The role of big data in smart city

The expansion of big data and the evolution of Internet of Things (IoT) technologies have played an important role in the feasibility of smart city initiatives. Big data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources, and the IoT allows the integration of sensors, radio-frequency identification, and Bluetooth in the real-world environment using highly networked services. The combination of the IoT and big data is an unexplored research area that has brought new and interesting challenges for achieving the goal of future smart cities. These new challenges focus primarily on problems related to business and technology that enable cities to actualize the vision, principles, and requirements of the applications of smart cities by realizing the main smart environment characteristics. In this paper, we describe the state-of-the-art communication technologies and smart-based applications used within the context of smart cities. The visions of big data analytics to support smart cities are discussed by focusing on how big data can fundamentally change urban populations at different levels. Moreover, a future business model of big data for smart cities is proposed, and the business and technological research challenges are identified. This study can serve as a benchmark for researchers and industries for the future progress and development of smart cities in the context of big data.     

Making use of cloud computing for healthcare provision: Opportunities and challenges

Cloud or utility computing is an emerging new computing paradigm designed to deliver numerous computing services through networked media such as the Web. This approach offers several advantages to potential users such as “metered” use (i.e., pay-as-you-go) which offers scalability, online delivery of software and virtual hardware services (e.g., collaboration programmes, virtual servers, virtual storage devices) which would enable organizations to obviate the need to own, maintain and update their software and hardware infrastructures. The flexibility of this emerging computing service has opened many possibilities for organizations that did not exist before. Among those organizations are those engaged in healthcare provision. The aim of this article is to shed some light on this development and explore the potential (and future) of cloud computing in contributing to the advancement of healthcare provision. A small case study will also be presented and discussed.     

Developing a government enterprise architecture framework to support the requirements of big and open linked data with the use of cloud computing

Governmental and local authorities are facing many new information and communication technologies challenges. The amount of data is rapidly increasing. The data sets are published in different formats. New services are based on linking and processing differently structured data from various sources. Users expect openness of public data, fast processing, and intuitive visualisation. The article addresses the challenges and proposes a new government enterprise architecture framework. The following partial architectures are included: big and open linked data storage, processing, and publishing using cloud computing. At first, the key concepts are defined. Next, the basic architectural roles and components are specified. The components result from the decomposition of related frameworks. The main part of the article deals with the detailed proposal of the architecture framework and partial views on architecture (sub-architectures). A methodology, including a proposal of appropriate steps, solutions and responsibilities for them, is described in the next step - after the verification and validation of the new framework with respect to the attributes of quality. The new framework responds to emerging ICT trends in order to evolve government enterprise architecture continually and represent current architectural components and their relationships.     

Outlook on moving of computing services towards the data sources

The internet of things (IoT) is potentially interconnecting unprecedented amounts of raw data, opening countless possibilities by two main logical layers: become data into information, then turn information into knowledge. The former is about filtering the significance in the appropriate format, while the latter provides emerging categories of the whole domain. This path of the data is a bottom-up flow. On the other hand, the path of the process is a top-down flow, starting at the strategic level of business and scientific institutions. Today, the path of the process treasures a sizeable amount of well-known methods, architectures and technologies: the so called Big Data. On the top, Big Data analytics aims variable association (e-commerce), data mining (predictive behaviour) or clustering (marketing segmentation). Digging the Big Data architecture there are a myriad of enabling technologies for data taking, storage and management. However the strategic aim is to enhance knowledge with the appropriate information, which does need of data, but not vice versa. In the way, the magnitude of upcoming data from the IoT will disrupt the data centres. To cope with the extreme scale is a matter of moving the computing services towards the data sources. This paper explores the possibilities of providing many of the IoT services which are currently hosted in monolithic cloud centres, moving these computing services into nano data centres (NaDa). Particularly, data-information processes, which usually are performing at sub-problem domains. NaDa distributes computing power over the already present machines of the IP provides, like gateways or wireless routers to overcome latency, storage cost and alleviate transmissions. Large scale questionnaires have been taken for 300 IT professionals to validate the points of view for IoT adoption. Considering IoT is by definition connected to the Internet, NaDa may be used to implement the logical low layer architecture of the services. Obviously, such distributed NaDa send results on a logical high layer in charge of the information-knowledge turn. This layer requires the whole picture of the domain to enable those processes of Big Data analytics on the top.     

广西云计算产业发展现状分析

在全球云计算迅猛发展的背景下,与国内外先进省市相比,广西的云计算产业发展仍然是任重道远,结合广西企业在云计算产业的发展现状,分析了广西发展云计算产业面临的困难与挑战。     

Real-time big data processing for anomaly detection: A Survey

The advent of connected devices and omnipresence of Internet have paved way for intruders to attack networks, which leads to cyber-attack, financial loss, information theft in healthcare, and cyber war. Hence, network security analytics has become an important area of concern and has gained intensive attention among researchers, off late, specifically in the domain of anomaly detection in network, which is considered crucial for network security. However, preliminary investigations have revealed that the existing approaches to detect anomalies in network are not effective enough, particularly to detect them in real time. The reason for the inefficacy of current approaches is mainly due the amassment of massive volumes of data though the connected devices. Therefore, it is crucial to propose a framework that effectively handles real time big data processing and detect anomalies in networks. In this regard, this paper attempts to address the issue of detecting anomalies in real time. Respectively, this paper has surveyed the state-of-the-art real-time big data processing technologies related to anomaly detection and the vital characteristics of associated machine learning algorithms. This paper begins with the explanation of essential contexts and taxonomy of real-time big data processing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies. Finally, the identified research challenges of real-time big data processing in anomaly detection are discussed.     

云计算网络安全现状与思考

云计算是一种新型的资源高效利用模式,它的高效率和高资源利用率使得云计算逐步成为信息时代的主流,然而随着云计算的横空出世,可信云计算、云计算安全的问题也纷至沓来。可信云计算技术与安全云计算技术两者既可以组合使用,也可以独立使用。它们共同构成了云计算网络安全技术。云计算网络安全技术的技术支撑是可信访问控制,密文检索与处理,数据存在与可使用性证明,数据隐私保护,虚拟安全技术,云资源访问控制等。可信云安全计算技术及其特征支撑着整个可信安全云系统,包括可信云服务数据中心、可信云用户端以及可信云管理平台。可以构建可信云安全技术的各应用系统,包括可信政务云系统、可信家务云系统、可信企业云系统、可信商务云系统。     

基于GAE的云计算应用开发

云计算已经成为学术界与工业界共同的研究热点。文章阐述Google云计算平台的技术框架,分析并深入比较网络应用程序的传统开发方式与基于Google App Engine平台开发的区别,并给出基于Google App Engine平台上开发网络应用程序的基本过程,说明了云平台下开发的优势。     

Discovering the potential of cloud computing in accelerating the search for curing serious illnesses

Derided, when it emerged in 2007 as a fad, cloud computing has proved to be a viable business model for remotely delivering IT services through the Web (and other media) on a pay-as-you-go basis. The flexibility of this emerging computing service has opened many possibilities for organizations. Drug companies and medical research centers are among those organizations that are likely to benefit from this new IT service model. As well as providing massive cost-savings, cloud computing could offer these organizations the opportunity to greatly enhance the efficiency of their operations. For cloud providers, this is a new field to expand their reach. The aim of this article is to explore this new development and the potential of cloud computing in contributing to the advancement of research in life science and explain why this IT service model (despite many of its problems) could be game-changer for companies engaged in this business.     

Data and infrastructure security auditing in cloud computing environments

For many companies the remaining barriers to adopting cloud computing services are related to security. One of these significant security issues is the lack of auditability for various aspects of security in the cloud computing environment. In this paper we look at the issue of cloud computing security auditing from three perspectives: user auditing requirements, technical approaches for (data) security auditing and current cloud service provider capabilities for meeting audit requirements. We also divide specific auditing issues into two categories: infrastructure security auditing and data security auditing. We find ultimately that despite a number of techniques available to address user auditing concerns in the data auditing area, cloud providers have thus far only focused on infrastructure security auditing concerns.     

基于资源组合预测的云计算任务调度算法

云计算资源负荷状态的动态变化,使得如何预测其变化情况,从而合理地实施任务调度,成为亟待解决的问题。针对该问题,提出一种基于资源组合预测的云计算任务调度算法。该模型采用偏最小二乘回归从各种单一模型的预测结果中提取有效成分,建立组合预测模型;再依据对资源结点的资源预测结果和最小完成时间原则实施任务调度。仿真测试结果表明,该算法具有较好的性能与负载均衡性。     

[2015年第2期] 云计算发展态势与关键技术进展

近年来,随着信息技术的突破和数据科学的崛起,云计算引起了产业界、学术界和政 府部门的高度关注。 文章简要阐述了云计算的产生背景与面临的问题, 探讨了云计算的虚拟 化技术、编程模式、数据存储管理、节能技术、云计算安全、资源调度等若干关键技术的研究进 展,并对云计算的发展态势提出了自己的看法,如： 云计算的标准化、云计算的未来属于 PaaS、 混合云将成用户首选、图应用作为一种云服务等。     

基于差值和改进TOPSIS的企业应用云计算技术研究

针对企业是否适合采纳应用云计算的问题提出一种新的基于差值和改进TOPSIS的多属性群决策方法。首先结合成本和技术-组织-环境(TOE)框架提出企业云计算采纳应用的评价标准,然后利用平均差和标准差最大化方法确定未知属性权重向量,再利用一种改进的逼近理想解(TOPSIS)方法获取决策者之间的权重,最后利用语言加权算术平均(LWAA)算子对各决策专家给出的偏好信息集结算出综合评价值以及上限和下限值,通过比较评价值和上限、下限值决定企业是否适合采纳应用云计算技术。算例分析证明了研究提出的方法的有效性。     

A longitudinal study of the actual value of big data and analytics: The role of industry environment

Despite the popularity of big data and analytics (BDA) in industry, research regarding the economic value of BDA is still at an early stage. Little attention has been paid to quantifying the longitudinal impact of organizational BDA implementation on firm performance. Grounded in organizational learning theory, this study empirically demonstrates the impact of BDA implementation on organizational performance and how industry environment characteristics moderate the BDA-performance relationships. Using secondary data regarding BDA implementation from 2010 to February 2020, we find that BDA implementation has a significant impact on two types of business value creation: operational efficiency and business growth. Furthermore, the impact of BDA on operational efficiency is amplified in less dynamic and complex environments, while the BDA-business growth relationship is more pronounced in more dynamic, complex, and munificent environments. Collectively, this study provides a theory-centric understanding of BDA’s economic benefits. The findings offer insights to firms about what actual benefits BDA implementation may generate and how firms may align the use of BDA with the industry environments they are operating in.     

Towards performance evaluation of cloud service providers for cloud data security

Today’s data is sensitive that requires privacy and security both from the cloud service providers (CSP) as well as from users in its all the form of data states: data at rest, while transferring data, enquiring data, and processing the data. Cloud computing has been applied in the health sector, national security services, banking and other business and companies that store confidential data into the cloud as we have seen in recent years. Therefore, information and data security is a crucial issue that needs to be addressed thoroughly in the cloud computing business. This research deals with the performance analysis of recent cloud data security models. This paper proposes cloud data security models based on Business Process Modeling Notations (BPMN) and simulation results can reveal performances issues related to data security as part of any organizations initiative on Business process management (BPM).     

The role of positive and negative valence factors on the impact of bigness of data on big data analytics usage

The number of firms that intend to invest in big data analytics has declined and many firms that invested in the use of these tools could not successfully deploy their project to production. In this study, we leverage the valence theory perspective to investigate the role of positive and negative valence factors on the impact of bigness of data on big data analytics usage within firms. The research model is validated empirically from 140 IT managers and data analysts using survey data. The results confirm the impact of bigness of data on both negative valence (i.e., data security concern and task complexity), and positive valence (i.e., data accessibility and data diagnosticity) factors. In addition, findings show that data security concern is not a critical factor in using big data analytics. The results also show that, interestingly, at different levels of data security concern, task complexity, data accessibility, and data diagnosticity, the impact of bigness of data on big data analytics use will be varied. For practitioners, the findings provide important guidelines to increase the extent of using big data analytics by considering both positive and negative valence factors.     

The effect of twitter dissemination on cost of equity: A big data approach

Reducing information asymmetry between investors and a firm can have an impact on the cost of equity, especially in an environment or times of uncertainty. New technologies can potentially help disseminate corporate financial information, reducing such asymmetries. In this paper we analyse firms’ dissemination decisions using Twitter, developing a comprehensive measure of the amount of financial information that a company makes available to investors (iDisc) from a big data of firms’ tweets (1,197,208 tweets). Using a sample of 4131 firm-year observations for 791 non-financial firms listed on the US NASDAQ stock exchange over the period 2009–2015, we find evidence that iDisc significantly reduces the cost of equity. These results are pronounced for less visible firms which are relatively small in size, have a low analyst following and a small number of investors. Highly visible firms are less likely to benefit from iDisc in influencing their cost of equity as other communication channels may have widely disseminated their financial information. Our investigations encourage managers to consider the benefits of directly spreading a firm’s financial information to stakeholders and potential investors using social media in order to reduce firm equity premium (COE).     

Understanding the impact of big data on firm performance: The necessity of conceptually differentiating among big data characteristics

This study uses the resource-based view to explore the impact of data volume, data velocity, and data variety, which are the main characteristics of big data, on firm performance and the mediating roles of data value and data veracity on these relationships. To test the research model, we collected data from 143 top and middle level managers in the United States. The findings show that data variety positively improves data value generation, whereas data volume and data velocity do not impact it. Additionally, while data volume negatively impacts data veracity, data velocity and data variety positively impact it. Findings indicate the necessity of conceptually differentiating among big data characteristics in investigating their impacts on firm outcomes instead of treating big data as a holistic variable. The study provides useful insights for researchers and managers willing to better understand the role of big data characteristics in influencing firm performance.     

Conceptualizing a model for adoption of cloud computing in education

Cloud computing is a pervasive computing paradigm that has revolutionized how computer infrastructure and services are delivered. Current research trends on cloud computing have been focused on the technology, applications, costs, benefits, and security of cloud computing at the organizational level within small and medium sized enterprises. Little research attention has been paid on adoption and usage of cloud computing at educational establishments and how contextual factors can influence diffusion and adoption of cloud computing. Universities in developing countries are faced with challenging socio-economic and political constraints that limit their ability to invest in expensive information systems to compete on the global stage. Using constructs from the diffusion of innovation theory and the technology acceptance model, this paper proposes a model that takes account of contextual, economic, and technological influences in the perception and adoption of cloud computing at universities in sub-Saharan Africa. Results from a pilot study, based on the model, through a survey of university lecturers and IS experts shows reliability and validity of the instrument and supports its usage for a more extensive study. Implications, potential contributions to research, and suggestions for future study are discussed.     

Organizational agility through outsourcing: Roles of IT alignment,cloud computing and knowledge transfer

This study introduces the concept of outsourcing appropriation based on adaptive structuration theory (AST) to explore how organizations attain IT-dependent organizational agility through outsourcing appropriation. It examines the mediating role of IT alignment and the moderating roles of the adoption of cloud computing and knowledge transfer. The proposed model was tested using survey data from a sample of 170 IT executives. Our results indicate that the consensus of appropriation (COA) and faithfulness of appropriation (FOA) are positively associated with IT-dependent organizational agility, and the relationships are partially mediated by IT alignment. When adoption of cloud computing is high, the positive relationship between IT alignment and IT-dependent organizational agility becomes stronger than when it is low. Interestingly, results also show that the moderating roles of the transfer of explicit knowledge and transfer of tacit knowledge in the relationship between outsourcing appropriation and IT-dependent organizational agility vary significantly. The transfer of explicit knowledge negatively moderates the relationship between COA and agility. The transfer of tacit knowledge positively moderates the relationship between FOA and agility. These findings deepen our understanding of how organizations gain IT-dependent organizational agility in the context of IT outsourcing.