlights the important role of information governance, as it positively moderates the relationship between BDAC’s and a firm’s radical innovative capability, while there is a nonsignificant moderating effect for incremental innovation capabilities. Finally, we examine the effect of environmental uncertainty conditions in our model and find that information governance and BDACs have amplified effects under conditions of high environmental dynamism.     

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