Big Data Major Security Issues: Challenges and Defense Strategies

Big data has unlocked the door to significant advances in a wide range of scientific fields, and it has emerged as a highly attractive subject both in the world of academia and in business as a result. It has also made significant contributions to innovation, productivity gains, and competitiveness enhancements. However, there are many difficulties associated with data collecting, storage, usage, analysis, privacy, and trust that must be addressed at this time. In addition, inaccurate or misleading big data may lead to an incorrect or invalid interpretation of findings, which can negatively impact the consumers\u27 experiences. This article examines the challenges related to implementing big data security and some important solutions for addressing these problems. So, a total of 12 papers have been extracted and analyzed to add to the corpus of literature by concentrating on several critical issues in the big data analytics sector as well as shedding light on how these challenges influence many domains such as healthcare, education, and business intelligence, among others. While studies have proven that big data poses issues, their approaches to overcoming these obstacles vary. The most frequently mentioned challenges were data, process, privacy, and management. To address these issues, this paper included previously discovered solutions

An Overview of the Rising Challenges in Implementing Industry 4.0

Industry 4.0 is the fourth industrial revolution that was first introduced in Germany which then becomes a trend of future manufacturing industries. The Industry 4.0 also referred as the umbrella concept for new industrial paradigm which consists of a number of future industry characteristics, were related to cyber-physical systems (CPS), internet of things (IoT), internet of services (IoS), robotics, big data, cloud manufacturing and augmented reality. By adopting these technologies as the key development in more intelligent manufacturing processes including devices, machines, modules, and products, the process of information exchange, action and control will stimulate each other, subsequently to an intelligent manufacturing environment. However, in order to fully utilize the advantages of industry 4.0, there are some challenges that need to be overcome. This paper reviews the challenges in implementing Industry 4.0. The literatures found in this paper mainly from Google Scholar, Science Direct and Emerald. In short, the challenges can be imparted into seven major categories. There are data management and Integration, knowledge-driven, process, security, capital, workforce, and education

Beyond Pareto Analysis: A Decision Support Model for the Prioritization of Deviations with Natural Language Processing

In the manufacturing domain, the systematic problem-solving (SPS) process is essential to eliminate the root causes of deviations from expected performance. The major goal of SPS is to prevent the recurrence of known deviations. However, due to time and resource limitations, the deviations that occur on the shop floor should be prioritized before applying SPS. Therefore, a method to support the decision-making process for prioritization of deviations is required. Traditional methods, such as the Pareto analysis, are widely accepted and applied for easy use. But their performance is no more sufficient for the production environment with large fluctuations nowadays. Therefore, this paper proposes a decision support model - the error score - to prioritize deviations on the shop floor. The error score is calculated based on the process data as well as textual data found in the deviation documentation. As the quality of textual data in the deviation documentation has great effects on the performance of the model, Natural Language Processing (NLP) methods are developed to pre-process the unstructured text. To validate the model, it is applied to a real-world use case in the automotive industry to demonstrate and evaluate the performance. The study shows that the proposed model can effectively support the decision-making process on the shop floor and is superior to traditional methods

Upgrading legacy equipment to industry 4.0 through a cyber-physical interface

With the recent developments of Industry 4.0 technologies, maintenance can be improved significantly by making it “smart”, proactive and even self-aware. This paper introduces a new cutting-edge interfacing technology that enables smart active remote maintenance right on the machine in real-time while allowing integration of smart automated decision making and Industrial Internet of Things to upgrade existing legacy equipment through latest Industry 4.0 technology. This interfacing technology enables remote sensing and actuation access to legacy equipment for smart maintenance by entirely non-intrusive means, i.e. the original equipment does not have to be modified. The design was implemented in a real-world manufacturing environment

Annotation method of risk data in a certain field based on pattern matching

With the development of information technology and the increasing complexity of industrial technology, there is an urgent need for a certain field to use big data and artificial intelligence to improve the management and decision-making level. In order to classify the field’s risk text data through intelligent algorithms, analysing the risk distribution and the major problems, this paper researches on the annotation methods of training data in this field. The proposed data annotation method is based on pattern matching, addressing the special problems of risk data annotation in this field (such as strong professionalism, small data volume, high accuracy requirement and timeliness requirements). A new matching pattern is generated through the steps of text segmentation, keyword extraction, pattern preliminary generation, pattern relation tree construction, pattern optimization, pattern generalization, pattern verification, classification and annotation, and final classification and annotation are performed after pattern matching. Performance tests in terms of accuracy, recall rate, and annotation time have shown that the overall performance of the proposed method outperforms that of traditional item-by-item manual annotation, and semi-automatic annotation methods through machine learning. The method described in this paper has strong application value for risk data annotation in this field, and also has certain reference significance for high-density, high-accuracy and high-timeliness data annotation in other fields

Review of lean manufacturing with IR4.0 in automotive industry

This paper aims to study the possibility of integration between Lean Manufacturing (LM) and Industrial Revolutions 4.0 (IR4.0). LM is generally known and acknowledged as a feasible system in the industrial sector. However, a new paradigm of IR4.0 has influenced manufacturers to look further into how LM could be implemented and adopted. It drives the integration of an intelligent factory to control machines, humans, products, and cloud solutions along the value chain. Manufacturers, especially in the automotive industry such as Toyota Company, have been using lean concepts and methods for so many years to eliminate wastes, reduce operational costs, and improve production performance. By integrating LM with IR4.0, the manufacturers could enhance productivity and quality by using the implementation chain. Besides, it enables self-management operational processes that could ensure the customer's quality of production

Technology enablers for the implementation of Industry 4.0 to traditional manufacturing sectors: A review

The traditional manufacturing sectors (footwear, textiles and clothing, furniture and toys, among others) are based on small and medium enterprises with limited capacity on investing in modern production technologies. Although these sectors rely heavily on product customization and short manufacturing cycles, they are still not able to take full advantage of the fourth industrial revolution. Industry 4.0 surfaced to address the current challenges of shorter product life-cycles, highly customized products and stiff global competition. The new manufacturing paradigm supports the development of modular factory structures within a computerized Internet of Things environment. With Industry 4.0, rigid planning and production processes can be revolutionized. However, the computerization of manufacturing has a high degree of complexity and its implementation tends to be expensive, which goes against the reality of SMEs that power the traditional sectors. This paper reviews the main scientific-technological advances that have been developed in recent years in traditional sectors with the aim of facilitating the transition to the new industry standard.This research was supported by the Spanish Research Agency (AEI) and the European Regional Development Fund (ERDF) under the project CloudDriver4Industry TIN2017-89266-R

Artificial intelligence and visual analytics in geographical space and cyberspace: Research opportunities and challenges

In recent decades, we have witnessed great advances on the Internet of Things, mobile devices, sensor-based systems, and resulting big data infrastructures, which have gradually, yet fundamentally influenced the way people interact with and in the digital and physical world. Many human activities now not only operate in geographical (physical) space but also in cyberspace. Such changes have triggered a paradigm shift in geographic information science (GIScience), as cyberspace brings new perspectives for the roles played by spatial and temporal dimensions, e.g., the dilemma of placelessness and possible timelessness. As a discipline at the brink of even bigger changes made possible by machine learning and artificial intelligence, this paper highlights the challenges and opportunities associated with geographical space in relation to cyberspace, with a particular focus on data analytics and visualization, including extended AI capabilities and virtual reality representations. Consequently, we encourage the creation of synergies between the processing and analysis of geographical and cyber data to improve sustainability and solve complex problems with geospatial applications and other digital advancements in urban and environmental sciences

Is Malaysia ready for Industry 4.0? Issues and Challenges in Manufacturing Industry

Despite as a strong manufacturing economist in ASEAN, manufacturers in Malaysia are the beginners who are lack of proper understanding of the concepts and practices of Industry 4.0. The purpose of this paper is to identify the issues and challenges of Industry 4.0 from industry-based companies' aspect by conducting a literature review. This paper also highlighted the comparison between the potential challenges stated in the Malaysia National Policy on Industry 4.0 with the challenges proposed by previous studies of other countries. This paper is a literature review on previous studies regards to challenges or issues on implementation of Industry 4.0 from 2015 to 2019. Total 11 challenges in the processes of implementation Industry 4.0 into manufacturing companies are reviewed. Compared to previous studies, Malaysia National Policy on Industry 4.0 overlooked 3 challenges on Industry 4.0. This is the first review paper to compare the existing challenges in Industry 4.0 with the potential challenges stated in the Malaysia National Policy on Industry 4.0. &nbsp