Downstream Consent: A Better Legal Framework for Big Data

Incidents in highly hazardous process industries (HHPI) are a major concern for various stakeholders due to the impact on human lives, environment, and potentially huge financial losses. Because process activities, location and products are unique, risk analysis techniques applied in the HHPI has evolved over the years. Unfortunately, some limitations of the various quantitative risk analysis (QRA) method currently employed means alternative or more improved methods are required. This research has obtained one such method called Big Data QRA Method. This method relies entirely on big data techniques and real-time process data to identify the point at which process risk is imminent and provide the extent of contribution of other components interacting up to the time index of the risk. Unlike the existing QRA methods which are static and based on unvalidated assumptions and data from single case studies, the big data method is dynamic and can be applied to most process systems. This alternative method is my original contribution to science and the practice of risk analysis The detailed procedure which has been provided in Chapter 9 of this thesis applies multiple change-point analysis and other big data techniques like, (a) time series analysis, (b) data exploration and compression techniques, (c) decision tree modelling, (d) linear regression modelling. Since the distributional properties of process data can change over time, the big data approach was found to be more appropriate. Considering the unique conditions, activities and the process systems use within the HHPI, the dust fire and explosion incidents at the Imperial Sugar Factory and the New England Wood Pellet LLC both of which occurred in the USA were found to be suitable case histories to use as a guide for evaluation of data in this research. Data analysis was performed using open source software packages in R Studio. Based on the investigation, multiple-change-point analysis packages strucchange and changepoint were found to be successful at detecting early signs of deteriorating conditions of component in process equipment and the main process risk. One such process component is a bearing which was suspected as the source of ignition which led to the dust fire and explosion at the Imperial Sugar Factory. As a result, this this research applies the big data QRA method procedure to bearing vibration data to predict early deterioration of bearings and final period when the bearing’s performance begins the final phase of deterioration to failure. Model-based identification of these periods provides an indication of whether the conditions of a mechanical part in process equipment at a particular moment represent an unacceptable risk. The procedure starts with selection of process operation data based on the findings of an incident investigation report on the case history of a known process incident. As the defining components of risk, both the frequency and consequences associated with the risk were obtained from the incident investigation reports. Acceptance criteria for the risk can be applied to the periods between the risks detected by the two change-point packages. The method was validated with two case study datasets to demonstrate its applicability as procedure for QRA. The procedure was then tested with two other case study datasets as examples of its application as a QRA method. The insight obtained from the validation and the applied examples led to the conclusion that big data techniques can be applied to real-time process data for risk assessment in the HHPI.

An overview of air quality analysis by big data techniques: Monitoring, forecasting, and traceability

As the volume of data generating is increasing day by day in this internet world, the term Big Data is becoming a very popular buzzword in today's market. Big Data is used in various sectors of the internet world. In this paper an effort is made to demonstrate that even the healthcare industries are stepping into Big Data pool to take all benefits from its various advanced tools and technologies. The paper presents the review of various research efforts made in healthcare domain using Big Data concepts and methodologies. The thought of Big Data can be used for better health planning. Its methodologies can be used for healthcare data analytics which helps in better decision making to increase the business value and customer interest and to provide eHealth services among various healthcare stakeholders by using messaging standards like Health Level?, Digital Imaging and Communications in Medicine (DICOM), Health Insurance Portability and Accountability (HIPAA), message broker etc. Big Data techniques can be applied to develop systems for the early diagnosis of disease, understand connection between HATS (HIV/AIDS Tuberculosis and Silicosis) and also to develop integrated data analytics platforms. After presenting these many positive progresses of Big Data on healthcare, the paper also presents the hurdles faced by healthcare systems in using Big Data technologies. Further the paper includes the list of various Big Data tools, few case studies, few applications which are worth implementing using Big Data in healthcare followed by the concluding remarks.

Big Data has become an important technical force to advance many industries. Many big data techniques have been invented and open-sourced for public use. The small businesses, which form the major part of the whole business world, still face great challenges in applying big data solutions to their own businesses. However, there are few cases that have been published to report the procedure of engineering big data in small businesses. This fact leads to insufficient references for small businesses to take to figure out their own scenarios of applying big data. In this paper, we report a pilot case study on a small business applying big data to an electric signal process project. It describes in detail the procedure of analyzing the business logics, identifying big data requirements, and selecting appropriate big data techniques for engineering solutions. We also share lessons learned from this case study, which can be a general reference for those small businesses on trying their own big data application cases.

The purpose of this paper is to examine the impact of using big data techniques on the effectiveness of the internal control for companies listed on the Egyptian Stock Exchange in 2020. A sample of study contains 102 companies listed in six sectors on the Egyptian Stock Exchange. The Pearson correlation coefficient and multiple linear regression were used to analyze the relationships between the variables. The results revealed a significant effect for the use of big data techniques and the effectiveness of internal control (effective control environment and the provision of effective communication channels between internal management and external parties). The study concluded that the integration of business intelligence tools and blockchain databases with big data is critical to support accounting practices and develop the roles of accountants and auditors. It is recommended that all big data techniques be expanded in order to maintain the security and dependability of data in businesses. Also, it is suggested to rely more on big data techniques in the decision-making process, particularly Hadoop and machine learning.KeywordsBig Data techniquesHadoop techniquesMachine learning techniquesInternal control

Data is accumulating from almost all aspects of our everyday lives that it becomes huge and multi- structured and has hidden useful information. The challenges with Big Data include capture, curation, storage, search, sharing, transfer, analysis, and visualization. Big Data provides materials for mining hidden patterns to support innovation mostly by data mining. The interaction research with Big Data support methods for innovation is rare at present. Knowledge discovered by data mining is novel and quantitative. However, it still lacks a uniform knowledge management model to support the innovation process effectively. The fact shows that since there emergence, big data techniques are changing very fast. In this paper, developing and maintaining the information security system to illustrate how those big data systems evolve and also describes some key design factors and challenges for future big data systems. Proposed design generic systems that can provide near real-time analytic services for many netease applications, such as spam detection, game log analysis and social community mining. No solutions can address all big data problems, especially when data size keeps increasing, more complex user requirements need to handle, the emergence of new hardware violates the old design and the old system becomes too complicated for maintenance. We face a series of technical challenges that have not been well addressed by both academic community and industry.

Recent critiques of both the uses of and discourse surrounding big data have raised important questions as to the extent to which big data and big data techniques should be embraced. However, while the context-dependence of data has been recognized, there remains a tendency among social theorists and other commentators to treat certain aspects of the big data phenomenon, including not only the data but also the methods and tools used to move from data as database to data that can be interpreted and assigned meaning, in a homogenizing way. In this paper, we seek to challenge this tendency, and to explore the ways in which explicit consideration of the plurality of big data might inform particular instances of its exploitation. We compare one currently popular big data-inspired innovation — learning analytics — with three other big data contexts — the physical sciences, business intelligence and public health. Through these comparisons, we highlight some dangers of learning analytics implemented without substantial theoretical, ethical and design effort. In so doing, we also highlight just how plural data, analytical approaches and intentions are, and suggest that each new big data context needs to be recognized in its own singularity.

Big Data is known as huge data set that is not processed in single system. To increase the processing speed and storing high volume data we prefer big data than traditional techniques. Big data helps in decision making for business prospective also. Big Data also refers a huge quantity of data sets with a large variety can be stored, managed and processed. It make secure correct information fast and provide benefit to the researchers and customers by taking the properties of volume, variety, veracity, velocity, value, visualization, volatility, vulnerability, validity and variability into consideration. With the help of traditional database system we cannot handle large data. To handle huge dataset, consider big data tools and techniques. In this review paper we first proposed the introduction and their characteristics and related issues faced by big data. It also provided the tools for big data which are help to managing data. This article is like a small seed of helping to know about the data storage and management of big data.

The objective of this book is to introduce the basic concepts of big data computing and then to describe the total solution of big data problems using HPCC, an open-source computing platform. The book comprises 15 chapters broken into three parts. The first part, Big Data Technologies, includes introductions to big data concepts and techniques; big data analytics; and visualization and learning techniques. The second part, LexisNexis Risk Solution to Big Data, focuses on specific technologies and techniques developed at LexisNexis to solve critical problems that use big data analytics. It covers the open source High Performance Computing Cluster (HPCC Systems) platform and its architecture, as well as parallel data languages ECL and KEL, developed to effectively solve big data problems. The third part, Big Data Applications, describes various data intensive applications solved on HPCC Systems. It includes applications such as cyber security, social network analytics including fraud, Ebola spread modeling using big data analytics, unsupervised learning, and image classification. The book is intended for a wide variety of people including researchers, scientists, programmers, engineers, designers, developers, educators, and students. This book can also be beneficial for business managers, entrepreneurs, and investors.

The age of Big data is here, and the effective managing of this data can determine whether an organization succeeds or falls behind its competitors.In more extreme cases it can determine how effectively a country can manage a pandemic, as we have seen with the Covid-19 pandemic.Dealing with Big data present many challenges but equally there are a lot of solutions to these challenges via the use of available technologies.Furthermore, Technology in regard to managing Big data is widely available and has evolved in the past few years and there are very few excuses to why industry practitioners should not be employing scalable Big data technologies.In this study the researcher has identified the different challenges that arise when dealing with Big data and how the different technologies available on the market have been designed to deal with different challenges that Big data present.The introduction of new hardware architectures, as well as the continual accumulation of data, provide new data management difficulties.Reasoning with respect to a predefined set of resources is no longer relevant (i.e., computing, storage and main memory).Instead, using scalable technologies and techniques, data processing algorithms and processes must be designed with unlimited resources in mind.The research enquires into the different technologies currently being used by industry practitioners in Namibia and the different challenges they encounter in regard to their respective approaches.Additionally, a short online survey was also conducted to investigate the knowledge of relevant stakeholders in terms of Big data technologies.Finally, this study urges industry practitioners to desert the use of outdated technologies and to encourage one another to employ scalable technologies by providing incentives.

With the unpredictable development of technology, a wide variety of data is produced in a very short time from countless sources. Industry 4.0 is a revolution in new technology for the digital world of digital factories and smart products. The ‘Big Data’ used by machines to communicate with each other using the internet of things is the building block of Industry 4.0. Big data is not just data to be stored or accessed. It is also data that is made sense and analyzed. In this chapter, the characteristics of big data, its applications, and its place in industry 4.0 are presented. Firstly, history and definitions of the big data are introduced in this chapter, then some applications are explained by using big data methods and techniques. Finally, opportunities and challenges are mentioned for the future aspects of using big data analytics.

Utilizing big data to govern decisions is becoming increasingly valuable and the thermal spray process is no exception. The thermal spray process is unique as a material process in its capability to employ a wide range of advanced materials technologies: metals, ceramics, cermets, and oxides among others. Like any process, the thermal spray technology is most effective when utilizing material feedstock which is specifically designed for thermal spray. This paper will discuss how big data techniques can be employed to design disruptive materials technology. The thermal spray process presents unique challenges to modelling and simulation work due to the inherent complexity of the process. However, these challenges offer the opportunity to develop materials tailored for specific thermal spray processes to yield improved coating performance. Furthermore, big data material informatics can significantly accelerate the discovery of new alloy solutions. More than 100 years of experimental research underpins the science employed, but modern computational tools and materials informatics principles enable new decision strategies to be utilized. The big data approach relies on calculations which predict the microstructure of millions of alloy compositions and utilizing proprietary data mining algorithms to identify unique materials spaces which would never be discovered experimentally.

With the daily huge growth in the number of confirmed COVID-19 cases, COVID-19 extremely threatens public health, countries’ economic, social life, and the international relations around the world. The accurate diagnosis based on a huge amount of data has become a serious issue that effect the disease control, especially in the widespread countries. To monitor COVID-19, big data analytics tools and Artificial Intelligence (AI) techniques play a significant role in many aspects. The integration between both technologies will help healthcare workers for early and accurately diagnose COVID-19 cases. In addition, the strategic planning for crisis management is supported by aggregation of big data to be use in the epidemiologic directions. Moreover, AI and big data driven tools presents visualization for COVID-19 outbreak information that help in detecting risk allocation and regional transmissions. In this chapter, a review of recent works related to COVID-19 containment using AI and big data techniques is introduced, showing their main findings and limitations to make it easy for researchers to investigate new techniques that will help in COVID-19 pandemic.

This study examines, how big data technique can be applied in forensic accounting to improve fraud and cybercrime detection in Nigeria with the view to suggest empirical and technological-based contemporary techniques in order to effectively detect and prevent frauds and corruption, as well as to proffer policy recommendations for confronting the scourge of fraud and corruption. Being a survey research design, the study adopt quantitative data collection and analyses using structured questionnaires distributed to a sample purposively selected forensic auditors from EFCC office Kano Command. Data obtained from the survey was analyzed using descriptive statistics, regression analysis and other relevant econometrics. The findings, revealed that Big Data Technologies has a positive and significant impact in conduct of forensic auditing in the Nigerian public sector. Similarly, it has positive and significant impact on Forensic Accounting and Prevention and Detection of Fraud and Cybercrimes in the Nigerian Public Sector. The study has social, economic and technological implication as follows: Social benefit include bringing awareness, to both public and private organizations of the impact of big data technology in forensic accounting. Economically will contribute to realizing the potentials of science and technology to meet the most pressing challenges of sustainable economic growth and development through early detection of fraud and corrupt practices by forensic accountant in EFCC. Moreover, technological benefits comprises bridging the gap between traditional methods of detecting fraud/cybercrimes by proposing improved ways of its detection using contemporary technology (i.e. Big data). It is recommended that Government shall consider these findings of empirical based knowledge of causes, nature and the extent of fraud/cybercrimes and its implications on the national growth during its subsequent policies formulations.

Big data is ever playing an important role in the industry as well as many other organizations. With the passage of time, the volume of data is increasing. This increase will create huge bulk of data which needs proper tools and techniques to handle its management and organization. Different techniques and tools are being used to properly handle the management of data. A detailed report of these techniques and tools is needed which will help researchers to easily identify a tool for their data and take help to easily manage the data, organize the data, and extract meaningful information from it. The proposed study is an endeavour toward summarizing and identifying the tools and techniques for big data used in Industrial Internet of Things. This report will certainly help researchers and practitioners to easily use the tools and techniques for their need in an effective way.