''New technologies are advancing at an unprecedentedly accelerating pace over the years. The distance humanity has covered in 2200 years, from the Antikythera mechanism of ancient Greek world, the oldest known analogue computer, to the 4-bit first microprocessor in 1971, is not even comparable to the advancement of technology in the last 50 years. This dazzling journey of technological development has impacted all aspects of modern life, including industry.Earthquake engineering is one of the disciplines that has embraced new technologies. Earthquake engineers, accustomed to dealing with highly nonlinear and dynamic problems that require complex mathematical and often iterative approaches, are called nowadays to summon dexterity on advanced coding, and masteries on statistics and handling of large amount of data. Artificial Intelligence, Sensing Technologies of all sorts, and Big Data Analytics emerge as essential tools for reducing uncertainty, facilitating engineering process and enhancing knowledge. This Special Issue is a manifestation of the fact that the new technologies can be useful for the most challenging problems of earthquake engineering, opening new prospects in the field.''
As every new generation of civil aircraft creates more on-wing data and fleets gradually become more connected with the ground, an increased number of opportunities can be identified for more effective Maintenance, Repair and Overhaul (MRO) operations. Data are becoming a valuable asset for aircraft operators. Sensors measure and record thousands of parameters in increased sampling rates. However, data do not serve any purpose per se. It is the analysis that unleashes their value. Data analytics methods can be simple, making use of visualizations, or more complex, with the use of sophisticated statistics and Artificial Intelligence algorithms. Every problem needs to be approached with the most suitable and less complex method. In MRO operations, two major categories of on-wing data analytics problems can be identified. The first one requires the identification of patterns, which enable the classification and optimization of different maintenance and overhaul processes. The second category of problems requires the identification of rare events, such as the unexpected failure of parts. This cluster of problems relies on the detection of meaningful outliers in large data sets. Different Machine Learning methods can be suggested here, such as Isolation Forest and Logistic Regression. In general, the use of data analytics for maintenance or failure prediction is a scientific field with a great potentiality. Due to its complex nature, the opportunities for aviation Data Analytics in MRO operations are numerous. As MRO services focus increasingly in long term contracts, maintenance organizations with the right forecasting methods will have an advantage. Data accessibility and data quality are two key-factors. At the same time, numerous technical developments related to data transfer and data processing can be promising for the future.
De missie van mijn vakgebied is dat data analytics wordt toegepast om organisaties beter te maken. Ons onderzoek richt zich op de verbanden tussen het effectiever maken van organisaties, het verbeteren van individueel welzijn en maatschappelijke waarde. Onze faculteit wil duurzaam waarde realiseren voor organisaties, individu en maatschappij en de drie uitkomsten moeten in balans zijn. Daar staan we voor.
MULTIFILE
The scientific publishing industry is rapidly transitioning towards information analytics. This shift is disproportionately benefiting large companies. These can afford to deploy digital technologies like knowledge graphs that can index their contents and create advanced search engines. Small and medium publishing enterprises, instead, often lack the resources to fully embrace such digital transformations. This divide is acutely felt in the arts, humanities and social sciences. Scholars from these disciplines are largely unable to benefit from modern scientific search engines, because their publishing ecosystem is made of many specialized businesses which cannot, individually, develop comparable services. We propose to start bridging this gap by democratizing access to knowledge graphs – the technology underpinning modern scientific search engines – for small and medium publishers in the arts, humanities and social sciences. Their contents, largely made of books, already contain rich, structured information – such as references and indexes – which can be automatically mined and interlinked. We plan to develop a framework for extracting structured information and create knowledge graphs from it. We will as much as possible consolidate existing proven technologies into a single codebase, instead of reinventing the wheel. Our consortium is a collaboration of researchers in scientific information mining, Odoma, an AI consulting company, and the publisher Brill, sharing its data and expertise. Brill will be able to immediately put to use the project results to improve its internal processes and services. Furthermore, our results will be published in open source with a commercial-friendly license, in order to foster the adoption and future development of the framework by other publishers. Ultimately, our proposal is an example of industry innovation where, instead of scaling-up, we scale wide by creating a common resource which many small players can then use and expand upon.
Digitalisation has enabled businesses to access and utilise vast amounts of data. Business data analytics allows companies to employ the most recent and relevant data to comprehend situations and enhance decision-making. While the value of data itself is limited, substantial value can be directly or indirectly uncovered from data. This process is referred to as data monetisation. The most successful stories of data monetisation often originate from large corporations, as they have adequate resources to monetise their data. Notably, many such cases arise from prominent Big Tech companies in North America. In contrast, small and medium-sized enterprises (SMEs) have lagged behind in utilising their digital data assets effectively. They are frequently constrained by limited resources to build up capabilities and fully exploit their data. This places them at a strategic disadvantage, particularly as digitalisation is progressively reshaping markets and competitive relationships. Furthermore, the use of digital technologies and data are important in addressing societal challenges such as energy conservation, circularity, and the ageing of the population. This lag has been highlighted by SMEs we have engaged with, where managing directors have indicated their desire to operate based on data, but their companies lack the know-how and are unsure of ‘where to start’. Together with eight SMEs and other partners, we have defined a research project to gain insight into the potential and obstacles of data monetisation in SMEs. More specifically, we will explore how SMEs can transform data into strategic assets and create value. We attempt to demonstrate the journey of data monetisation and illustrate different possibilities to create value from data in SMEs. We will take a holistic approach to examine different aspects of data monetisation and their associations. The outcomes of this project are both practical and academic, such as an SME handbook, academic papers, and case studies.
The value of data in general has become eminent in recent times. Autonomous vehicles and Connected Intelligent Transport Systems (C-ITS), in particular, are rapidly emerging fields that rely a lot on “big data”. Data acquisition has been an important part of automotive research and development for years even before the advent of Internet of Things (IoT). Most datalogging is done using specialized hardware that stores data in proprietary formats on traditional hard drives in PCs or dedicated managed servers. The use of Artificial Intelligence (AI) throughout the world and specifically in the automotive sector is largely reliant on the data for the development of new and reliable technologies. With the advent of IoT technologies, the reliability of data capture could be enhanced and can improve ease of real-time analytics for analysis/development of C-ITS services and Autonomous systems using vehicle data. Data acquisition for C-ITS applications requires putting together several different domains ranging from hardware, software, communication systems, cloud storage/processing, data analytics, legal and privacy aspects. This requires expertise from different domains that small and medium scale businesses usually lack. This project aims at investigating requirements that have to be met in order to collect data from vehicles. Furthermore, this project also aims at laying foundations required for the development of a unified guidelines required to collect data from vehicles. With these guidelines, businesses that intend to use vehicle data for their applications are not only guided on the technical aspects of data collection but also equally understand how data from vehicles could be harvested in a secure, efficient and responsible manner.
