Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Worldwide Deployment of Predictive Asset Management at Air Liquide

TutorialAir Liquide launched an international program to monitor and assess equipment asset health, resulting in a positive step-change in availability and reliability worldwide. Using predictive analytics, potential asset failures may be identified and appropriate intervention planned. Intervention prior to failure averts a possible reliability incident, adverse customer impact, and costly emergency maintenance activitie

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

A resiliency framework for an enterprise cloud

This paper presents a systematic approach to develop a resilient software system which can be developed as emerging services and analytics for resiliency. While using the resiliency as a good example for enterprise cloud security, all resilient characteristics should be blended together to produce greater impacts. A framework, Cloud Computing Adoption Framework (CCAF), is presented in details. CCAF has four major types of emerging services and each one has been explained in details with regard to the individual function and how each one can be integrated. CCAF is an architectural framework that blends software resilience, service components and guidelines together and provides real case studies to produce greater impacts to the organizations adopting Cloud Computing and security. CCAF provides business alignments and provides agility, efficiency and integration for business competitive edge. In order to validate user requirements and system designs, a large scale survey has been conducted with detailed analysis provided for each major question. We present our discussion and conclude that the use of CCAF framework can illustrate software resilience and security improvement for enterprise security. CCAF framework itself is validated as an emerging service for Enterprise Cloud Computing with analytics showing survey analysi

A Citizen Observatory Approach for Developing a Disease Outbreak Early Warning System

In health matters, early warning systems are timely surveillance systems that collect information on epidemic-prone diseases to trigger prompt public health interventions. However, these systems rarely apply statistical methods to detect changes in trends or sentinel events that would require intervention. Often, they rely on an in-depth review done by epidemiologists of the data coming in, which is rarely done systematically. This research introduced the use of ICT for collecting and analyzing citizen observations on disease trends and outbreaks. A citizen observatory ICT tool, which utilizes mobile and web features was developed. Data was collected on symptoms observed from diseases in four locations within Nairobi city. The system made use of mathematical models and outlier detection techniques to detect observations that deviated from the expected pattern in the dataset. New clusters were considered as outliers and the system flagged them as potential outbreaks. We clustered data using a K-Means algorithm and the Euclidean distance of each object from its corresponding cluster centre was obtained. From the results, the developed prototype was able to detect an outbreak of Flu and URTI diseases for the period of study. The proposed tool can therefore enhance the management of risks associated with disease outbreaks.  Keywords: Early Warning Systems, citizen observatory, health surveillance, Outlier Detection, Modeling disease outbreak

Int J Ind Ergon

In occupational safety and health, big data and analytics show promise for the prediction and prevention of workplace injuries. Advances in computing power and analytical methods have allowed companies to reveal insights from the "big" data that previously would have gone undetected. Despite the promise, occupational safety has lagged behind other industries, such as supply chain management and healthcare, in terms of exploiting the potential of analytics and much of the data collected by organizations goes unanalyzed. The purpose of the present paper is to argue for the broader application of establishment-level safety analytics. This is accomplished by defining the terms, describing previous research, outlining the necessary components required, and describing knowledge gaps and future directions. The knowledge gaps and future directions for research in establishment-level analytics are categorized into readiness for analytics, analytics methods, technology integration, data culture, and impact of analytics.CC999999/ImCDC/Intramural CDC HHSUnited States

Service Now: CMDB Research

The MAPFRE Capstone team has been tasked with reviewing and recommending roadmap on the existing CMDB configuration. Paper discusses the team’s overall research on ServiceNow CMDB, Client’s deliverables and introduction to the latest technological innovations. Based on given objectives and team’s analysis we have recommended key solutions for the client to better understand the IT environment areas of business service impact, asset management, compliance, and configuration management. In addition, our research has covered all the majority of the technical and functional areas to provide greater visibility and insight into existing CMDB and IT environment

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures comprise of many interconnected cyber and physical assets, and as such are large scale cyber-physical systems. Hence, the conventional approach of securing these infrastructures by addressing cyber security and physical security separately is no longer effective. Rather more integrated approaches that address the security of cyber and physical assets at the same time are required. This book presents integrated (i.e. cyber and physical) security approaches and technologies for the critical infrastructures that underpin our societies. Specifically, it introduces advanced techniques for threat detection, risk assessment and security information sharing, based on leading edge technologies like machine learning, security knowledge modelling, IoT security and distributed ledger infrastructures. Likewise, it presets how established security technologies like Security Information and Event Management (SIEM), pen-testing, vulnerability assessment and security data analytics can be used in the context of integrated Critical Infrastructure Protection. The novel methods and techniques of the book are exemplified in case studies involving critical infrastructures in four industrial sectors, namely finance, healthcare, energy and communications. The peculiarities of critical infrastructure protection in each one of these sectors is discussed and addressed based on sector-specific solutions. The advent of the fourth industrial revolution (Industry 4.0) is expected to increase the cyber-physical nature of critical infrastructures as well as their interconnection in the scope of sectorial and cross-sector value chains. Therefore, the demand for solutions that foster the interplay between cyber and physical security, and enable Cyber-Physical Threat Intelligence is likely to explode. In this book, we have shed light on the structure of such integrated security systems, as well as on the technologies that will underpin their operation. We hope that Security and Critical Infrastructure Protection stakeholders will find the book useful when planning their future security strategies

An Integrated Cybersecurity Risk Management (I-CSRM) Framework for Critical Infrastructure Protection

Risk management plays a vital role in tackling cyber threats within the Cyber-Physical System (CPS) for overall system resilience. It enables identifying critical assets, vulnerabilities, and threats and determining suitable proactive control measures to tackle the risks. However, due to the increased complexity of the CPS, cyber-attacks nowadays are more sophisticated and less predictable, which makes risk management task more challenging. This research aims for an effective Cyber Security Risk Management (CSRM) practice using assets criticality, predication of risk types and evaluating the effectiveness of existing controls. We follow a number of techniques for the proposed unified approach including fuzzy set theory for the asset criticality, machine learning classifiers for the risk predication and Comprehensive Assessment Model (CAM) for evaluating the effectiveness of the existing controls. The proposed approach considers relevant CSRM concepts such as threat actor attack pattern, Tactic, Technique and Procedure (TTP), controls and assets and maps these concepts with the VERIS community dataset (VCDB) features for the purpose of risk predication. Also, the tool serves as an additional component of the proposed framework that enables asset criticality, risk and control effectiveness calculation for a continuous risk assessment. Lastly, the thesis employs a case study to validate the proposed i-CSRM framework and i-CSRMT in terms of applicability. Stakeholder feedback is collected and evaluated using critical criteria such as ease of use, relevance, and usability. The analysis results illustrate the validity and acceptability of both the framework and tool for an effective risk management practice within a real-world environment. The experimental results reveal that using the fuzzy set theory in assessing assets' criticality, supports stakeholder for an effective risk management practice. Furthermore, the results have demonstrated the machine learning classifiers’ have shown exemplary performance in predicting different risk types including denial of service, cyber espionage, and Crimeware. An accurate prediction can help organisations model uncertainty with machine learning classifiers, detect frequent cyber-attacks, affected assets, risk types, and employ the necessary corrective actions for its mitigations. Lastly, to evaluate the effectiveness of the existing controls, the CAM approach is used, and the result shows that some controls such as network intrusion, authentication, and anti-virus show high efficacy in controlling or reducing risks. Evaluating control effectiveness helps organisations to know how effective the controls are in reducing or preventing any form of risk before an attack occurs. Also, organisations can implement new controls earlier. The main advantage of using the CAM approach is that the parameters used are objective, consistent and applicable to CPS