Big Data Veracity Research Articles

Integrating human-centric automation and sustainability through the NAToRM framework: A neuromorphic computing approach for resilient industry 5.0 supply chains

Industry 5.0 supply chains face critical challenges in effectively managing the rapidly growing volume, variety, velocity, and veracity of big data while simultaneously ensuring sustainability, privacy, and ethical practices. The complex and interconnected nature of modern supply networks and the swift adoption of advanced technologies have created an urgent need for innovative frameworks to navigate these multifaceted challenges. Existing approaches often fail to adequately address the unique demands of Industry 5.0, lacking the ability to process data in real time, uncover deep insights, and enable dynamic, risk-informed decision-making. Moreover, there is a pressing need for frameworks that emphasize interdisciplinary collaboration and proactively address the potential negative impacts of emerging technologies. This paper introduces a novel, multidisciplinary framework that integrates cutting-edge techniques to tackle these challenges head-on, paving the way for more resilient, intelligent, and adaptable supply chains in the Industry 5.0 era.

Data-Centric Solutions for Addressing Big Data Veracity with Class Imbalance, High Dimensionality, and Class Overlapping

Data-Centric Solutions for Addressing Big Data Veracity with Class Imbalance, High Dimensionality, and Class Overlapping

Big Data Analytics and Culture

The existing big data analytics measures were developed without considering the cultural dimensions of developing countries. This research aims to develop and validate measures for big data Vs and cultural big data analytics and study their impacts on the developing countries' big data value proposition. Following MacKenzie's and Shiau and Huang's scale development procedures, data was collected twice from individuals in a developing country to refine the scale and reexamine its properties. PLS methods were used to study the impacts of big data Vs and cultural big data analytics on the value proposition. The findings revealed that big data analytics snobbism and conformism positively impact big data value proposition. Similarly, big data volume, velocity, and variety positively impact the value proposition. Paradoxically, big data veracity and variability do not significantly affect the value proposition. Surprisingly, big data analytics fatalism negatively impacts the value proposition. Theoretical and practical contributions were offered.

A Data-Driven Innovation Model of Big Data Digital Learning and Its Empirical Study

Digital learning is the use of telecommunication technology to deliver information for education and training. As the increased acceleration of the propagation speed of the web, a lot of data collected by automated or semi-automated way. The 4s (Volume, Velocity, Variety and Veracity) of big data increase the challenge to extract useful value via systemic framework. This study aims to construct the data model of big data digital learning. Based on the digital learning data, data-driven innovation framework was proposed to identify data form and collect data. Bayesian network was proposed to capture learning model to extract user experience of students to enhance learning efficiency. Empirical study was conducted on a university to validate the proposed approach. The results have been implemented to support the strategies to improve student learning outcomes and competitiveness.

Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach

The design and management of smart cities and the IoT is a multidimensional problem. One of those dimensions is cloud and edge computing management. Due to the complexity of the problem, resource sharing is one of the vital and major components that when enhanced, the performance of the whole system is enhanced. Research in data access and storage in multi-clouds and edge servers can broadly be classified to data centers and computational centers. The main aim of data centers is to provide services for accessing, sharing and modifying large databases. On the other hand, the aim of computational centers is to provide services for sharing resources. Present and future distributed applications need to deal with very large multi-petabyte datasets and increasing numbers of associated users and resources. The emergence of IoT-based, multi-cloud systems as a potential solution for large computational and data management problems has initiated significant research activity in the area. Due to the considerable increase in data production and data sharing within scientific communities, the need for improvements in data access and data availability cannot be overlooked. It can be argued that the current approaches of large dataset management do not solve all problems associated with big data and large datasets. The heterogeneity and veracity of big data require careful management. One of the issues for managing big data in a multi-cloud system is the scalability and expendability of the system under consideration. Data replication ensures server load balancing, data availability and improved data access time. The proposed model minimises the cost of data services through minimising a cost function that takes storage cost, host access cost and communication cost into consideration. The relative weights between different components is learned through history and it is different from a cloud to another. The model ensures that data are replicated in a way that increases availability while at the same time decreasing the overall cost of data storage and access time. Using the proposed model avoids the overheads of the traditional full replication techniques. The proposed model is mathematically proven to be sound and valid.

An effective veracity management for sentiment analysis in social media using crowdsourcing and multi‐set feature learning

An effective veracity management for sentiment analysis in social media using crowdsourcing and multi‐set feature learning

IEEE Access Special Section Editorial: Cloud-Fog-Edge Computing in Cyber–Physical–Social Systems (CPSS)

Cyber–Physical–Social Systems (CPSS) integrate the cyber, physical, and social spaces together. One of the ultimate goals of cyber–physical–social systems is to make our lives more convenient and intelligent by providing prospective and personalized services for users. To achieve this goal, a wide range of data in CPSS are employed as the starting point for research, since the data contain the users’ historical behavior trajectory and the users’ demand preference. Generated and collected from social and physical spaces and integrated into the cyberspace, CPSS data are complex and heterogeneous, recording all aspects of users’ lives in the forms of image, audio, video, and text. Generally, the collected or generated data in CPSS satisfy the 4Vs (volume, variety, velocity, and veracity) of big data. Thus, knowing how to deal with CPSS big data efficiently is the key to providing services for users. From another perspective, CPSS big data are specified as the global historical data and the local real-time data. Cloud computing, as a powerful paradigm for implementing the data-intensive applications, has an irreplaceable role in processing global historical data. On the other hand, with the increasing computing capacity and communication capabilities of mobile terminal devices, fog-edge computing, as an important and effective supplement of cloud computing, has been widely used to process local real-time data. Therefore, the question of how to systematically and efficiently process CPSS big data (including both the global historical data and the local real-time data) in CPSS has become the key for providing services. The goal of this Special Section is therefore to provide insights and views into the area of Cloud-Fog-Edge Computing in CPSS, as well as to provide directions for research in the field.

An Enhanced Framework to Ensure Big Data Veracity in Social Business Intelligence

An Enhanced Framework to Ensure Big Data Veracity in Social Business Intelligence

A Case Study of Return On Investment for Multi-sites Test Handler in The Semiconductor Industry Through Theory of Industry 4.0 ROI Relativity

<p class="0abstract">The Fourth Industrial Revolution started in the year 2011 with the aim to improve productivity similar with that of the previous three industry revolutions that occurred 200 years ago. The revolutions were promoted and implemented to improve the efficiency and speed of productivity. However, Industry 4.0 is likely a supplementation of the existing approach, with the purpose to centralize the processes and factories together to be controlled by a central console. The goal is to procure productivity and manufacturing data to enable data analysis so that the industry’s performance affected by big data variables, namely, velocity, veracity, variety, value, and visibility are monitored and rectified for continued productivity. This research initiates the development of a theory of Industry 4.0 ROI Relativity from the economic theory of firms, incorporating the pick-and-place test equipment and Industry 4.0 variables. A case study through the experimental research approach (ERA) was conducted by measuring the effects of velocity and veracity accuracy on the good-unit per hour (UPH), profit, and return on investment (ROI) of the pick-and-place test equipment and Industry 4.0. Then, the data were analyzed with Pearson correlation coefficient to determine inter-correlations among the velocity and veracity accuracy with the UPH, profit, and ROI. This research concluded that a significant average and negative correlation exists among velocity, UPH, profit, and ROI. Furthermore, the inter-correlation analysis results show a significant average and positive correlation among veracity accuracy percentage, UPH, profit, and ROI.</p>

Towards an Archaeological‐Ethnographic Approach to Big Data: Rethinking Data Veracity

For its volume, velocity, and variety (the 3 Vs), big data has been ever more widely used for decision‐making and knowledge discovery in various sectors of contemporary society. Since recently, a major challenge increasingly recognized in big data processing is the issue of data quality, or the veracity (4th V) of big data. Without addressing this critical issue, big data‐driven knowledge discoveries and decision‐making can be very questionable. In this paper, we propose an innovative methodological approach, an archaeological‐ethnographic approach that aims to address the challenge of big data veracity and to enhance big data interpretation. We draw upon our three recent case studies of fake or noise data in different data environments. We approach big data as but another kind of human behavioral traces in human history. We call to combine ethnographic data in interpreting big data, including problematic data, in broader contexts of human behaviors.

IoT Based Decision Making System to Improve Veracity of Big Data

Data are vital to help decision making. On the off chance that data have low veracity, choices are not liable to be sound. Internet of Things (IoT) quality rates big data with error, irregularity, deficiency, trickery, and model guess. Improving data veracity is critical to address these difficulties. In this article, we condense the key qualities and difficulties of IoT, which impact data handling and decision making. We audit the scene of estimating and upgrading data veracity and mining indeterminate data streams. Also, we propose five suggestions for future advancement of veracious big IoT data investigation that are identified with the heterogeneous and appropriated nature of IoT data, self-governing basic leadership, setting mindful and area streamlined philosophies, data cleaning and handling procedures for IoT edge gadgets, and protection safeguarding, customized, and secure data administration.

Veracity in big data: How good is good enough.

Veracity, one of the five V's used to describe big data, has received attention when it comes to using electronic medical record data for research purposes. In this perspective article, we discuss the idea of data veracity and associated concepts as it relates to the use of electronic medical record data and administrative data in research. We discuss the idea that electronic medical record data are "good enough" for clinical practice and, as such, are "good enough" for certain applications. We then propose three primary issues to attend to when establishing data veracity: data provenance, cross validation, and context.

Big Data Analytics for Healthcare System

Debate on big data analytics has earned a remarkable interest in industry as well as academia due to knowledge, information and wisdom extraction from big data. Big Data is a game changer for the overall effectiveness of maintaining data centres, because of its potential as a powerful tool in collecting, storing, managing and maintaining information in different forms. The significance of big data enables cost reductions, time reductions, new product development and optimized offerings, and smart decision making. Big data is playing a vital role in different industries like government, education, media, retail, banking, tourism, airlines and healthcare. Among many industries, health care industry is using big data for various purposes successfully by having efficient tools & analytical techniques. The objective of this paper is to identify the applications of Big Data Analytics in the health sector to help the patients, the healthcare providers and the hospital staff to work efficiently and to make the healthcare administration in a smoother way. The data which is collected for analysis purposes in various sources in health care industry are presented in this paper. Research identifies the recent surge in healthcare data as the key to improving health outcomes and reducing the ever increasing healthcare costs and to manage the voluminous amounts of healthcare data generated by patients in today’s healthcare arena. This Paper provides a brief study of 7v’s such as velocity, volume, variety, veracity, variability, visualization and value of Big Data in healthcare to utilize its power for specific research and real world problems. This paper provides an overview of architectural framework for the application of big data analytics in healthcare and some of the most popularly used tools which are available in different platforms to analyze big data are discussed to offer better healthcare services.

The relationship between intermittent coherence and precision of ISBAS InSAR ground motion velocities: ERS-1/2 case studies in the UK

Retrieving ground motion information for non-urban and semi-vegetated areas using differential Interferometric Synthetic Aperture Radar (InSAR) and Small Baseline Subset (SBAS) approaches with C-band satellite radar imagery is challenging due to temporal decorrelation. By exploiting six stacks of medium resolution ERS-1/2 SAR images acquired between 1992 and 2000 over four regions of interest in the UK, this paper demonstrates the performance of the recently developed processing method Intermittent SBAS (ISBAS). This approach builds upon the conventional low-resolution SBAS method and, by relaxing the approach to selecting image pixels to process and accounting for the intermittent nature of non-urban targets, is capable to extend the coverage of motion results across the full range of land cover types, even those typically unfavourable for InSAR. On average, the new ISBAS implementation provides 4 to 26 times more coverage than SBAS for the processed regions, with the spatial coverage of ground motion solutions increasing from only 4–12% land pixels with SBAS, to 39–99% with ISBAS. Despite relying only on temporal subsets of the networks of small baseline interferograms, intermittently coherent pixels show velocity standard errors of 0.8–1.4mm/year on average, hence retain sub-millimetre to millimetre precision. The empirical relationships between intermittent coherence and standard errors in the estimated ground motion velocity are computed for each of the six datasets, and confirm that errors are controlled by the number of independent observations used for each image pixel to extract the ISBAS solution. In particular, velocity standard errors εvel for the intermittently coherent pixels are inversely proportional to the square root of the number of best coherence interferograms used, ni, and can be modelled as εvel=11/ni mm/year on average for the six datasets. The established empirical relationship also allows informed decisions on the ISBAS threshold for ni to be made. This is achieved by setting the maximum acceptable error in the velocity estimate εMAX, and then computing the corresponding minimum ni to accept an intermittently coherent pixel that will guarantee the desired precision. In the present era of ‘big SAR data’ and their derived ‘big InSAR data’, we discuss perspectives on the use of huge datasets of thousands or even millions of ground deformation time series – such as those produced using ISBAS, with a particular focus on the veracity of big data and the need for a quality assessment check-point in the ‘big InSAR data’ cycle.

Methods for enhancing the reproducibility of clinical epidemiology research in linked electronic health records: results and lessons learned from the CALIBER platform

ABSTRACTObjectivesElectronic health records (EHR) across primary, secondary, and tertiary care are increasingly being linked for research at a population level. The increasing volume, variety, velocity, and veracity of big biomedical data makes research reproducibility challenging. Research reproducibility and replicability is essential for the external validity and generalizability of scientific findings and the lack of standardized approaches and tools and relative opaqueness of data manipulation methods is detrimental to their integrity. The objective of this study was to explore, evaluate and propose methods, tools and approaches for addressing some of the challenges associated with reproducibility when using linked national electronic health records for research.
 ApproachWe systematically searched literature and internet resources for well-established and appropriate methods, tools, and approaches used in related scientific disciplines. The identified techniques were systematically evaluated in terms of their capacity to facilitate reproducible research in routinely collected health data across the life course of a research project: from protocol creation and raw data curation to data transformation and statistical analysis though to finding dissemination and impact. Most importantly, the identified techniques were tested and applied in a contemporary database of linked electronic health records. CALIBER is a research data platform of linked national electronic health records from primary care (Clinical Practice Research Datalink), secondary care (Hospital Episode Statistics), acute coronary syndrome disease registry (Myocardial Ischaemia National Audit Project) and cause-specific mortality (Office for National Statistics) for roughly 2 million adults.
 ResultsFirstly, we present the review of methods and approaches which we identified through our search. Secondly, we propose a set of recommendations for applying them within the context of research projects making use of linked routinely collected health data. Focal interests included: a) documentation of data (attributes, relationships, and interpretation), b) data processing (source code, instructions, and parameters), c) results (visualizations, figures), and any supplementary material. Thirdly, we present approaches around a) raw data curation using international metadata standards, b) study protocol encoding, c) provenance and sharing of data transformation and statistical analysis operations, d) public and private data retention, and e) computable EHR-driven phenotypes.
 ConclusionThe complexity and size of routinely collected health data is increasing through linkages across distributed data sources. The scientific community benefits from findings which can be replicated. This study presents a number of methods, tools and approaches across the project life course for ensuring that their research studies are reproducible and replicable from the wider scientific community.

Toward intrusion detection using belief decision trees for big data

Big data refers to datasets that we cannot manage with standard tools and within which lie valuable information previously hidden. New data mining techniques are needed to deal with the increasing size of such data, their complex structure as well as their veracity which is on covering questions of data imperfection and uncertainty. Even though big data veracity is often overlooked, it is very challenging and important for an accurate and reliable mining and knowledge discovery. This paper proposes MapReduce-based belief decision trees for big data as classifiers of uncertain large-scale datasets. The proposed averaging and conjunctive classification approaches are experimented for intrusion detection on KDD’99 massive intrusion dataset. Several granularity attacks’ levels have been considered depending on whether dealing with whole kind of attacks, or grouping them in categories or focusing on distinguishing normal and abnormal connections.

Comparing and Analyzing the Characteristics of Hadoop, Cassandra and Quantcast File Systems for Handling Big Data

Objective: With the emergence of the notion of “Internet of Things (IoT)”, colossal amount of information is being generated through the sensors and other computing devices and chips. This paper is an attempt to provide a lucid comparison among three prominent technologies used for handling Big Data, viz. Hadoop Distributed File System, Cassandra file system and Quantcast file system. Apart for these three premier file systems, the paper also explores a newly proposed A train Distributed System for handling Big Data. Methods: An inner perspective of the above stated file systems in details considering various aspects for handling big data has been described. The paper also provides sagacity on the situations wherein these technologies are useful. Findings: Effective tackling of the five V’s (Variety, Volume, Velocity, Veracity and Value) of Big Data has become a challenging task for the researcher around the world. Hadoop is one such technology which is open source and is capable of handling big data in an effective manner. It breaks the big data into fixed sized chunks known as block and these blocks are saved at distinct locations in a distributed manner. The Cassandra file system is an alternative to Hadoop which eliminates the single point failure problem of Hadoop as it follows master-less peer to peer distributed ring architecture instead of client server architecture. The third technology is the quantcast file system which is written in C++ language. It also handles the big data in an effective and efficient manner. Moreover it claims to save upto fifty percent of the disk space by implementing erasure encoding. Application: The concerned organization to use any of these available frameworks for handling big data depending upon their nature of needs.

Big Data in radiation therapy: challenges and opportunities.

Data collected and generated by radiation oncology can be classified by the Volume, Variety, Velocity and Veracity (4Vs) of Big Data because they are spread across different care providers and not easily shared owing to patient privacy protection. The magnitude of the 4Vs is substantial in oncology, especially owing to imaging modalities and unclear data definitions. To create useful models ideally all data of all care providers are understood and learned from; however, this presents challenges in the guise of poor data quality, patient privacy concerns, geographical spread, interoperability and large volume. In radiation oncology, there are many efforts to collect data for research and innovation purposes. Clinical trials are the gold standard when proving any hypothesis that directly affects the patient. Collecting data in registries with strict predefined rules is also a common approach to find answers. A third approach is to develop data stores that can be used by modern machine learning techniques to provide new insights or answer hypotheses. We believe all three approaches have their strengths and weaknesses, but they should all strive to create Findable, Accessible, Interoperable, Reusable (FAIR) data. To learn from these data, we need distributed learning techniques, sending machine learning algorithms to FAIR data stores around the world, learning from trial data, registries and routine clinical data rather than trying to centralize all data. To improve and personalize medicine, rapid learning platforms must be able to process FAIR “Big Data” to evaluate current clinical practice and to guide further innovation.

Leveraging Frontline Employees’ Small Data and Firm-Level Big Data in Frontline Management

The advent of new forms of data, modern technology, and advanced data analytics offer service providers both opportunities and risks. This article builds on the phenomenon of big data and offers an integrative conceptual framework that captures not only the benefits but also the costs of big data for managing the frontline employee (FLE)-customer interaction. Along the positive path, the framework explains how the “3Vs” of big data (volume, velocity, and variety) have the potential to improve service quality and reduce service costs by influencing big data value and organizational change at the firm and FLE levels. However, the 3Vs of big data also increase big data veracity, which casts doubt about the value of big data. The authors further propose that because of heterogeneity in big data absorptive capacities at the firm level, the costs of adopting big data in FLE management may outweigh the benefits. Finally, while FLEs can benefit from big data, extracting knowledge from such data does not discount knowledge derived from FLEs’ small data. Rather, combining and integrating the firm’s big data with FLEs’ small data are crucial to absorbing and applying big data knowledge. An agenda for future research concludes.

Veracity of Big Data

Veracity of Big Data