Automatic definition of engineer archetypes: A text mining approach

With the rapid and continuous advancements in technology, as well as the constantly evolving competences required in the field of engineering, there is a critical need for the harmonization and unification of engineering professional figures or archetypes. The current limitations in tymely defining and updating engineers' archetypes are attributed to the absence of a structured and automated approach for processing educational and occupational data sources that evolve over time. This study aims to enhance the definition of professional figures in engineering by automating archetype definitions through text mining and adopting a more objective and structured methodology based on topic modeling. This will expand the use of archetypes as a common language, bridging the gap between educational and occupational frameworks by providing a unified and up-to-date engineering professional figure tailored to a specific period, specialization type, and level. We validate the automatically defined industrial engineer archetype against our previously manually defined profile

A User-centric Taxonomy for Conversational Generative Language Models

Conversational generative language models (GLMs) like ChatGPT are being rapidly adopted. Previous research on non-conversational GLMs showed that formulating prompts is critical for receiving good outputs. However, it is unclear how conversational GLMs are used when solving complex problems that require multi-step interactions. This paper addresses this research gap based on findings from a large participant event we conducted, where ChatGPT was iteratively and in a multi-step manner used while solving a complex problem. We derived a taxonomy of prompting behavior employed for solving complex problems as well as archetypes. While the taxonomy provides common knowledge on GLMs usage based on analyzed input-prompts, the different archetypes facilitate the classification of operators according to their usage. With both we provide exploratory knowledge and a foundation for design science research endeavors, which can be referred to, enabling further research and development of prompt engineering, prompting tactics, and prompting strategies on common ground

Computational Models (of Narrative) for Literary Studies

In the last decades a growing body of literature in Artificial Intelligence (AI) and Cognitive Science (CS) has approached the problem of narrative understanding by means of computational systems. Narrative, in fact, is an ubiquitous element in our everyday activity and the ability to generate and understand stories, and their structures, is a crucial cue of our intelligence. However, despite the fact that - from an historical standpoint - narrative (and narrative structures) have been an important topic of investigation in both these areas, a more comprehensive approach coupling them with narratology, digital humanities and literary studies was still lacking. With the aim of covering this empty space, in the last years, a multidisciplinary effort has been made in order to create an international meeting open to computer scientist, psychologists, digital humanists, linguists, narratologists etc.. This event has been named CMN (for Computational Models of Narrative) and was launched in the 2009 by the MIT scholars Mark A. Finlayson and Patrick H. Winston1

Exploring Pharmaceutical Mass Customization

The core purpose of therapeutic pharmaceutical products is to induce responses to various diseases in patients and thereby bring societal value; however, unmet medical needs currently prevail. Conventional treatment of these products predominantly embraces a one-size-fits-all design and is manufactured in a mass-production context. A mass-production context is driven by economies of scale, however, a one-size-fits-all product design challenges the satisfaction of individual patient needs. Pharmaceutical product customization thus aims to satisfy individuals’ treatment needs and thereby improve their therapeutic outcome; however, this implies a high product variety and low-volume production environment which challenges the cost-effective production with current mass-production platforms.To address this challenge of achieving the cost-effective production of customized pharmaceutical products, this thesis explores a unified approach to cost-effective design, manufacturing and supply of customized pharmaceutical products. For this purpose, the mass customization principles of product modularization, process flexibility and postponement are adopted and adapted in a pharmaceutical production context.This thesis proposes methodologies to design and model customized pharmaceutical products and production systems in a unified manner. Furthermore, customized product designs are proposed using product modularization as a design strategy and reconfigured pharmaceutical supply chain (SC) archetypes using postponement as a strategy for the cost-effective design, manufacturing and supply. The findings suggest that an increased degree of modularization in the pharmaceutical product increases the patient benefit and thus improves therapeutic patient outcomes. In addition, current mass production platforms do not display the process flexibility required for the cost-effective production of customized pharmaceutical products. Moreover, with an increased degree of postponement, opportunities for reduced production costs in the SC emerge. Finally, the cost-effective customization of pharmaceutical products requires an integrated approach of product modularization and postponement. While modeling the production system, this thesis, however, considers an SC from the manufacturer to the pharmacy and patient assessing contemporary cost-effectiveness. Future research directions should investigate societal consequences from a wider, spatial and temporal, health care system perspective

A Two-Level Identity Model To Support Interoperability of Identity Information in Electronic Health Record Systems.

The sharing and retrieval of health information for an electronic health record (EHR) across distributed systems involves a range of identified entities that are possible subjects of documentation (e.g., specimen, clinical analyser). Contemporary EHR specifications limit the types of entities that can be the subject of a record to health professionals and patients, thus limiting the use of two level models in healthcare information systems that contribute information to the EHR. The literature describes several information modelling approaches for EHRs, including so called “two level models”. These models differ in the amount of structure imposed on the information to be recorded, but they generally require the health documentation process for the EHR to focus exclusively on the patient as the subject of care and this definition is often a fixed one. In this thesis, the author introduces a new identity modelling approach to create a generalised reference model for sharing archetype-constrained identity information between diverse identity domains, models and services, while permitting reuse of published standard-based archetypes. The author evaluates its use for expressing the major types of existing demographic reference models in an extensible way, and show its application for standards-compliant two-level modelling alongside heterogeneous demographics models. This thesis demonstrates how the two-level modelling approach that is used for EHRs could be adapted and reapplied to provide a highly-flexible and expressive means for representing subjects of information in allied health settings that support the healthcare process, such as the laboratory domain. By relying on the two level modelling approach for representing identity, the proposed design facilitates cross-referencing and disambiguation of certain demographics standards and information models. The work also demonstrates how it can also be used to represent additional clinical identified entities such as specimen and order as subjects of clinical documentation

Contributions to energy informatics, data protection, AI-driven cybersecurity, and explainable AI

This cumulative dissertation includes eleven papers dealing with energy informatics, privacy, artificial intelligence-enabled cybersecurity, explainable artificial intelligence, ethical artificial intelligence, and decision support. In addressing real-world challenges, the dissertation provides practical guidance, reduces complexity, shows insights from empirical data, and supports decision-making. Interdisciplinary research methods include morphological analysis, taxonomies, decision trees, and literature reviews. From the resulting design artifacts, such as design principles, critical success factors, taxonomies, archetypes, and decision trees ¬ practitioners, including energy utilities, data-intensive artificial intelligence service providers, cybersecurity consultants, managers, policymakers, regulators, decision-makers, and end users can benefit. These resources enable them to make informed and efficient decisions

BIG DATA AND ANALYTICS AS A NEW FRONTIER OF ENTERPRISE DATA MANAGEMENT

Big Data and Analytics (BDA) promises significant value generation opportunities across industries. Even though companies increase their investments, their BDA initiatives fall short of expectations and they struggle to guarantee a return on investments. In order to create business value from BDA, companies must build and extend their data-related capabilities. While BDA literature has emphasized the capabilities needed to analyze the increasing volumes of data from heterogeneous sources, EDM researchers have suggested organizational capabilities to improve data quality. However, to date, little is known how companies actually orchestrate the allocated resources, especially regarding the quality and use of data to create value from BDA. Considering these gaps, this thesis – through five interrelated essays – investigates how companies adapt their EDM capabilities to create additional business value from BDA. The first essay lays the foundation of the thesis by investigating how companies extend their Business Intelligence and Analytics (BI&A) capabilities to build more comprehensive enterprise analytics platforms. The second and third essays contribute to fundamental reflections on how organizations are changing and designing data governance in the context of BDA. The fourth and fifth essays look at how companies provide high quality data to an increasing number of users with innovative EDM tools, that are, machine learning (ML) and enterprise data catalogs (EDC). The thesis outcomes show that BDA has profound implications on EDM practices. In the past, operational data processing and analytical data processing were two “worlds” that were managed separately from each other. With BDA, these "worlds" are becoming increasingly interdependent and organizations must manage the lifecycles of data and analytics products in close coordination. Also, with BDA, data have become the long-expected, strategically relevant resource. As such data must now be viewed as a distinct value driver separate from IT as it requires specific mechanisms to foster value creation from BDA. BDA thus extends data governance goals: in addition to data quality and regulatory compliance, governance should facilitate data use by broadening data availability and enabling data monetization. Accordingly, companies establish comprehensive data governance designs including structural, procedural, and relational mechanisms to enable a broad network of employees to work with data. Existing EDM practices therefore need to be rethought to meet the emerging BDA requirements. While ML is a promising solution to improve data quality in a scalable and adaptable way, EDCs help companies democratize data to a broader range of employees