State of the art in big data applications in microgrid: A review

The prospering Big data era is emerging in the power grid. Multiple world-wide studies are emphasizing the big data applications in the microgrid due to the huge amount of produced data. Big data analytics can impact the design and applications towards safer, better, more profitable, and effective power grid. This paper presents the recognition and challenges of the big data and the microgrid. The construction of big data analytics is introduced. The data sources, big data opportunities, and enhancement areas in the microgrid like stability improvement, asset management, renewable energy prediction, and decision-making support are summarized. Diverse case studies are presented including different planning, operation control, decision making, load forecasting, data attacks detection, and maintenance aspects of the microgrid. Finally, the open challenges of big data in the microgrid are discussed.     

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.     

Factors influencing effective use of big data: A research framework

Information systems (IS) research has explored “effective use” in a variety of contexts. However, it is yet to specifically consider it in the context of the unique characteristics of big data. Yet, organizations have a high appetite for big data, and there is growing evidence that investments in big data solutions do not always lead to the derivation of intended value. Accordingly, there is a need for rigorous academic guidance on what factors enable effective use of big data. With this paper, we aim to guide IS researchers such that the expansion of the body of knowledge on the effective use of big data can proceed in a structured and systematic manner and can subsequently lead to empirically driven guidance for organizations. Namely, with this paper, we cast a wide net to understand and consolidate from literature the potential factors that can influence the effective use of big data, so they may be further studied. To do so, we first conduct a systematic literature review. Our review identifies 41 factors, which we categorize into 7 themes, namely data quality; data privacy and security and governance; perceived organizational benefit; process management; people aspects; systems, tools, and technologies; and organizational aspects. To explore the existence of these themes in practice, we then analyze 45 published case studies that document insights into how specific companies use big data successfully. Finally, we propose a framework for the study of effective use of big data as a basis for future research. Our contributions aim to guide researchers in establishing the relevance and relationships within the identified themes and factors and are a step toward developing a deeper understanding of effective use of big data.     

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.     

The reality of virtual schools: A review of the literature

Virtual schooling was first employed in the mid-1990s and has become a common method of distance education used in K-12 jurisdictions. The most accepted definition of a virtual school is an entity approved by a state or governing body that offers courses through distance delivery – most commonly using the Internet. While virtual schools can be classified in different ways, the three common methods of delivery are by independent, asynchronous or synchronous means. Presently, the vast majority of virtual school students tended to be a select group of academically capable, motivated, independent learners. The benefits associated with virtual schooling are expanding educational access, providing high-quality learning opportunities, improving student outcomes and skills, allowing for educational choice, and achieving administrative efficiency. However, the research to support these conjectures is limited at best. The challenges associated with virtual schooling include the conclusion that the only students typically successful in online learning environments are those who have independent orientations towards learning, highly motivated by intrinsic sources, and have strong time management, literacy, and technology skills. These characteristics are typically associated with adult learners. This stems from the fact that research into and practice of distance education has typically been targeted to adult learners. The problem with this focus is that adults learn differently than younger learners. Researchers are calling for more research into the factors that account for K-12 student success in distance education and virtual school environments and more design research approaches than traditional comparisons of student achievement in traditional and virtual schools.     

Cross-cultural analysis in online community research: A literature review

Cross-cultural online community research can support theoretical generalizability, increase methodological robustness and give insights into user online behavior. The objective of this paper is to review the existing literature on comparative cross-cultural online community research in order to investigate the current state of the literature, extract conceptual patterns and identify methodological and emergent issues. This will inform the development of the field, map out research delimiters, and set out guidelines for future research. The findings from the literature review demonstrated five key areas of methodological difficulty in cross-cultural online community comparative analysis; sampling form, country selection, number of cultures compared, participant type and interpretation of data. Key themes that emerged from the literature included the use of the nation state as a unit of culture, a lack of definition of the concept of online community, and the impact of current theory on cross-cultural online community analyses. Recommendations in the areas of methodology, definition and theory are provided. These findings should be of interest to both specific online community researchers, and those in other multidisciplinary fields where online communities are being used as a research environment.     

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.     

Internet use that reproduces educational inequalities: Evidence from big data

Although the Internet has become ubiquitous in students' lives in school and at home, little is known about whether the Internet is used to close or reproduce educational inequalities. Drawing upon Bourdieu's notion of capital, there are two kinds of Internet use: capital-enhancing versus entertainment. This study used two big data analytic tools to examine interest in and usage of two highly popular websites that primarily target children and adolescents: KhanAcademy.org and CartoonNetwork.com. The former represents a capital-enhancing use of the Internet, while the latter represents an Internet use for entertainment. Data analysis revealed that high sociodemographic status was positively correlated with interest in Khan Academy, while low sociodemographic status was positively correlated with interest in Cartoon Network. This study provided some evidence that existing educational inequalities may be reproduced through unequal Internet use.     

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.     

A spatiotemporal compression based approach for efficient big data processing on Cloud

It is well known that processing big graph data can be costly on Cloud. Processing big graph data introduces complex and multiple iterations that raise challenges such as parallel memory bottlenecks, deadlocks, and inefficiency. To tackle the challenges, we propose a novel technique for effectively processing big graph data on Cloud. Specifically, the big data will be compressed with its spatiotemporal features on Cloud. By exploring spatial data correlation, we partition a graph data set into clusters. In a cluster, the workload can be shared by the inference based on time series similarity. By exploiting temporal correlation, in each time series or a single graph edge, temporal data compression is conducted. A novel data driven scheduling is also developed for data processing optimisation. The experiment results demonstrate that the spatiotemporal compression and scheduling achieve significant performance gains in terms of data size and data fidelity loss.     

Sentiment mining in a collaborative learning environment: capitalising on big data

ABSTRACT

The ability to exploit students’ sentiments using different machine learning techniques is considered an important strategy for planning and manoeuvring in a collaborative educational environment. The advancement of machine learning technology is energised by the healthy growth of big data technologies. This helps the applications based on Sentiment Mining (SM) using big data to become a common platform for data mining activities. However, very little has been studied on the sentiment application using a huge amount of available educational data. Therefore, this paper has made an attempt to mine the academic data using different efficient machine learning algorithms. The contribution of this paper is two-fold: (i) studying the sentiment polarity (positive, negative and neutral) from students’ data using machine learning techniques, and (ii) modelling and predicting students’ emotions (Amused, Anxiety, Bored, Confused, Enthused, Excited, Frustrated, etc.) using the big data frameworks. The developed SM techniques using big data frameworks can be scaled and made adaptable for source variation, velocity and veracity to maximise value mining for the benefit of students, faculties and other stakeholders.     

Industry classification with online resume big data: A design science approach

Industry classification is a vital step of industry analysis and competitive intelligence. However, existing schemes and methods are limited by the lagged information of firms’ business and the lack of consideration of the human resource aspects. In this paper, we adopt a design science approach to develop and evaluate a novel industry classification method by constructing a labor mobility network using online resume big data collected from the professional social network. We also propose a hierarchical extension of the community detection algorithm to better discover scalable firm clusters on the constructed network. The evaluation conducted on real-world datasets shows that our method outperforms the existing industry classification schemes and the state-of-the-art methods by improving their explanatory power and enlarging the cross-industry variation. Moreover, two application cases confirm the validity of our method in earlier revealing firms’ action of entering new industries.     

A review of the IT outsourcing literature: Insights for practice

This paper reviews research studies of information technology outsourcing (ITO) practice and provides substantial evidence that researchers have meaningfully and significantly addressed the call for academics to produce knowledge relevant to practitioners. Based on a review of 191 IT outsourcing articles, we extract the insights for practice on six key ITO topics relevant to practitioners. The first three topics relate to the early 1990s focus on determinants of IT outsourcing, IT outsourcing strategy, and mitigating IT outsourcing risks. A focus on best practices and client and supplier capabilities developed from the mid-1990s and is traced through to the late 2000s, while relationship management is shown to be a perennial and challenging issue throughout the nearly 20 years under study. More recently studies have developed around offshore outsourcing, business process outsourcing and the rise, decline and resurrection of application service provision. The paper concludes by pointing to future challenges and developments.     

A hybrid index for temporal big data

Temporal index provides an important way to accelerate query performance in temporal big data. However, the current temporal index cannot support the variety of queries very well, and it is hard to take account of the efficiency of query execution as well as the index construction and maintenance. In this paper, we propose a novel segmentation-based hybrid index B+-Tree, called SHB+- tree, for temporal big data. First, the temporal data in temporal table deposited is separated to fragments according to the time order. In each segment, the hybrid index is constructed by integrating the temporal index and the object index, and the temporal big data is shared by them. The performance of construction and maintenance is improved by employing the segmented storage strategy and bottom-up index construction approaches for every part of the hybrid index. The experimental results on benchmark data set verify the effectiveness and efficiency of the proposed method.     

Value co-creation between firms and customers: The role of big data-based cooperative assets

To better understand how big data interconnects firms and customers in promoting value co-creation, we propose a theoretical framework of big data-based cooperative assets based on evidence of multiple case studies. We identify four types of big data resources and four types of associated digital platforms, and we explore how firms develop the cooperative assets by transforming big data resources via the theoretical lens of service-dominant logic. This study offers a new theoretical perspective on value co-creation and an alternative competitive strategy in the era of big data for firms.     

The rise of “big data” on cloud computing: Review and open research issues

Cloud computing is a powerful technology to perform massive-scale and complex computing. It eliminates the need to maintain expensive computing hardware, dedicated space, and software. Massive growth in the scale of data or big data generated through cloud computing has been observed. Addressing big data is a challenging and time-demanding task that requires a large computational infrastructure to ensure successful data processing and analysis. The rise of big data in cloud computing is reviewed in this study. The definition, characteristics, and classification of big data along with some discussions on cloud computing are introduced. The relationship between big data and cloud computing, big data storage systems, and Hadoop technology are also discussed. Furthermore, research challenges are investigated, with focus on scalability, availability, data integrity, data transformation, data quality, data heterogeneity, privacy, legal and regulatory issues, and governance. Lastly, open research issues that require substantial research efforts are summarized.     

Preface: Security and privacy in big data clouds

This special issue assembles a set of twelve papers, which provide new insights on the security and privacy technology of big data in cloud computing environments. This preface provides overview of all articles in the viewpoint set.     

External integrity verification for outsourced big data in cloud and IoT: A big picture

As cloud computing is being widely adopted for big data processing, data security is becoming one of the major concerns of data owners. Data integrity is an important factor in almost any data and computation related context. It is not only one of the qualities of service, but also an important part of data security and privacy. With the proliferation of cloud computing and the increasing needs in analytics for big data such as data generated by the Internet of Things, verification of data integrity becomes increasingly important, especially on outsourced data. Therefore, research topics on external data integrity verification have attracted tremendous research interest in recent years. Among all the metrics, efficiency and security are two of the most concerned measurements. In this paper, we will bring forth a big picture through providing an analysis on authenticator-based data integrity verification techniques on cloud and Internet of Things data. We will analyze multiple aspects of the research problem. First, we illustrate the research problem by summarizing research motivations and methodologies. Second, we summarize and compare current achievements of several of the representative approaches. Finally, we introduce our view for possible future developments.     

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.