Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Exploring Cloud Adoption Possibilities for the Manufacturing Sector: A Role of Third-Party Service Providers

As the manufacturing sector strides towards digitalization under the influence of Industry 4.0, cloud services have emerged as the new norm, driving change and innovation in this rapidly transforming landscape. This study investigates the possibilities of cloud adoption in the manufacturing sector by developing a conceptual model to identify suitable cloud-based solutions and explores the role of third-party service providers in aiding manufacturers throughout their cloud adoption journey. The research methods consist of a comprehensive literature review of the manufacturing industry, digital transformation, cloud computing, etc., followed by qualitative analyses of industrial benchmarks case studies and an investigation into an application of the developed model to a hypothetical food manufacturing company as an example. This study indicates that cloud adoption can yield substantial benefits in the manufacturing sector, including operational efficiency, cost reduction, and innovation, etc. The study concludes that the developed conceptual model provides a practical framework to identify the most suitable cloud-based solutions during the cloud adoption process in the manufacturing context. In addition, third-party service providers like Capgemini are capable of not only filling the technical gaps but also consulting strategic directions and innovations for their client organizations, hence playing a vital role in driving the industrial digital transformation process. With an extensive mapping of their capabilities, a set of recommendations intended to assist Capgemini in enhancing capabilities and improving competitive performance in the market has been offered

Machine learning based anomaly detection for industry 4.0 systems.

223 p.This thesis studies anomaly detection in industrial systems using technologies from the Fourth Industrial Revolution (4IR), such as the Internet of Things, Artificial Intelligence, 3D Printing, and Augmented Reality. The goal is to provide tools that can be used in real-world scenarios to detect system anomalies, intending to improve production and maintenance processes. The thesis investigates the applicability and implementation of 4IR technology architectures, AI-driven machine learning systems, and advanced visualization tools to support decision-making based on the detection of anomalies. The work covers a range of topics, including the conception of a 4IR system based on a generic architecture, the design of a data acquisition system for analysis and modelling, the creation of ensemble supervised and semi-supervised models for anomaly detection, the detection of anomalies through frequency analysis, and the visualization of associated data using Visual Analytics. The results show that the proposed methodology for integrating anomaly detection systems in new or existing industries is valid and that combining 4IR architectures, ensemble machine learning models, and Visual Analytics tools significantly enhances theanomaly detection processes for industrial systems. Furthermore, the thesis presents a guiding framework for data engineers and end-users

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Integrating digital analytics in strategic marketing decision making : An absorptive capacity approach

Over the last two decades, digital analytics has evolved from simply monitoring clicks to ever more complex campaign analyses and evaluations of marketing communication performance. However, the rapid rate of technological development has overshadowed the strategic importance of digital analytics for marketing practitioners and researchers alike. This has led to a situation, where the technical execution of digital analytics has become known relatively well but its practical benefits and purposes have been left without much attention. This research aims to breach this research gap by answering the question: how can companies integrate digital analytics in their strategic marketing decision making. This is a qualitative single case study, the focus of which is the company Unilever. The empirical data comprises eight semi-structured interviews as well as two digital analytics reports supplied by the case company. Literature on the absorptive capacity theory and strategic marketing decision making was used as a basis for the analysis of the data. The research findings suggest that it is possible to use digital analytics to generate strategically relevant knowledge on actual customer behaviour, wants and interests. The findings also indicate that digital analytics as a concept is broad and consists of several analytics subclasses, each with their distinctive characteristics and purposes. Furthermore, the findings highlight the challenges and obstacles that companies face in their attempts to integrate digital analytics in their strategic decision making processes. The main theoretical contribution of this study is to illuminate the strategic relevance of digital analytics. Digital analytics should not be viewed simply as a tool to monitor on-going marketing campaigns; in the right hands, it can generate valuable insights to support strategic marketing decision making. Another significant contribution is the conceptualisation of digital marketing knowledge. This research presents strong evidence to support the statement that the knowledge generated through digital analytics is fundamentally different from that of conventional consumer and market research. Companies can therefore use digital analytics to generate new insights on their customers, competitors and markets. In order to unleash this potential, companies must strive towards a flexible organisational structure and processes, offer purposeful employee training and support, develop a functional data management platform and be strategically committed towards developing their digital analytics capacity. Finally, this study provides research avenues for future research on the utility of digital analytics as a decision support tool. Further longitudinal analysis or a multiple case study on this research topic would work toward confirming, refuting or supplementing the results of this study

Methodologies for the assessment of industrial and energy assets, based on data analysis and BI

In July 2020, post pandemic onset, Europe launched the Next Generation EU (NGEU) program. The amount of resources deployed to revitalize Europe has reached 750 billion. The NGEU initiative directs significant resources to Italy. These funds can enable our country to boost investment and increase employment. The missions of Italian Recovery and Resilience Plan (PNRR) include digitization, innovation and sustainable mobility (rail network investments, etc.). In this context, this doctorate thesis discusses the importance of infrastructure for society with a special focus on energy, railway and motorway infrastructure. The central theme of sustainability, defined by the World Commission on Environment and Development (WCDE) as ''development that meets the needs of the present generation without compromising the ability of future generations to meet their needs’’, is also highlighted. Through their activities and relationships, organizations contribute positively or negatively to the goal of sustainable development. Sustainability becomes an integrated part of corporate culture. First research in this thesis describes how Artificial Intelligence techniques can play a supporting role for both maintenance operators in tunnel monitoring and those responsible for safety in operation. Relevant information can be extracted from large volumes of data from sensor equipment in an efficient, fast, dynamic and adaptive manner and made immediately usable by those operating machinery and services to support rapid decisions. Performing sensor-based analysis in motorway tunnels represents a major technological breakthrough that would simplify tunnel management activities and thus the detection of possible deterioration, while keeping risk within tolerance limits. The idea involves the creation of an algorithm for detecting faults, acquiring real-time data from tunnel subsystem sensors and using it to help identify the tunnel's state of service. Artificial intelligence models were trained over a sixmonth period with a granularity of one-hour time series measured on a road tunnel forming part of the Italian motorway systems. The verification was carried out with 3 reference to a series of failures recorded by the sensors. The second research argument is relates to the transfer capacities of high-voltage overhead lines (HVOHL), which are often limited by the critical temperature of the power line, which depends on the magnitude of the current transferred and the environmental conditions, i.e. ambient temperature, wind, etc. In order to use existing power lines more effectively (with a view to progressive decarbonization) and more safely with respect to critical power line temperatures, this work proposes a Dynamic Thermal Rating (DTR) approach using IoT sensors installed on a number of HV OHL located in different geographical locations in Italy. The objective is to estimate the temperature and ampacity of the OHL conductor, using a data-driven thermomechanical model with a bayesian probabilistic approach, in order to improve the confidence interval of the results. This work shows that it might be possible to estimate a spatio-temporal temperature distribution for each OHL and an increase in the threshold values of the effective current to optimize the OHL ampacity. The proposed model was validated using the Monte Carlo method. Finally, in this thesis is presented study on KPIs as indispensable allies of top management in the asset control phase. They are often overwhelmed by the availability of a huge amount of Key Performance Indicators (KPIs). Most managers struggle In understanding and identifying the few vital management metrics and instead collect and report a vast amount of everything that is easy to measure. As a result, they end up drowning in data, thirsty for information. This condition does not allow good systems management. The aim of this research is help the Asset Management System (AMS) of a railway infrastructure manager using business intelligence (BI) to equip itself with a KPI management system in line with the AM presented by the normative ISO 55000 - 55001 - 55002 and UIC (International Union of Railways) guideline, for the specific case of a railway infrastructure. This work starts from the study of these regulations, continues with the exploration, definition and use of KPIs. Subsequently KPIs of a generic infrastructure are identified and analyzed, 4 especially for the specific case of a railway infrastructure manager. These KPIs are fitted in the internal elements of the AM frameworks (ISO-UIC) for systematization. Moreover, an analysis of the KPIs now used in the company is made, compared with the KPIs that an infrastructure manager should have. Starting from here a gap analysis is done for the optimization of AMS