Big Data Capabilities Research Articles

Research on Optimization of Big Data Fusion QM Model Based on Block-chain Distribution

In terms of poor classification and detection effect of big data fusion QM model as well as weak feature mining ability, a big data fusion QM model based on block-chain distribution optimization is proposed in the paper. Moreover, a block storage structure model for the distribution of big data is first established on the block-chain to perform shared scheduling according to the attribute distribution of the distributed big data on the block-chain, and then the big data of the distributed big data on the block-chain is mined. On this basis, the differentiated feature distributed retrieval method is used to cluster the distributed big data information on the block-chain, and the rough set feature quantity is extracted from the fuzzy cluster center. Meanwhile, combining the fuzzy correlation feature detection method, the information of the block-chain distributed big data is reorganized to extract the statistical features of each module. In addition, the quantitative recursion in the process of block-chain distributed big data fusion is analyzed, so that the optimization design of the QM model of block-chain distributed big data fusion can be realized. The simulation results show that the feature resolution ability of block-chain distributed big data fusion with the method proposed in the paper is better, and the classification detection and feature mining capabilities of block-chain distributed big data are improved, which has high application value in the block-chain distributed big data information retrieval and scheduling.

Alignment capabilities of big data’s value creation in the context of service delivery processes

PurposeWhile big data creates business value, knowledge on how value is created remains limited and research is needed to discover big data’s value mechanism. The purpose of this paper is to explore value creation capabilities of big data through an alignment perspective.Design/methodology/approachThe paper is based on a single case study of a service division of a large Danish wind turbine generator manufacturer based on 18 semi-structured interviews.FindingsA strategic alignment framework comprising human, information technology, organization, performance, process and strategic practices are used as a basis to identify 15 types of alignment capabilities and their inter-dependent variables fostering the value creation of big data. The alignment framework is accompanied by seven propositions to obtain alignment of big data in service processes.Research limitations/implicationsThe study demonstrates empirical anchoring of how alignment capabilities affect a company’s ability to create value from big data as identified in a service supply chain.Practical implicationsService supply chains and big data are complex matters. Therefore, understanding how alignment affects a company’s ability to create value of big data may help the company to overcome challenges of big data.Originality/valueThe study demonstrates how value from big data can be created following an alignment logic. By this, both critical and complementary alignment capabilities have been identified.

The Impact of Big Data Analytics Capabilities on Competitive Advantage Applied on “Etisalat Egypt”

The purpose of this study is to investigate how Big Data Analytics is used and perceived to affect a firm’s competitive advantage. The study will focus on the telecommunication sector, due to the high volume of data being used for analysis in this sector; it is always an area of great interest. This paper investigates the role that Big Data Analytics Capabilities play in achieving a competitive advantage in the Egyptian telecommunication sector. Using the Dynamic Capabilities framework, this paper looks at how a firm can gain an advantage from big data. For collecting data, the study adopted the questionnaire method; the study used the questionnaire-based survey method; the study used two questionnaires; one for big data analysis department employees to investigate the independent variable Big Data Analytics capabilities, and the other one for the top and middle managerial levels employees to investigate the dependent variable competitive advantage. The results show that there is a statistically significant impact of “Big Data Analytics Capabilities” on Competitive advantage. Infrastructure flexibility and management capabilities were relatively more important than Personnel Expertise between all the dimensions of Big Data Analytics capabilities. But these differences are small, so all the dimensions should be given the same value in building Big Data Analytics capabilities. Management in the telecommunication sector should take into account that enhancing Big Data capabilities and considering big data as part of their operations can improve their Competitive Advantage and their Performance.

Research on the Application and Development of Big Data Technology in China’s Computer Industry under the Going-out Strategy

Big data industry should give appropriate support and incentive policies to encourage it to strengthen the research and development of big data core technologies and effectively improve the practical application capabilities of big data. Considering the development of big data enterprises, it must be based on the support of advanced technology. Of course, as an enterprise, the operating cost is also one of the key considerations. In this regard, in order to promote the profit growth of enterprises, the government must increase the research and development of special technologies for big data and actively promote the diffusion of big data technology within and among industries, in order to promote the dividend of big data industry. Released as soon as possible, this way grasped the convergence of big data industry and traditional industry and then speed up the transformation of scientific and technological achievements.

Firm-level capabilities towards big data value creation

Big data has played an increasingly important role in using data to improve business value. In response to several big data challenges, the purpose of this study is to identify firm-level capabilities required to create value from big data. The adjacent theories of business process management and IT business value underpinned the study, together with an in-depth case study that led to the identification of twenty-four types of capabilities related to IT, process, performance, human, strategic, and organizational practices. The findings confirmed the application of practices and capabilities of adjacent theories, as well as certain practices and attributes that were both changed and reinforced at the intersection of big data. As an outstanding additional support to the extant big data studies, this work empirically confirms and portrays hitherto unexplored capabilities of big data and set their roles, thus providing a holistic overview of firm-level capabilities that are required for big data value creation.

Big data management and environmental performance: role of big data decision-making capabilities and decision-making quality

PurposeThis study is undertaken to examine the antecedents and role of big data decision-making capabilities toward decision-making quality and environmental performance among the Chinese public and private hospitals. It also examined the moderating effect of big data governance that was almost ignored in previous studies.Design/methodology/approachThe target population consisted of managerial employees (IT experts and executives) in hospitals. Data collected using a survey questionnaire from 752 respondents (374 respondents from public hospitals and 378 respondents from private hospitals) was subjected to PLS-SEM for analysis.FindingsFindings revealed that data management challenges (leadership focus, talent management, technology and organizational culture for big data) are significant antecedents for big data decision-making capabilities in both public and private hospitals. Moreover, it was also found that big data decision-making capabilities played a key role to improve the decision-making quality (effectiveness and efficiency), which positively contribute toward environmental performance in public and private hospitals of China. Public hospitals are playing greater attention to big data management for the sake of quality decision-making and environmental performance than private hospitals.Practical implicationsThis study provides guidelines required by hospitals to strengthen their big data capabilities to improve decision-making quality and environmental performance.Originality/valueThe proposed model provides an insight look at the dynamic capabilities theory in the domain of big data management to tackle the environmental issues in hospitals. The current study is the novel addition in the literature, and it identifies that big data capabilities are envisioned to be a game-changer player in effective decision-making and to improve the environmental performance in health sector.

Adapting big data standards, maturity models to smart grid distributed generation: critical review

Big data standards and capability maturity models (CMMs) help developers build applications with reduced coupling and increased breadth of deployment. In smart grids, stakeholders currently work with data management techniques that are unique and customised to their own goals, thereby posing challenges for grid-wide integration and deployment. Although big data standards and CMMs exist for other domains, no work in the literature considers adapting them to smart grids, which will benefit from both. Further, existing smart grid standards and CMMs do not fully account for big data challenges. This study bridges the gap by analysing the role of big data in smart grids, and explores if and how big data standards and CMMs can be adapted specifically to ten distributed generation (DG) use-cases that use big data. In doing so, this work provides a useful starting point for researchers and industry members developing standards and CMM assessments for smart grid DG.

A New Therapeutic Framework for Atrial Fibrillation Drug Development

Atrial fibrillation (AF) is a highly prevalent cardiac arrhythmia and cause of significant morbidity and mortality. Its increasing prevalence in aging societies constitutes a growing challenge to global healthcare systems. Despite substantial unmet needs in AF prevention and treatment, drug developments hitherto have been challenging, and the current pharmaceutical pipeline is nearly empty. In this review, we argue that current drugs for AF are inadequate because of an oversimplified system for patient classification and the development of drugs that do not interdict underlying disease mechanisms. We posit that an improved understanding of AF molecular pathophysiology related to the continuous identification of novel disease-modifying drug targets and an increased appreciation of patient heterogeneity provide a new framework to personalize AF drug development. Together with recent innovations in diagnostics, remote rhythm monitoring, and big data capabilities, we anticipate that adoption of a new framework for patient subsegmentation based on pathophysiological, genetic, and molecular subsets will improve success rates of clinical trials and advance drugs that reduce the individual patient and public health burden of AF.

Hunting for open clusters in Gaia DR2: 582 new open clusters in the Galactic disc

Context. Open clusters are key targets for studies of Galaxy structure and evolution, and stellar physics. Since the Gaia data release 2 (DR2), the discovery of undetected clusters has shown that previous surveys were incomplete. Aims. Our aim is to exploit the Big Data capabilities of machine learning to detect new open clusters in Gaia DR2, and to complete the open cluster sample to enable further studies of the Galactic disc. Methods. We use a machine-learning based methodology to systematically search the Galactic disc for overdensities in the astrometric space and identify the open clusters using photometric information. First, we used an unsupervised clustering algorithm, DBSCAN, to blindly search for these overdensities in Gaia DR2 (l, b, ϖ, μα*, μδ), and then we used a deep learning artificial neural network trained on colour–magnitude diagrams to identify isochrone patterns in these overdensities, and to confirm them as open clusters. Results. We find 582 new open clusters distributed along the Galactic disc in the region |b| < 20°. We detect substructure in complex regions, and identify the tidal tails of a disrupting cluster UBC 274 of ∼3 Gyr located at ∼2 kpc. Conclusions. Adapting the mentioned methodology to a Big Data environment allows us to target the search using the physical properties of open clusters instead of being driven by computational limitations. This blind search for open clusters in the Galactic disc increases the number of known open clusters by 45%.

Big Data, IoT Wearable and m Health Cloud Platform Integration Triads a Logical Way to Patient Health Monitoring

IoT along with big data capabilities is useful in fall detection, medical fridges, sportsman care, patient surveillance, chronic disease management, sleep control and monitoring, etc. Every year a large number of patients are identified with diabetes or cardiac disorders. There is a greater need to handle many patients with the existing medical staff and doctors like cardiologists and diabetologists. This chapter aims at establishing the logical conceptualization and linkages of IoT enabled system linkages with wearable, big-data platforms and cloud-based mhealth delivery. The study subsequently aims at qualitatively and quantitatively validating and putting forth a feasible nuanced understanding framework linking the major contemporary technology triads/automated care delivery process platform in the healthcare context with prime focus on patient health monitoring and care-delivery.

Conceptualizing big data practices

PurposeThe purpose of this paper is to provide a conceptual understanding of Big Data practices in organizations, which will enable exploring the operational and strategic roles of Big Data in organizational performance.Design/methodology/approachBoth academic and non-academic literature studies on Big Data were reviewed so as to capture what was known about Big Data practices. Qualitative interviews were conducted with firm executives about Big Data practices in their organizations. Both literature review and interview results were analyzed based on the dynamic capabilities perspective.FindingsThe analysis of the results suggests that Big Data capability develops when the resources parts of Big Data and the skill and competency parts are integrated and then grow into a dynamic capability.Research limitations/implicationsThis study contributes to the literature with the concept of Big Data capability that best characterizes Big Data practices in organizations. Validity of this concept should be tested in empirical studies.Originality/valueThe development of the concept of Big Data capability helps to fill a gap in the research literature that theoretical understanding of big data practices is lacking or needs to be updated. It motivates practitioners to develop this capability so as to create and maintain their strategic advantage.

The democracy in a network society: Problems and prospects of development

This article examines the role of the new media and social networks as a factor in the development of deliberative democracy. In the late 1990s, it was thought that the communication revolution would be a way to overcome the crisis of representative democracy. With the development of the Internet, the transition from a hierarchical model of communications to a network was supposed to bring changes to the sphere of politics. New media and social networks should guarantee ac- cess of civil society to the political public sphere and help narrow the gap between the elite and the people. Requirements are being formulated to increase the transparency and accountability of state institutions, and to strengthen policy control. It was believed that the Internet will become a place where the principles of deliberative democracy will be embodied. But now we should admit that the new media development causes disappointment, and a times anxiety. Instead of the growth of public discussions, we may encounter “echo chambers”, a victim of the essence of information for effect, which as a result, leads to the radicalization of citizens. Governments and IT-corporations exploit the increased capabilities of social networks and Big Data to control and manipulate information, which jeopardizes the possibility of free discussion. The impact on emotions and simple slogans turned out to be more acceptable in the Internet age than a reflec- tive discussion on social issues. The communication revolution has led to changes in the public sphere, but at the same time, it can lead to the erosion of democracy.

Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case

Industry 4.0 is considered to be the fourth industrial revolution introducing a new paradigm of digital, autonomous, and decentralized control for manufacturing systems. Two key objectives for Industry 4.0 applications are to guarantee maximum uptime throughout the production chain and to increase productivity while reducing production cost. As the data-driven economy evolves, enterprises have started to utilize big data techniques to achieve these objectives. Big data and IoT technologies are playing a pivotal role in building data-oriented applications such as predictive maintenance.In this paper, we use a systematic methodology to review the strengths and weaknesses of existing open-source technologies for big data and stream processing to establish their usage for Industry 4.0 use cases. We identified a set of requirements for the two selected use cases of predictive maintenance in the areas of rail transportation and wind energy. We conducted a breadth-first mapping of predictive maintenance use-case requirements to the capabilities of big data streaming technologies focusing on open-source tools. Based on our research, we propose some optimal combinations of open-source big data technologies for our selected use cases.

Multivariate Big Data Analysis for intrusion detection: 5 steps from the haystack to the needle

The research literature on cybersecurity incident detection & response is very rich in automatic detection methodologies, in particular those based on the anomaly detection paradigm. However, very little attention has been devoted to the diagnosis ability of the methods, aimed to provide useful information on the causes of a given detected anomaly. This information is of utmost importance for the security team to reduce the time from detection to response. In this paper, we present Multivariate Big Data Analysis (MBDA), a complete intrusion detection approach based on 5 steps to effectively handle massive amounts of disparate data sources. The approach has been designed to deal with the main characteristics of Big Data, that is, the high volume, velocity and variety. The core of the approach is the Multivariate Statistical Network Monitoring (MSNM) technique proposed in a recent paper. Unlike in state of the art machine learning methodologies applied to the intrusion detection problem, when an anomaly is identified in MBDA the output of the system includes the detail of the logs of raw information associated to this anomaly, so that the security team can use this information to elucidate its root causes. MBDA is based in two open software packages available in Github: the MEDA Toolbox and the FCParser. We illustrate our approach with two case studies. The first one demonstrates the application of MBDA to semistructured sources of information, using the data from the VAST 2012 mini challenge 2. This complete case study is supplied in a virtual machine available for download. In the second case study we show the Big Data capabilities of the approach in data collected from a real network with labeled attacks.

Small Numbers Bargaining in the Age of Big Data: Evidence From a Two-Sided Labor Matching Platform

In this study, we examine how firms use the big data capabilities of third-party platforms to find transaction partners. While use of the platform’s big data capabilities creates value by lowering search costs, firms may capture little of this value if they become entirely dependent on the platform. We therefore argue that firms will invest in strategic redundancy, i.e., they will continue to rely partly on their internal screening capabilities to identify partners so as to maintain their bargaining power relative to the platform. We further predict that this reliance on internal screening will be greater the lower the relative advantage of the platform’s big data capabilities and the more salient the threat to the firm’s bargaining power. We test these predictions in the context of an online labor platform, using a research discontinuity design to examine the effect of the platform’s recommendations on the firm’s decision to hire an applicant. Consistent with our theory, we find that firms' use of the platform’s recommendations is lower in later stages of the hiring process, in larger sub-markets, and for firms with greater experience on the platform. Our study thus sheds new light on how firms make use of (third-party) big data techniques, showing that firms may strategically choose to limit such use in order to maintain independence.

Strategic orientations, developmental culture, and big data capability

Prior research articulated the importance of developing a big data analytics capability but did not show how to cultivate this development. Drawing on the literature on this topic, this study develops the concept of Big Data capability, which enhances our understanding of Big Data practice beyond that captured in previous literature on the concept of big data analytics capability. This study further highlights the strategic implications of the concept by testing its relationship to three strategic orientations and one aspect of organizational culture. Findings show that customer, entrepreneurial, and technology orientations, and developmental culture are important contributors to the development of Big Data capability.

A survey on Image Data Augmentation for Deep Learning

Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augmentation algorithms discussed in this survey include geometric transformations, color space augmentations, kernel filters, mixing images, random erasing, feature space augmentation, adversarial training, generative adversarial networks, neural style transfer, and meta-learning. The application of augmentation methods based on GANs are heavily covered in this survey. In addition to augmentation techniques, this paper will briefly discuss other characteristics of Data Augmentation such as test-time augmentation, resolution impact, final dataset size, and curriculum learning. This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing Data Augmentation. Readers will understand how Data Augmentation can improve the performance of their models and expand limited datasets to take advantage of the capabilities of big data.

Big Data‐Savvy Teams’ Skills, Big Data‐Driven Actions and Business Performance

AbstractPrior studies on big data analytics have emphasized the importance of specific big data skills and capabilities for organizational success; however, they have largely neglected to investigate the use of cross‐functional teams’ skills and links to the role played by relevant data‐driven actions and business performance. Drawing on the resource‐based view (RBV) of the firm and on unique data collected from 240 big data experts working in global agrifood networks, we examine the links between the use of big data‐savvy (BDS) teams’ skills, big data‐driven (BDD) actions and business performance. BDS teams depend on multi‐disciplinary skills (e.g. computing, mathematics, statistics, machine learning and business domain knowledge) that help them turn their traditional business operations into modern data‐driven insights (e.g. knowing real‐time price changes and customer preferences), leading to BDD actions that enhance business performance. Our results, raised from structural equation modelling, indicate that BDS teams’ skills that produce valuable insights are the key determinants for BDD actions, which ultimately contribute to business performance. We further demonstrate that those organizations that emphasize BDD actions perform better compared to those that do not focus on such applications and relevant insights.

Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture

AbstractThe importance of big data and predictive analytics has been at the forefront of research for operations and manufacturing management. The literature has reported the influence of big data and predictive analytics for improved supply chain and operational performance, but there has been a paucity of literature regarding the role of external institutional pressures on the resources of the organization to build big data capability. To address this gap, this paper draws on the resource‐based view of the firm, institutional theory and organizational culture to develop and test a model that describes the importance of resources for building capabilities, skills and big data culture and subsequently improving cost and operational performance. We test our research hypotheses using 195 surveys, gathered using a pre‐tested questionnaire. Our contribution lies in providing insights regarding the role of external pressures on the selection of resources under the moderating effect of big data culture and their utilization for capability building, and how this capability affects cost and operational performance.

Ieee Access Special Section Editorial: Cloud and Big Data-Based Next-Generation Cognitive Radio Networks

In cognitive radio networks (CRN), secondary users (SUs) are required to detect the presence of the licensed users, known as primary users (PUs), and to find spectrum holes for opportunistic spectrum access without causing harmful interference to PUs. However, due to complicated data processing, non-real-time information exchange and limited memory, SUs often suffer from imperfect sensing and unreliable spectrum access. Cloud computing can solve this problem by allowing the data to be stored and processed in a shared environment. Furthermore, the information from a massive number of SUs allows for more comprehensive information exchanges to assist the resource allocation and interference management at the cloud center while relieving the stringent capacity demands in fronthaul links. Moreover, spectrum resources should be made available to more users, especially when the spectrum is underutilized but occupies a large band. Hence, cloud-based CRN can generate massive sensing samples that will benefit the applications of big data algorithms. The approaches to spectrum sensing and spectrum management can be greatly improved with decision-making capabilities of spectral big data.