KMIS 2023 Abstracts

Full Papers Short Papers
Full Papers
Paper Nr: 86
Title:

Challenges to Implementing Effective Data Governance: A Literature Review
Authors:

Carlos A. Bassi and Solange N. Alves-Souza
Abstract: Implementing an efficient data governance program implies overcoming a series of identified challenges. Reviewing the scope of publications of case studies (CS) of Data Governance (DG) projects allowed identifying diverse types of challenges directly related to the data and/or the organization while others suffer from external influences. The market segment and the country of the operation can also influence DG projects. Investigating these challenges to find ways to face them can contribute to the successful implementation of a DG program. From 201 papers initially collected in the process of the literature review, 44 publications presented CS that implement DG projects. As a result, we identify the most impactful challenges for implementation of DG projects that should be prioritized.
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Paper Nr: 95
Title:

Adaptive Granulation: Data Reduction at the Database Level
Authors:

Hossein Haeri, Niket Kathiriya, Cindy Chen and Kshitij Jerath
Abstract: In an era where data volume is growing exponentially, effective data management techniques are more crucial than ever. Traditional methods typically manage the size of large datasets by reducing or aggregating data using a pre-specified granularity. However, these methods often face challenges in retaining vital information when dealing with large and complex datasets, especially when such datasets reside in databases. We propose a novel and innovative approach called Adaptive Granulation that addresses this issue by performing data reduction or aggregation at the database level itself. A key concern that arises in the data reduction process is the potential trade-off between the reduction of data volume and the preservation of prediction accuracy. This is particularly relevant in scenarios where the primary goal is to leverage the reduced dataset for predictive modeling. Our method employs Allan variance, originally developed for frequency stability analysis of atomic clocks, to dynamically adjust the granularity of data aggregation based on the inherent structure and characteristics of the dataset. By minimizing bias across different scales, Adaptive Granulation effectively manages trade-offs between diverse aspects of the data such as underlying patterns, noise levels, and sampling density. This paper outlines the algorithmic strategies for implementing Adaptive Granulation at the database level and assesses its performance through the reduction of the training set size for a downstream regression task on a variety of real-world and synthetic datasets. The results indicate that our method can adaptively optimize granule sizes to effectively balance data patterns, noise levels, and sample densities across the entire data space. Adaptive Granulation thus represents a significant advancement for efficient data management and reduction in the big data era.
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Paper Nr: 124
Title:

Explainable Intrusion Detection for Internet of Medical Things
Authors:

Shafique A. Memon, Uffe K. Wiil and Mutiullah Shaikh
Abstract: IoMT sensors are used for continuous real-time remote monitoring of patients’ health indicators. IoMT integrate several devices to capture sensitive medical data from devices such as implants and wearables that results in cost-effective and improved health. In IoT settings, the Message Queuing Telemetry Transport (MQTT) protocol is frequently used for machine-to-machine data transfer. However, secure transmission of sensitive health data is critical because these devices are resource constrained and are more vulnerable to MQTT based threats including brute force attack. This warrants a robust, effective, and reliable threat mitigation mechanism, while maintaining a fine balance between accuracy and interpretability. Based on a comprehensive overview of previous work and available datasets, we propose an explainable intrusion detection mechanism to detect MQTT-based attacks. The MQTT-IOT-IDS2020 dataset is used as a benchmark. Particle swarm optimization (PSO) is used for the selection of optimal features from the dataset. The performance of ten machine learning (ML) methods is evaluated and compared. The results demonstrate excellent classification accuracies between 97% and 99%. We applied LIME explanations to increase human interpretability for the best performing model.
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Paper Nr: 132
Title:

Digital Transformation of an OEM Development Process from a Socio-Technical Perspective: A Case Study
Authors:

Ekin Uhri, Christoph Matz and Ingrid Isenhardt
Abstract: Based on the RFLP concept (requirements, functions, logic, product) of model-based systems engineering, a function-oriented approach can enable a universal, data and model-driven knowledge management system for product development. Additionally, this approach can automate development steps and ease communication between disciplines. However, the impact of function-orientation on the established organizational structures remains unexplored. This case study investigates the effects of digital transformation towards function-orientation on the knowledge management system of a large corporation from a socio-technical perspective. OSTO® system model (open, socio-technical, economic) is employed to analyse and redesign the socio-technical system to observe the possible effects. The results show that the main limitations of the development department lie within the information and decision-making systems. RFLP-based function-oriented development can address these limitations, resulting in an efficient, universal, data-driven knowledge management system.
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Paper Nr: 144
Title:

Survival Analysis as a Risk Stratification Tool for Threshold Exceedance Forecasting
Authors:

George Marinos, Manos Karvounis and Ioannis N. Athanasiadis
Abstract: This study presents a novel framework designed for predicting threshold exceedance in time series data through the use of survival analysis techniques. Contrasting the traditional binary classification methodologies typically applied to this problem, our approach offers a unique perspective, modeling and predicting not only the occurrence but also the time-to-event information. This significant differentiation furnishes an invaluable tool for understanding and anticipating extreme events, and more importantly, it enhances decision-makers’ comprehension of temporal dynamics of such risks, enabling early intervention strategies. These facets are especially critical in various domains where timely action is essential. The effectiveness of our methodology has been empirically confirmed using both simulated and real-world datasets, showcasing our method’s precision in forecasting threshold exceedance. An illustrative application within the food safety domain, leveraging real-world data related to food recalls over time, further demonstrates the practical utility of our approach, particularly in preventing and controlling high-risk hazards like salmonella. These findings underscore the wide-ranging implications of our method, particularly in applications where understanding the temporal dynamics of risks is paramount.
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Paper Nr: 156
Title:

Ontology Proposal for Support Team: A Case Study in a Software Development Company for the Financial Market
Authors:

Maria C. Lazaretti, Vitor C. Silva, Nelson N. Tenório Junior, Thaise M. Teixeira and Steffi Becker
Abstract: Knowledge management has become a crucial activity for organizations focused on knowledge. This is particularly true for software development companies, as their knowledge has become a complex factor directly influencing the practice of developing and maintaining software products. One challenge in software maintenance is organizing knowledge effectively. While tools like bug tracking support the maintenance of software-based products, they primarily automate processes and may not address knowledge organization comprehensively. To enhance tool utilization, one approach is to incorporate ontology, which explicitly represent knowledge for efficient retrieval. This work aims to present a prototype ontology that can be further improved through a proactive and reactive knowledge management initiative in a software development company. A case study was conducted in the customer support sector of the company, utilizing data analysis from a dedicated database and engaging in conversations with a company collaborator. The prototype is presented along with its capabilities to address the problem identified in this study. It is concluded that the developed ontology could be an option for assisting the knowledge organization process in the studied company. However, further research is necessary to assess the return on investment of implementing the suggested solution.
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Paper Nr: 159
Title:

A Taxonomy for Autonomous LLM-Powered Multi-Agent Architectures
Authors:

Thorsten Händler
Abstract: Large language models (LLMs) have revolutionized the field of artificial intelligence, endowing it with sophisticated language understanding and generation capabilities. However, when faced with more complex and interconnected tasks that demand a profound and iterative thought process, LLMs reveal their inherent limitations. Autonomous LLM-powered multi-agent systems represent a strategic response to these challenges. While these architectures hold promising potential in amplifying AI capabilities, striking the right balance between different levels of autonomy and alignment remains the crucial challenge for their effective operation. This paper proposes a comprehensive multi-dimensional taxonomy, engineered to analyze how autonomous LLM-powered multi-agent systems balance the dynamic interplay between autonomy and alignment across various aspects inherent to architectural viewpoints such as goal-driven task management, agent composition, multi-agent collaboration, and context interaction. Our taxonomy aims to empower researchers, engineers, and AI practitioners to systematically analyze the architectural dynamics and balancing strategies employed by these increasingly prevalent AI systems. The exploratory taxonomic classification of selected representative LLM-powered multi-agent systems illustrates its practical utility and reveals potential for future research and development. An extended version of this paper is available on arXiv (Händler, 2023).
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Paper Nr: 166
Title:

Towards Effective Ecosystems: A Framework for Mapping Knowledge Governance and Management Activities of Innovation Ecosystems Constituent Elements
Authors:

Gustavo Simas da Silva
Abstract: Innovation and knowledge ecosystems are integral parts of today’s fast-paced global economy. However, the challenge of effectively governing and managing knowledge within these complex networks remains largely unaddressed. Through a scoping literature review, focusing on existing frameworks, models and best practices related to knowledge management and governance, this paper introduces the ARA (Actors, Resources, Actions) Framework. The framework serve as tool for mapping knowledge management and governance activities in operational, tactical and strategical levels with respect to the constituent elements of innovation ecosystems. A conceptual Entity Relationship Diagram (ERD) is developed, providing a visual representation of the relationships between actors, resources and actions, serving as a referential artifact for ecosystem database modeling. The paper concludes by discussing the practical implications of the ARA Framework for stakeholders and offering insights into future research and the combined utility with other models for innovation and knowledge ecosystems, such as Open Innovation frameworks and the Triple or Quadruple Helix models.
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Short Papers
Paper Nr: 21
Title:

Enhancing Leadership and Management Effectiveness: Leveraging Actor-Network Theory for Project Risk Mitigation
Authors:

Arthur Wilson, Brad Carey and Amma Buckley
Abstract: Investment decisions of leaders and managers influence the adoption and use of digital technologies that then transform their organization’s products, services, and operations. To foster transformation, project management methods are commonly used. However, project failure rates often exceed success rates regardless of the industry sector, or project management methodology. While project success has traditionally been measured in terms of time, cost, and quality, recent research suggests that success includes the dynamic interaction between multiple actors in diverse networks. Traditional project management methods may not adequately identify and help mitigate the risks associated with complex and dynamic influence of leadership and management on project success. This study uses actor-network theory (ANT) to examine opportunities to enhance the effectiveness of leadership and management in projects and mitigating associated risks. By doing so, this study aims to provide insights into how organizations can improve project success rates.
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Paper Nr: 37
Title:

Emotional Intelligence: Predictor of Success and Career Advancement - A Survey of Bulgarian Digital Entrepreneurs
Authors:

Ana Todorova and Diana Antonova
Abstract: Emotional intelligence has gone from being a buzzword to a potential prospect for business success. An increasing body of research focuses on emotionally intelligent leadership, which fosters creativity, encourages open communication, and cultivates loyalty among employees and business groups. Discussions about whether emotional intelligence predicts the success and professional realization of the individual are still in the early phase of research in Bulgarian scientific and business circles. The authors of this report allow that emotional intelligence has a significant, positive relationship with career success and advancement. The authors surveyed 1175 Bulgarian digital entrepreneurs to confirm or reject the claim. The results show that women have more developed emotional intelligence competencies – self-awareness, self-control, motivation, empathy and social skills. However, the analysis demonstrates that emotional intelligence is not a sufficient predictor of career development..
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Paper Nr: 49
Title:

Autonomy and Turnover: A Survey Applied to Distributed Software Teams
Authors:

Luis Amorim, Ivaldir D. Farias Júnior and Marcelo Marinho
Abstract: Distributed teams have gained prominence in software companies. However, studies indicate that Distributed Software Development (DSD) companies often face challenges related to high developer turnover. Conversely, other research suggests that autonomy and its associated factors can mitigate or prevent such turnover. This study investigates the relationship between autonomy and turnover within DSD teams. To accomplish this, we conducted a survey based on previous Systematic Literature Review (SLR) research involving 181 software engineers worldwide. Our findings shed light on the key autonomy factors that impact turnover in DSD projects, including recognition, communication, collaboration, trust, and task balance. By offering a comprehensive understanding of these autonomy factors, our study provides software companies and organizations with valuable insights for addressing the issue of turnover in DSD projects.
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Paper Nr: 52
Title:

Knowledge Transfer Factors for Internal Combustion Engine (ICE) Industry to Electric Vehicle (EV) Industry by Artificial Intelligent: Machine Learning
Authors:

Yinglak Dangjaroen, Mongkolchai Wiriyapinit and Sukree Sinthupinyo
Abstract: This study aims to identify the factors influencing knowledge transfer within companies transitioning from the internal combustion engine (ICE) industry to the electric vehicle (EV) industry through an extensive literature review. In addition to summarizing findings and proposing strategies for utilizing artificial intelligence in knowledge transfer, our framework reveals the relevance of three key knowledge transfer factors and three distinct forms of artificial intelligence, including machine learning, in facilitating knowledge transfer. These insights can prove invaluable to entrepreneurs operating within the internal combustion engine automotive sector, offering essential guidance for enhancing the knowledge transfer process and navigating the transition to the electric vehicle industry. By implementing these strategies, businesses can maintain and support their competitiveness in this evolving business.
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Paper Nr: 57
Title:

Towards Digital Transformation: Knowledge Management as an Enabler in a Public Sector Asset Lifecycle
Authors:

Viivi Siuko, Jussi Myllärniemi and Pasi Hellsten
Abstract: Organisations often have visions of implementing advanced digital technologies, such as digital twins, regardless of whether the organisations are mature enough for these technologies. It is a common misconception that implementing advanced technologies will automatically lead to digital transformation and solve organisational challenges, such as disruptions in information flows or the inability to learn from recurring mistakes. The reality is, however, the contrary: emerging advanced technologies and digital transformation demand first and foremost reliable, high-quality data and the ability to use them. Therefore, organisations with inadequate information processes need to pay attention to their knowledge management (KM). In this paper, we demonstrate how KM is an enabler of digital transformation. A case study of a public sector asset lifecycle was conducted. Data were collected by interviewing 26 people representing the focal case organisation and its stakeholders. The results highlight the importance of organised KM for digital transformation. We identify enablers of digital transformation from the KM perspective.
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Paper Nr: 77
Title:

Bottlenecks in Regional Innovation Ecosystem: A Case on Region with Extremely Low RDI Activity
Authors:

Ville Pöntinen and Jyri Vilko
Abstract: The benefits of innovation ecosystems and the knowledge they contain have long been studied as part of business innovation and their importance has been recognized as vital for regional vigour. Ecosystems always involve different kinds of actors and their mutual roles and dialogue form a complex system. This study examined the performance of an innovation ecosystem in the region in southeast Finland through a singlecase study method. The region is known for the fact that very little innovation activity takes place within it. The study used a group interview as the primary data collection method. The main findings indicated that factors hindering RDI activities in the region include a lack of trust in the actors’ relationships, which made organizations less willing to collaborate, and a weak innovation culture, which appears to be caused in part by the region’s blue-collar traditions and low education levels. Furthermore, the innovation funding received by local higher education institutions had not resulted in a significant increase in company RDI activity. Another problem appeared to be that companies were not ready to commit to long-term co-development and were more interested in achieving short-term benefits by focusing on ongoing projects.
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Paper Nr: 78
Title:

Using Knowledge Maps to Create a Business School Faculty Portrait
Authors:

Tatiana Gavrilova, Dmitry Kudryavtsev and Olga Alkanova
Abstract: The primary university faculty activities are Teaching, Research, Applied practice (e.g. consulting), and Professional Service (including administrative activities). It often happens that the scope and specifics of faculty competencies and expertise are not well understood by colleagues within their university or outside. This paper presents a new approach for mapping faculty competencies in universities, focusing on three dimensions (3D): research, teaching, and applied practice. The approach was demonstrated at a business school, which is a part of a large university. The need for the knowledge map there was driven by the development of the new school strategy and the demand for more intense industry-university collaboration. The survey method was applied for data collection and involved 63 faculty members. The data about the faculty’s expertise was structured using predefined subject areas and presented in the form of digital knowledge maps. These maps represent areas of expertise, including well-developed and underdeveloped areas, providing a comprehensive overview of faculty capabilities. The suggested approach gives universities an opportunity to create such knowledge maps for evidence-based talent and knowledge management.
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Paper Nr: 100
Title:

Data Mesh for Managing Complex Big Data Landscapes and Enhancing Decision Making in Organizations
Authors:

Otmane Azeroual and Radka Nacheva
Abstract: In the age of digitization, data is of the utmost importance. Organizations can gain competitive advantage by being ahead of the curve in organizing data, deriving insights from it, and turning those insights into action. In practice, however, many organizations fail to meet this challenge. Far too many decisions are made without data, decision makers don’t trust their own data. The data warehouse, later the data lake and more recently the data lakehouse have been propagated as solutions to these problems in recent decades. In some cases, this actually succeeds, in other cases challenges remain. The recently prominent data mesh approach changes the perspective on data and in this respect provides valuable impulses for data architectures in general. Data mesh is a new architectural concept for data management in organizations. Therefore, in this paper, we introduce this new data concept and provide a clear overview of the design of a data mesh architecture. We will then show how it can be technically implemented and what potential there is for using data mesh in organizations. Our methodology is a type of investigation that provides a helpful and practical guide to understanding the principles and patterns of data mesh and their implementation in organizations. Our research result has shown that the data mesh approach is therefore a very good tool for organizations where data sharing and reuse is crucial. In addition to facilitating scalability, data mesh can enable better data integration and data management, improving data quality while fostering a culture of data-driven decision-making.
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Paper Nr: 106
Title:

The Dark Side of Sharing Knowledge in the Social Media Era: Faculty Members’ Perspectives
Authors:

Kamla A. Al-Busaidi and Ibtisam Al-Wahaibi
Abstract: This pilot study examines the dark side of social media platforms (SMPs) for knowledge sharing (KS) from knowledge management (KM) and information systems security(ISS) perspectives. SMPs have become a mainstream technology with several potential opportunities for KS especially during the COVID 19 pandemic. However, the literature indicates a dark side to SMPs, and knowledge workers may encounter several challenges that might negatively affect their use. Hence, this study specifically assesses the negative effects of knowledge power loss, codification efforts, privacy breaches and cyberattacks on KS through SMPs. Based on 42 faculty members and structure equation modelling-based analysis, the results indicate that only knowledge power loss is the main negative influencer of knowledge workers’ use of SMPs for KS. Further analysis indicated that knowledge power loss negatively affects sharing implicit not explicit knowledge. This study provides initial insights for researchers and practitioners.
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Paper Nr: 107
Title:

Acceptance of Digital Sales and Marketing Tools: A Customer Perspective on Intention to Use
Authors:

Tommi Mahlamäki, Kaj Storbacka and Samuli Pylkkönen
Abstract: Digital sales and marketing tools are important for the success of modern business-to-business (B2B) companies. While many of these information systems or tools can also be used by the customer, surprisingly little is known about the customer’s perception of these tools and the acceptance process itself. This research targets this research gap by developing and testing a structural equation model (PLS-SEM) to investigate customer perceptions and intentions to use digital sales and marketing tools, namely sales configurators. An online questionnaire was developed, and the responses of 113 B2B professionals were analyzed. The results showed that customers’ intention to use digital sales and marketing tools was influenced by ease of use and perceived usefulness. Ease of navigation and information quality also played a significant role in the acceptance process of these tools.
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Paper Nr: 111
Title:

Utility of Univariate Forecasting for Workload Metrics Predictions in Enterprise Applications
Authors:

Andrey Kharitonov, Roheet Rajendran, Hendrik Müller and Klaus Turowski
Abstract: Modern enterprise IT systems are complex solutions that require careful planning of computational capacities and placement, especially in the cloud environments where the total cost of ownership directly depends on provisioned resources. The decision process on infrastructure transformation or capacity sizing of existing IT landscapes can be supported by collecting and analyzing the workload data of the running systems. However, the scope and length of this data are limited, as its collection is often an expensive and lengthy process. Therefore, within this work, we empirically evaluate multiple techniques for extending the workload data by employing various univariate time series forecasting algorithms. We analyze a use case of SAP-based enterprise applications and rely on real-world workload data collected from various running SAP system landscapes. Our analysis demonstrates that XGBoost is best suited for univariate forecasting SAP-specific key performance indicators for both stationary and trending time series. However, the shape of the workload profile has a high degree of influence on the results of the forecasting. Enterprise applications’ workload data that represent regular day-to-day operations without irregular events is a prerequisite for accurate forecasting.
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Paper Nr: 116
Title:

Digital Twins for Traffic Congestion in Smart Cities: A Novel Solution Using Data Mining Techniques
Authors:

Arianna Anniciello, Simona Fioretto, Elio Masciari and Enea V. Napolitano
Abstract: This article serves as a position paper that explores the complex issue of traffic management in smart cities and the challenges it presents. The problem of urban traffic is particularly relevant in our modern world, where more and more people are moving to urban environments, leading to congestion, pollution and reduced quality of life. To address this challenge, we propose an innovative methodology based on Digital Twins. The paper proposes an extended approach that integrates Digital Twins with other existing techniques such as Trajectory Mining, Process Mining, and Decision Making. These techniques, which combine motion data, process analysis, and data-driven Decision Making, can enrich the Digital Twin model, provide a deeper understanding of traffic flows, and deliver more targeted and effective traffic management solutions. This proposal represents a significant step forward in the search for innovative and sustainable solutions for urban traffic management, and lays the foundation for further research and development in this critical area.
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Paper Nr: 130
Title:

A Knowledge Graph Approach for Exploratory Search in Research Institutions
Authors:

Tim Schopf, Nektarios Machner and Florian Matthes
Abstract: Over the past decades, research institutions have grown increasingly and consequently also their research output. This poses a significant challenge for researchers seeking to understand the research landscape of an institution. The process of exploring the research landscape of institutions has a vague information need, no precise goal, and is open-ended. Current applications are not designed to fulfill the requirements for exploratory search in research institutions. In this paper, we analyze exploratory search in research institutions and propose a knowledge graph-based approach to enhance this process.
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Paper Nr: 137
Title:

Making Information Research-Organizational Operation Translation a Specialized Professional Duty: A Proposal
Authors:

Sherry L. Xie and Yubao Gao
Abstract: The push for information research to be translated into practice, including organizational operations, typically lies with research teams rather than the various information practitioners, such as the managers of information, knowledge, and/or records. This may delay the application of relevant research outcomes to organizational operations, thus harming the advancement of the organization. We propose establishing a professional duty devoted to the research-to-practice/organizational operation translation to address this issue. We specify the duty, argue for its pertinence, and outline a path for its (own) translation into practice. We believe that the proposal is worth experimenting with irrespective of organizational specificities, including their setting ups for the management of information, knowledge, and/or records. For this position paper, we use the field of (government) records management as an example for the experiment.
Paper Nr: 145
Title:

Decoding the Language of Care: A Typology of Caregiver Utterances and Their Influence on Assistive Technology Use
Authors:

Takeru Komori, Dai Sakuma, Miki Saijo and Takumi Ohashi
Abstract: Amid a global caregiver shortage and a growing reliance on assistive technology, this research investigates the intricate interactions between caregivers and care recipients in elder care settings, primarily focusing on caregivers’ verbal utterances and the conditions under which these exchanges occur. Drawing on Weiner’s causal attribution theory, we developed a typology of caregiver utterances that prompt shifts in care recipients’ attributions during the use of assistive technology. This typology—comprised of ’praise’, ’affirmation /acceptance’, ’confirmation’, and ’feedback’ categories—illuminates key links between caregiver communication strategies and care recipients’ perception shifts. Notably, ’confirmation’ utterances tend to align with attributions to ’ability’, whereas ’feedback’ utterances correspond more closely with ’effort’. Our analysis of temporal fluctuations revealed significant changes in the frequency of these utterances throughout various stages of assistive technology usage. By offering a holistic understanding of these complex dynamics, this study seeks to shape the development of more effective caregiver communication strategies. Such enhancements are pivotal to optimize care recipients’ experiences and engagement with assistive technology, thus addressing the ongoing caregiver deficit. Future research endeavors will expand our dataset and examine the potential generalizability of our findings to other caregiving environments.
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Paper Nr: 170
Title:

ZeitGeist: A Generic Tool Supporting the Dissemination of Time Series Data Following FAIR Principles
Authors:

Andreas Schmidt, Mohamad A. Koubaa, Jan Schweikert, Karl-Uwe Stucky, Wolfgang Süß and Veit Hagenmeyer
Abstract: An important point for the widespread dissemination of FAIR-data is the lowest possible entry barrier for preparing and providing data to other scientists according to the FAIR criteria. If scientists have to manually extract, transform and annotate the data according to the FAIR criteria and then export it to make it available to the public, this requires a significant investment of time that does not primarily reward the scientist who prepares and provides the data. The Energy Lab at KIT is running a large cluster of an Influx database management system with energy related time series data being stored in a variety of individual databases over periods of up to 15 years. In order to increase the willingness to make data available to the scientific public, we develop a tool that greatly supports and automates the publication and annotation process of time series data stored in Influx databases.
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Paper Nr: 190
Title:

Tag Recommendation System for Data Catalog Site of Japanese Government
Authors:

Yasuhiro Yamada
Abstract: This paper proposes a tag recommendation system for a data catalog site of the Japanese government. The site publishes datasets that include files containing statistical data, government documents, and other files of the Japanese government. These datasets also each include the title, description, publication date, and tags, where a tag is a single-word or compound term which represents the content of a dataset. The system uses multi-label classification in machine learning to recommend tags for the datasets; multi-label classification is a method that outputs multiple tags for each input dataset. There are many tags already in datasets hosted on the site that appear infrequently. It is difficult to predict such infrequent tags from the datasets by multi-label classification. To deal with this problem, we use an existing oversampling approach which increases the data of infrequent tags in a training dataset for the learning process of the multi-label classification.
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Paper Nr: 29
Title:

Discovering Potential Founders Based on Academic Background
Authors:

Arman Arzani, Marcus Handte, Matteo Zella and Pedro J. Marrón
Abstract: Technology transfer is central to the development of an iconic entrepreneurial university. Academic science has become increasingly entrepreneurial, not only through industry connections for research support or transfer of technology but also in its inner dynamic. To foster knowledge transfer, many universities undergo a scouting process by their innovation coaches. The goal is to find staff members and students, who have the knowledge, expertise and the potential to found startups by transforming their research results into a product. Since there is no systematic approach to measure the innovation potential of university members based on their academic activities, the scouting process is typically subjective and relies heavily on the experience of the innovation coaches. In this paper, we study the discovery of potential founders to support the scouting process using a data-driven approach. We create a novel data set by integrating the founder profiles with the academic activities from 8 universities across 5 countries. We explain the process of data integration as well as feature engineering. Finally by applying machine learning methods, we investigate the classification accurracy of founders based on their academic background. Our analysis shows that using a Random Forest (RF), it is possible to successfully differentiate founders and non-founders. Additionally, this accuracy of the classification task remains mostly stable when applying a RF trained on one university to another, suggesting the existence of a generic founder profile.
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Paper Nr: 56
Title:

Improving Corporate Governance Using DAO
Authors:

Seyed Saman Hashemi-Khiabani and Daniel F. Polónia
Abstract: This study analyzes the transformative potential of Decentralized Autonomous Organizations (DAOs) in reshaping traditional corporate governance through Knowledge Management (KM) and Information Systems (IS). By highlighting the limitations of centralized governance models, such as power centralization, opacity, and stakeholder conflicts, this study argues for the efficacy of DAOs, which are undergirded by blockchain technology and smart contracts. DAOs epitomize non-hierarchical structures, autonomous functionality, and consensus-driven decision making. By leveraging decentralized platforms for transparent transactions and decision recording, DAOs augment organizational accountability and compliance. Through smart contracts, DAOs can effectively codify and manage business rules, ethical standards, and legal requirements, thereby enhancing the utilization of organizational knowledge and information systems. The study concludes that while DAOs may not universally replace traditional organizational structures, they can significantly reduce governance deficiencies and optimize knowledge and resource management, thereby offering a more effective corporate governance model in specific sectors.
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Paper Nr: 75
Title:

Knowledge Sharing Between Higher Educational Institutions: Evaluation of a Transfer Platform
Authors:

Claudia Doering, Holger Timinger and Christian Wolff
Abstract: The paper presents a platform for knowledge and technology transfer processes and relevant information for higher educational institutions (HEIs). Nowadays knowledge transfer or third-mission activities are daily business for universities. Nevertheless, they often lack internal standardized processes and procedures. Every HEI tends to establish their own way of efficiently handling transfer activities, without sharing their knowledge with other HEIs. The presented platform outlines standardized processes with knowledge from several HEIs, presented in digitized form. The processes and the platform were evaluated with use cases and a standardized user experience questionnaire (UEQ). The results presented in this contribution indicate a high need for and positive perception of the processes and the platform.
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Paper Nr: 87
Title:

Graph Analytics for Avian Science Data
Authors:

Ami Pandat, Minal Bhise and Sanjay Srivastava
Abstract: Data management solutions are becoming increasingly necessary as more Big Data applications are developed. One such area that deals with Big Data is Big Graphs. Complex relationships exist in graph-based applications. Analytics and data extraction are better solutions for understanding such complex applications. Data from Avian Science has shown significant growth in recent years. Graph analytics can be used to interpret complex scientific data and their relationships. This paper uses graph analytics to discuss the application of graph analytics in avian science. For the eBird Dataset, four Graph Analytics techniques were identified and implemented. These methods extract information about path patterns, node popularity, connections to other nodes, and clustering. The Dataset includes real-time data on bird observation and distribution. Each analytics technique extracts data from the birds’ observations. The findings show that graph analytics for avian science data can aid in predicting a wide range of crowd-sourced information. Additionally, the work can be expanded using machine learning methods.
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Paper Nr: 101
Title:

Remote Monitoring of Heart Failure Patients Treatment Programme: Customer Experience, Expectations, Barriers and Conditions
Authors:

Annamaija Paunu and Nina Helander
Abstract: Telemedicine in health care is becoming more important as digitalization continues to spread in all areas of our everyday life. The ageing population, not only in Finland, but all over the world, and the shortage of health care personnel forces to develop new solutions. This case study investigates the effectiveness of the telecare programme from the perspective of patients’ service experiences and identifies barriers and conditions that should be taken into account in the wider introduction of a new type of telecare service. Empirical study points out the patients’ expectations of the new telehealth service model and presents benefits and challenges that may occur in new telehealth services. Self-monitoring of health through the device portfolio seems to be an interesting possibility for several respondents in the study. The patient experience in regards to health care and health care personnel was satisfactory and participants had a positive view of remote monitoring of heart failure through the device portfolio.
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Paper Nr: 118
Title:

Benefits and Challenges of Robotic Process Automation
Authors:

Laura Lahtinen, Tommi Mahlamäki and Jussi Myllärniemi
Abstract: Digitalization has been shaping the ways how we work and live for a considerable length of time. Businesses’ competitiveness is partially determined by their capability to adopt and leverage new technologies. One of the latest trends in digitalization is the automation of repetitive, low-cognitive human tasks in white-collar jobs. A tool that was created to automate low-cognitive human tasks, Robotic Process Automation utilizes software robots to address this topic. RPA gains attraction because it is easily scalable, and implemented at a rather low cost and the use of it doesn’t require prior programming skills. This research relies on existing literature and identifies the benefits and challenges of Robotic Process Automation.
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Paper Nr: 126
Title:

Proposed Extensions to the Methodology of Technology Scouting
Authors:

Diego Lasso-Lazo, Francisco Álvarez-Arévalo, Javier Patiño-Chuni, Javier Valdiviezo-Ortiz, Jorge Bermeo-Conto and Juan P. Carvallo
Abstract: An extended framework for the Technology scouting process is developed. The developed framework proposes the use of a series of screening mechanisms and verification instruments that extend the process already described in UNE 166006:2018. The extended framework was implemented in two cases of study for Technology scouting services provided by CEDIA to innovation stakeholders. The results demonstrated the effectiveness of using the extended framework, reducing stagnation points and the risk of information bias, two main issues often reported in the techonology scouting process.
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Paper Nr: 142
Title:

Business Intelligence, Business Analytics, and Intellectual Capital: An Opportunity for Innovation Potential in the Private Healthcare?
Authors:

Pasi Hellsten, Jussi Myllärniemi and Milla Ratia
Abstract: This paper looks upon the role of business intelligence (BI), business analytics (BA), and intellectual capital (IC) in managerial decision-making in the private healthcare sector in Finland, and scrutinizes the potential for innovation, enabled by BI/BA as a function and think it’s value creation in organizations. The study was conducted by qualitative research methods with inductive approach using semi-structured, thematic interviews. The study scrutinizes the managerial insight of BI and BA and the tools’ use in data-driven value creation, also contemplating the potential for organizational operation, both from private healthcare and consulting companies’ point of view, enabling the management of the private healthcare sector to utilize the whole potential and best practices. Two practical outcomes of the study are: it will provide information and understanding on the managerial aspect of BI/BA area in the Finnish private healthcare sector companies and show its potential for innovation.
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Paper Nr: 147
Title:

Toward Standardization and Automation of Data Science Projects: MLOps and Cloud Computing as Facilitators
Authors:

Christian Haertel, Christian Daase, Daniel Staegemann, Abdulrahman Nahhas, Matthias Pohl and Klaus Turowski
Abstract: The significant increase in the amount of generated data provides potential for organizations to improve performance. Accordingly, Data Science (DS), which encompasses the methods to extract knowledge from data, has increased in popularity. Nevertheless, enterprises often fail to reap the benefits from data as they suffer from high failure rates in the conducted DS projects. Literature suggests that the main reason for the lack of success is shortcomings in the current pool of DS project management methodologies. Hence, new procedures for DS are required. Consequently, in this paper, the outline for a model for DS project standardization and automation is discussed. Following a summary of DS project challenges and success factors, the concept, which will incorporate MLOps and cloud technologies, and its individual components to address these issues are described on a high level. Therefore, the foundation for further research endeavors in this area is presented.
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Paper Nr: 175
Title:

Knowledge Economy in the Anthropocene: A Blueprint for Urban Cities
Authors:

Kemi E. Ayanda
Abstract: This paper unfolds transformative paradigms, combining profound insights from the knowledge economy and groundbreaking technology to frame sustainable urban futures in the Anthropocene, an era characterized by significant human-driven ecological transformations. It emphasizes the revolutionary potential of innovations such as Geospatial Technology, internet of things (IoT), and integrated renewables in redefining green and brownfield developments, crucial for forging resilient and ecologically balanced urban habitats. The exploration incorporates diverse strategies like universal digital access, communal participation, and ethical technology deployment, ensuring equitable knowledge dissemination and fostering ethical advancements. These strategies are seamlessly interlaced to create inclusive, sustainable, and resilient urban landscapes, showcasing a profound respect for our planet’s boundaries. The paper, therefore, crafts a visionary blueprint where knowledge, technology, and ethics amalgamate, providing urban spaces the resilience and foresight needed to navigate the multifaceted challenges of the Anthropocene, thereby paving the way for a sustainable and equitable future.
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