Orchestrating big data analytics capability for sustainability: A study of air pollution management in China

Under rapid urbanization, cities are facing many societal challenges that impede sustainability. Big data analytics (BDA) gives cities unprecedented potential to address these issues. As BDA is still a new concept, there is limited knowledge on how to apply BDA in a sustainability context. Thus, this study investigates a case using BDA for sustainability, adopting the resource orchestration perspective. A process model is generated, which provides novel insights into three aspects: data resource orchestration, BDA capability development, and big data value creation. This study benefits both researchers and practitioners by contributing to theoretical developments as well as by providing practical insights.     

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.     

Optimizing the energy efficiency of chiller systems in the semiconductor industry through big data analytics and an empirical study

Big data analytics is playing a more and more prominent role in the manufacturing industry as corporations attempt to utilize vast amounts of data to optimize the operation of plants and factories to gain a competitive advantage. Since the advent of Industry 4.0, also known as smart manufacturing, big data analytics, combined with expert domain knowledge, is facilitating ever-greater levels of speed and automaticity in manufacturing processes. The semiconductor industry is a fundamental driver of this transformation; moreover, due to the highly complex and energy-consuming nature of the semiconductor manufacturing process, semiconductor fabrication facilities (fabs) can also benefit greatly from incorporating big data analytics to improve production and energy efficiency. This paper developed a big data analytics framework, along with an empirical study conducted in collaboration with a semiconductor manufacturer in Taiwan, to optimize the energy efficiency of chiller systems in semiconductor fabs. Chiller systems are one of the most energy-consuming systems within a typical modern fab. The developed big data analytics framework allows production managers to ensure that chiller systems operate at an optimized level of energy efficiency under dynamically changing conditions, while fulfilling the chilling demands. Compared to the commonly-used heuristics previously employed at the fab to tune chiller system parameters, by the utilization of big data analytics, it is shown that fabs can achieve substantial energy savings, greater than 12%. The developed framework and the lessons learned from the empirical study are not only generalizable but also useful for practitioners who are interested in applying big data analytics to optimize the performance of other equipment systems in fabs.     

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.     

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.     

Actualizing big data analytics affordances: A revelatory case study

Drawing on a revelatory case study, we identify four big data analytics (BDA) actualization mechanisms: (1) enhancing, (2) constructing, (3) coordinating, and (4) integrating, which manifest in actions on three socio-technical system levels, i.e., the structure, actor, and technology levels. We investigate the actualization of four BDA affordances at an automotive manufacturing company, i.e., establishing customer-centric marketing, provisioning vehicle-data-driven services, data-driven vehicle developing, and optimizing production processes. This study introduces a theoretical perspective to BDA research that explains how organizational actions contribute to actualizing BDA affordances. We further provide practical implications that can help guide practitioners in BDA adoption.     

The promising future of healthcare services: When big data analytics meets wearable technology

As policy-makers and business practitioners across the globe expend extraordinary effort toward the field of e-health, the thriving development of healthcare-wearable technology is creating great opportunities and posing a remarkable future for healthcare services. This paper employs a game theory model to investigate the dynamics of wearable device market. We extend the two-dimensional product differentiation model by incorporating consumer diversity, consumer density, and firms’ big data analytics (BDA) investment strategy. Our model reveals that with differentiated consumer densities firms are more likely to engage in quality competition and the firm that invests in BDA can achieve higher profits. Furthermore, the overall quality of biomedical and healthcare services can be improved under various market conditions. Our findings provide practical guidance to wearable device manufacturers on optimizing competition strategies and offer insights to social planners on potential policy-making to promote better healthcare services.     

云—边缘系统中跨域大数据作业调度技术研究

为了降低因处理这些跨域大数据带来的作业完成时延,首先提出了以最小化系列跨域作业平均完成时间为优化目标的在线随机调度算法ranTA。ranTA基于跨域资源的异构性在线地计算出各计算任务调度至不同位置的偏好,并以此偏好作为概率调度每个计算任务;更进一步,为了避免将“热点”数据积压在边缘集群造成性能瓶颈,提出基于ranTA的捎带式数据重分布机制ranTA-data,其将部分数据随任务执行留存至云数据中心。ranTA-data不仅优化了当前作业的完成时间,也能证明在该机制下系列作业的平均完成时间以大概率汇聚于最优解附近。大规模仿真实验表明,所提出的在线随机化算法与数据重部署机制相比传统方法,平均降低系列作业完成时间近30%。     

Deliberate storytelling in big data analytics adoption

The emergence of big data analytics (BDA) has posed opportunities as well as multiple challenges to business practitioners, who have called for research on the behavioural factors underlying BDA adoption at the individual level. The purpose of this study is to extend the information systems (IS) research on storytelling and to explore the role and characteristics of deliberate storytelling in individual‐level BDA adoption. This case study used the grounded theory approach to extract qualitative data from 24 interviews, field notes, and documentary data. The explicit contributions of the study to the literature include (a) increasing our understanding of the facilitating role of deliberate storytelling in individual‐level BDA adoption, (b) identifying four deliberate storytelling patterns and seven underlying corporate stories disseminated by organizations to influence individual behaviour, and (c) defining the core characteristics of effective deliberate storytelling. This study has multiple implications for business practitioners and demonstrates how deliberate storytelling can be used as a facilitating mechanism in daily business practice.     

Using big data analytics to extract disease surveillance information from point of care diagnostic machines

This paper explains a novel approach for knowledge discovery from data generated by Point of Care (POC) devices. A very important element of this type of knowledge extraction is that the POC generated data would never be identifiable, thereby protecting the rights and the anonymity of the individual, whilst still allowing for vital population-level evidence to be obtained. This paper also reveals a real-world implementation of the novel approach in a big data analytics system. Using Internet of Things (IoT) enabled POC devices and the big data analytics system, the data can be collected, stored, and analyzed in batch and real-time modes to provide a detailed picture of a healthcare system as well to identify high-risk populations and their locations. In addition, the system offers benefits to national health authorities in forms of optimized resource allocation (from allocating consumables to finding the best location for new labs) thus supports efficient and timely decision-making processes.     

A novel virtual node approach for interactive visual analytics of big datasets in parallel coordinates

Big data is a collection of large and complex ​datasets that commonly appear in multidimensional and multivariate data formats. It has been recognized as a big challenge in modern computing/information sciences to gain (or find out) due to its massive volume and complexity (e.g. its multivariate format). Accordingly, there is an urgent need to find new and effective techniques to deal with such huge ​datasets. Parallel coordinates is a well-established geometrical system for visualizing multidimensional data that has been extensively studied for decades. There is also a variety of associated interaction techniques currently used with this geometrical system. However, none of these existing techniques can achieve the functions that are covered by the Select layer of Yi’s Seven-Layer Interaction Model. This is because it is theoretically impossible to find a select of data items via a mouse-click (or mouse-rollover) operation over a particular visual poly-line (a visual object) with no geometric region. In this paper, we present a novel technique that uses a set of virtual nodes to practically achieve the Select interaction which has hitherto proven to be such a challenging sphere in parallel coordinates visualization.     

Smart urban planning using Big Data analytics to contend with the interoperability in Internet of Things

The recent growth and expansion in the field of Internet of Things (IoT) is providing a great business prospective in the direction of the new era of smart urban. The insight of the smart urban is extensively preferred, as it improves the excellence of life of citizens, connecting several regulations, that is, smart transportation, smart parking, smart environment, smart healthcare, and so forth. Continuous intensification of the multifaceted urban set-up is extensively challenged by real-time processing of data and smart decision capabilities. Consequently, in this paper, we propose a smart city architecture which is based on Big Data analytics. The proposed scheme is comprised of three modules: (1) data acquisition and aggregation module collects varied and diverse data interrelated to city services, (2) data computation and processing module performs normalization, filtration, processing and data analysis, and (3) application and decision module formulates decisions and initiates events. The proposed architecture is a generic solution for the smart urban planning and variety of datasets is analyzed to validate this architecture. In addition, we tested reliable datasets on Hadoop server to verify the threshold limit value (TLV) and the investigation demonstrates that the proposed scheme offer valuable imminent into the community development systems to get better the existing smart urban architecture. Moreover, the efficiency of proposed architecture in terms of throughput is also shown.     

分布式信息系统内部监控审计平台设计与实现

为解决信息系统的内部安全管理问题,提出了一种基于分布式业务系统的内部安全管理解决方案.通过内部监控审计平台建设,实现对系统核心业务应用、数据库和系统日志3个级别的监控审计功能;根据规则库配置策略对监控审计数据进行采集、过滤、转换和存储,并通过数据交换平台汇聚到安全管理中心,从而实现对分布式业务系统的集中监控管理、统计分析和预警.结合具体应用,验证了该设计方案的有效性和准确性.     

城市道路交通数据可视分析综述

目的 交通是困扰现代大都市的世界性难题.近年来,可视分析技术在分析和利用交通大数据中扮演了越来越重要的角色,成为一项重要的智能交通技术.本文将全面回顾自信息可视化和可视分析兴起以来城市交通数据可视分析领域的研究现状.方法 从道路交通流量分析和其他交通问题分析两个方面,按照数据的类型及问题的分类探讨交通领域的可视化技术和可视分析系统,简单回顾近年来出现的研究新趋势.结果 早期研究注重对道路流量的可视化展示方案,主要方法有箭头图、马赛克图和轨迹墙等.随着可视分析手段的丰富,对城市道路交通流量的分析层次上升到交通事件层面,但是交通事件的定义仅局限于交通拥堵.应用可视分析的其他交通问题领域包括公共交通、交通事故和人群出行行为等.近年出现了挖掘和利用交通轨迹或交通事件的社会属性或称环境上下文信息的研究新趋势.结论 从对交通流量的可视化到交通事件的可视分析,从面向道路交通状况到与交通相关的其他社会性问题,从单纯反映路况的交通数据到富含社会性语义的多源数据,从传统的PC端可视化和交互范式到新型的可视化展示介质,交通数据可视化领域的研究在深度和广度上都得到大大拓展,未来该领域的研究趋势也体现于其中.     

网络信息审计系统中数据采集的研究与实现

数据采集是网络信息审计系统的基础组件.故而对流行的网络数据采集工具Libpcap进行了详细的分析,指出该工具只适合在普通网络环境下运行,不能满足基于高速网络的信息审计系统的需求.为此,对零拷贝技术进行了研究与试验,并成功实现了该技术,从软件上满足了基于高速网络的信息审计系统的需求.     

带野值的单类分类器在安全审计中的应用

单类分类器是当前模式识别领域的一个研究热点。带野值的单类分类器是在单类分类器的基础上,通过引入少量珍贵的异常样本（野值）,以加强分类器的性能。该模型适用于处理正类样本数目远多于反类样本的两类数据类别不平衡问题。提出了将带野值的支持向量描述方法应用于安全审计数据分析中,并通过实验证实了该方法对异常样本更为敏感,具有良好的应用潜力。     

Mapping the study of learning analytics in higher education

ABSTRACT

In recent years, the application of technological innovation in higher education has become more and more widely spread, and technological innovation has been improving the level of education. In the research of higher education with innovation technology, one of the main focuses is on the dynamic data which can lay a foundation for the analysis of educational activities by learning analytics. The dynamic data created by technological innovation will become the key basis for analytical research and development in higher education. The methods and analysis results of learning analytics will directly affect decision-making and strategy about higher education. In this paper, we use bibliometric and visualisation methods to review the literature, in order to highlight the development of learning analytics in higher education. Using bibliometric analysis, our study depicts the development process of the main methods used in learning analytics, and summarises the current situation in this field, which increases the level of understanding provided by those studies. Finally, we summarise the research hotspots and study trends, which will be useful for future study in this field.