Data mining for enhanced driving effectiveness: an eco-driving behaviour analysis model for better driving decisions

With the growing demand for energy efficient vehicles, automobile companies are constantly searching for better ways to study their customers’ driving behaviour for effective new product design and development. One emerging driving behaviour among modern, eco-friendly drivers is the utilising of advanced vehicle technology for smarter, safer and more fuel-efficient driving. While many eco-driving studies focus on minimising fuel consumption, little attention is paid to how the behaviour of an individual driver and the type of vehicle used impact driving effectiveness. This study addresses this gap by proposing a novel overall drive effectiveness index that uses data mining for better driving decisions. Utilising data mining techniques, the index examines the impact of driving behaviour on driving effectiveness. A novel fuel consumption prediction model based on vehicle speed, engine speed and engine load was constructed. This decision-making support model accurately predicts real-time fuel consumption based on different driving behaviours, and hence, the driving effectiveness. Both the proposed index and fuel consumption model can be used to support decision-making in new product design and development.     

Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects

Big data has recently been recognised as one of the most important areas of future technology. It has attracted the attention of many industries, since it has the potential to provide companies with high business value. This paper examines the forms of business value that companies can create from big data analytics investments, the direct impacts it has on the financial performance of a firm, and the mediating effects of market performance and customer satisfaction. Drawing on the resource-based view theory, this study demonstrates that the business value achieved from investments in big data analytics leads to advantages in terms of the financial performance of a firm. The results offer evidence of the existence of a customer satisfaction mediation effect and of the absence of a market performance mediation effect. Theoretical and practical implications are discussed at the end of the paper.     

Modelling quality dynamics,business value and firm performance in a big data analytics environment

Big data analytics have become an increasingly important component for firms across advanced economies. This paper examines the quality dynamics in big data environment that are linked with enhancing business value and firm performance (FPER). The study identifies that system quality (i.e. system reliability, accessibility, adaptability, integration, response time and privacy) and information quality (i.e. completeness, accuracy, format and currency) are key to enhance business value and FPER in a big data environment. The study also proposes that the relationship between quality and FPER is mediated by business value of big data. Drawing on the resource-based theory and the information systems success literature, this study extends knowledge in this domain by linking system quality, information quality, business value and FPER.     

A review of systematic evaluation and improvement in the big data environment

The era of big data brings unprecedented opportunities and challenges to management research. As one of the important functions of management decision-making, evaluation has been given more functions and application space. Exploring the applicable evaluation methods in the big data environment has become an important subject of research. The purpose of this paper is to provide an overview and discussion of systematic evaluation and improvement in the big data environment. We first review the evaluation methods based on the main analytic techniques of big data such as data mining, statistical methods, optimization and simulation, and deep learning. Focused on the characteristics of big data (association feature, data loss, data noise, and visualization), the relevant evaluation methods are given. Furthermore, we explore the systematic improvement studies and application fields. Finally, we analyze the new application areas of evaluation methods and give the future directions of evaluation method research in a big data environment from six aspects. We hope our research could provide meaningful insights for subsequent research.     

Iot based laundry services: an application of big data analytics,intelligent logistics management,and machine learning techniques

The authors propose an innovative Internet of Things (IoT) based E-commerce business model Cloud Laundry for mass scale laundry services. The model utilises big data analytics, intelligent logistics management, and machine learning techniques. Using GPS and real-time update of big data, it calculates the best transportation path and update and re-route the logistic terminals quickly and simultaneously. Cloud laundry intelligently and dynamically provides the best laundry solutions based on the current state spaces of the laundry terminals through the user's specifications and thus offers local hotel customers with convenient, efficient, and transparent laundry services. Taking advantage of the rapid development of the big data industry, user interest modelling, and information security and privacy considerations, cloud laundry uses smartphone terminal control and big data models to maintain customers’ security needs. Different from the traditional laundry industry, cloud laundry companies have higher capital turnover, more liquidity, and stronger profitability. Therefore, this new generation of smart laundry business model could be of interest to not only academic researchers, but E-commerce entrepreneurs as well.     

Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement

With the shrinking feature size of integrated circuits driven by continuous technology migrations for wafer fabrication, the control of tightening critical dimensions is critical for yield enhancement, while physical failure analysis is increasingly difficult. In particular, the yield ramp up stage for implementing new technology node involves new production processes, unstable machine configurations, big data with multiple co-linearity and high dimensionality that can hardly rely on previous experience for detecting root causes. This research aims to propose a novel data-driven approach for Analysing semiconductor manufacturing big data for low yield (namely, excursions) diagnosis to detect process root causes for yield enhancement. The proposed approach has shown practical viability to efficiently detect possible root causes of excursion to reduce the trouble shooting time and improve the production yield effectively.     

国内外农业大数据应用研究分析

针对农业领域数据规模大、数据结构复杂、空间数据挖掘能力不足等问题,研究了大数据开源技术在农业领域的数据分析体系中的应用。借鉴国内外学者在农业大数据的研究成果,基于农业数据时空属性的特征,结合农业数据的特点分析了Hadoop、Storm和Spark开源大数据挖掘技术,归纳性阐述了如何开发适合农业需求的大数据系统。最后,简要分析了农业大数据技术所面临的挑战和研究难题,指出需要加大力度进一步深入理论和应用研究,从而推动和实现基于数据的科学决策,为国家粮食提供安全保障。     

Recommender systems using cluster-indexing collaborative filtering and social data analytics

As a result of the extensive variety of products available in e-commerce settings during the last decade, recommender systems have been highlighted as a means of mitigating the problem of information overload. Collaborative filtering (CF) is the most widely used algorithm to build such systems, and improving the predictive accuracy of CF-based recommender systems has been a major research challenge. This research aims to improve the prediction accuracy of CF by incorporating social network analysis (SNA) and clustering techniques. Our proposed model identifies the most influential people in an online social network by SNA and then conducts clustering analysis using these people as initial centroids (cluster centres). Finally, the model makes recommendations using cluster-indexing CF based on the clustering outcomes. In this step, our model adjusts the effect of neighbours in the same cluster as the target user to improve prediction accuracy by reflecting hidden information about his or her social community. The experimental results indicate that the proposed model outperforms other comparison models, including conventional CF, with statistical significance.     

Research on coordinated development between metropolitan economy and logistics using big data and Haken model

To quantitatively study the relationship and mutual effects between metropolitan economy and logistics is an important, yet pending issue, which can scientifically guide the urban planning and investment. Through the identified evaluation indexes of metropolitan logistics and economic development, this paper first builds up an evaluation process model of metropolitan economic and logistics development, based on big data analytics (BDA), the entropy evaluation method, and the maximum deviation method. BDA can help extract the exact data about the indicators of metropolitan economy and logistics. Then a Haken model is adopted to ravel out the dynamic co-evolutionary law of economy and logistics in five Chinese cities, which complements the above static evaluation. The results show that the economic development is an order parameter and plays a key role in the coordinated development of metropolitan logistics and economy. However, from 2013 to 2014, these five cities had not established an orderly evolved positive-feedback mechanism through which economic development promotes the coordinated development of metropolitan logistics and economic development.     

Understanding big consumer opinion data for market-driven product design

Big consumer data provide new opportunities for business administrators to explore the value to fulfil customer requirements (CRs). Generally, they are presented as purchase records, online behaviour, etc. However, distinctive characteristics of big data, Volume, Variety, Velocity and Value or ‘4Vs’, lead to many conventional methods for customer understanding potentially fail to handle such data. A visible research gap with practical significance is to develop a framework to deal with big consumer data for CRs understanding. Accordingly, a research study is conducted to exploit the value of these data in the perspective of product designers. It starts with the identification of product features and sentiment polarities from big consumer opinion data. A Kalman filter method is then employed to forecast the trends of CRs and a Bayesian method is proposed to compare products. The objective is to help designers to understand the changes of CRs and their competitive advantages. Finally, using opinion data in Amazon.com, a case study is presented to illustrate how the proposed techniques are applied. This research is argued to incorporate an interdisciplinary collaboration between computer science and engineering design. It aims to facilitate designers by exploiting valuable information from big consumer data for market-driven product design.     

大数据在民爆行业信息化中的应用

在工业化与信息化深度融合的当下,及时、准确、全面的信息是分析民爆安全生产机制、构建民爆信息管理体系的前提。针对因民爆行业的特殊性,出现的信息化地区垄断、信息孤岛等问题,对目前民用爆炸物品安全生产的信息化程度进行了探讨,提出了将大数据思维运用到民爆行业一体化进程中,构建民爆安全生产大数据服务云平台,即对现有的民爆信息化系统的数据进行收集、存储、归类、分析、挖掘,并将分析结果作为民爆安全生产大数据服务云平台的重要数据支撑。依据大数据建立的民爆安全生产大数据服务云平台对保障民爆行业的安全生产管理具有现实意义。     

大数据在民爆行业信息化中的应用

在工业化与信息化深度融合的当下,及时、准确、全面的信息是分析民爆安全生产机制、构建民爆信息管理体系的前提。针对因民爆行业的特殊性,出现的信息化地区垄断、信息孤岛等问题,对目前民用爆炸物品安全生产的信息化程度进行了探讨,提出了将大数据思维运用到民爆行业一体化进程中,构建民爆安全生产大数据服务云平台,即对现有的民爆信息化系统的数据进行收集、存储、归类、分析、挖掘,并将分析结果作为民爆安全生产大数据服务云平台的重要数据支撑。依据大数据建立的民爆安全生产大数据服务云平台对保障民爆行业的安全生产管理具有现实意义。     

面向电子商务的客户关系管理数据挖掘模型研究

客户资源是企业竞争力的归宿,客户关系需要进行科学管理,这已成为现代企业的共识。电子化的客户关系管理要求企业建立客户资源数据库,并基于客户资源数据库通过网络实现客户需求分析、挖掘客户资源、实现个性化客户服务等。本文对支持向量机理论进行了研究,在介绍SVM原理的基础上,给出了基于支持向量机理论的高维空间数据挖掘方法,并结合实例研究给出了面向电子商务的智能客户关系管理模型。     

Institutional effects and the decision to make environmental investments

An unstated assumption in most business research is that a primary goal of managers is profit maximisation. Recently, managers have faced additional pressure to also address environmental issues, while maintaining profits. The literature (Russo, M. and Fouts, P., 1997. A resource-based perspective on corporate environmental performance and profitability. Academy of Management Journal, 40 (3), 534–559 and Pagell, M. and Gobeli, D., 2009. How plant managers' experiences and attitudes towards sustainability relate to operational performance. Production and Operations Management, 18 (3), 278–299) suggests these goals are compatible and that organisations can and indeed need to address environmental issues as part of their profit maximisation efforts. However, institutional theory suggests that managers may have other goals that drive their decisions, beyond the desire to maximise profits. This research explores two institutions, the nation or country and industry, and their effects on the decision to make environmental investments. The results indicate that managers do indeed respond to institutions when making these decisions and that in some countries there is a general level of underinvestment in the environment, which is likely harming both organisational and environmental outcomes.     

A GA-based optimisation model for big data analytics supporting anticipatory shipping in Retail 4.0

In Retail 4.0, omni-channels require a seamless and complete integration of all available channels for purchasing. The diversification of channels not only diversifies data sources, but also rapidly generates an enormous amount of data. This highlights a need of big data analytics to extract meaningful knowledge for decision-making. In addition, anticipatory shipping is getting more popular to ensure fast product delivery. The goal is to predict when a customer will make a purchase and then begin shipping the product to the nearest distribution centres before the customer places the orders online. This paper proposes a genetic algorithm (GA)-based optimisation model to support anticipatory shipping. Cloud computing is deployed to store the big data generated from all channels. Cluster-based association rule mining is applied to discover the purchase pattern and predict future purchase in terms of If-Then prediction rules. A modified GA is then used to generate optimal anticipatory shipping plans. Apart from transportation cost and travelling distance, the confidence of prediction rules is also considered in the GA. A number of numerical experiments have been carried out to demonstrate the trade-off of different factors in anticipatory shipping, and the optimisation reliability of the model is verified.     

The role of digital technologies for the service transformation of industrial companies

The role of digital technologies in service business transformation is under-investigated. This paper contributes to filling this gap by addressing how the Internet of things (IoT), cloud computing (CC) and predictive analytics (PA) facilitate service transformation in industrial companies. Through the Data–Information–Knowledge–Wisdom (DIKW) model, we discuss how the abovementioned technologies transform low-level entities such as data into information and knowledge to support the service transformation of manufacturers. We propose a set of digital capabilities, based on the extant literature and the findings from four case studies. Then, we discuss how these capabilities support the service transformation trajectories of manufacturers. We find that IoT is foundational to any service transformation, although it is mostly needed to become an availability provider. PA is essential for moving to the performance provider profile. Besides providing scalability in all profiles, CC is specifically used to implement an industrialiser strategy, therefore leading to standardised, repeatable and productised offerings.     

Big data and supply chain decisions: the impact of volume,variety and velocity properties on the bullwhip effect

The bullwhip effect is causing inefficiencies in today’s supply chains. This study deals with the potential of big data on the improvement of the various supply chain processes. The aim of this paper is to elaborate which characteristic of big data (lever) has the greatest potential to mitigate the bullwhip effect. From previous research, starting points for big data applications are derived. By using an existing system dynamics model, the big data levers ‘velocity’, ‘volume’ and ‘variety’ are transferred into a simulation. Overall, positive impacts of all the big data levers are elaborated. Findings suggest that the data property ‘velocity’ relatively bears the greatest potential to enhance performance. The results of this research will help in justifying the application of big data in supply chain management. The paper contributes to the literature by operationalising big data in the control engineering analyses.     

间接蒸发冷却技术在数据中心应用的经济性分析

本文分析了间接蒸发冷却技术在数据中心中应用的背景,对间接蒸发冷却技术原理进行了介绍,通过实际案例对间接蒸发冷却技术在项目中的应用进行了初投资和运行费用的分析比较,指出了间接蒸发冷却技术在数据中心领域应用前景。     

Using digital photography to implement the McFarland method

The McFarland method allows the concentration of bacterial cells in a liquid medium to be determined by either of two instrumental techniques: turbidimetry or nephelometry. The microbes act by absorbing and scattering incident light, so the absorbance (turbidimetry) or light intensity (nephelometry) measured is directly proportional to their concentration in the medium. In this work, we developed a new analytical imaging method for determining the concentration of bacterial cells in liquid media. Digital images of a series of McFarland standards are used to assign turbidity-based colour values with the aid of dedicated software. Such values are proportional to bacterial concentrations, which allow a calibration curve to be readily constructed. This paper assesses the calibration reproducibility of an intra-laboratory study and compares the turbidimetric and nephelometric results with those provided by the proposed method, which is relatively simple and affordable; in fact, it can be implemented with a digital camera and the public domain software ImageJ.     

计量性是复杂工业产品技术状态控制及大数据管理的基石

以质量控制观念及大数据管理为视角,阐述了计量性的概念、意义和价值,指出了计量性实质上就是产品状态参量的可激励性、可控制性和可观测性。按照技术目标与能力要求,将计量性划分为5个不同的技术等级,以贯穿从产品的设计制造到使用维护的全寿命过程。讨论了计量性对提升产品成熟度的作用。