@CONFERENCE{MTMT:34824235,
	title = {Architectures of Contemporary Information Systems and Legal/Regulatory Environment},
	url = {https://m2.mtmt.hu/api/publication/34824235},
	author = {Molnár, Bálint and Báldy, Péter and Balázs, Krisztina Eszter},
	booktitle = {Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 2,},
	unique-id = {34824235},
	abstract = {The information interchange, between humans and computers, and the data processing in the context of Information Systems and Enterprises became a complex structure with a rapidly developing technology stack. This paper proposes an integrated approach that combines recent technological approaches in architecture for Information Systems and Enterprises concerning the recent development of the technology landscape. There are established scientific and research disciplines in domains such as Enterprise Architecture, Analysis, and Design of Information Systems, Business Process Management and Modeling, Data Management and Administration, and Human-Computer Interaction. The rapid development of Artificial Intelligence (Machine Learning, Data Science) and its applications in enterprise environments necessitates defining a research framework that can support the alignment of these components for research and practical applications. Since Information Systems are of socio-technological phenomenon, and quick development of technologies meddles with the privacy, and personal data of individuals. This fact implies that the legal, regulatory, and ethical set of rules should be considered and built-in through architectural building blocks into Enterprise Architecture. Therefore, the existing and emerging regulatory frameworks are considered to make it possible and realize compliance through artifacts that care about conformance to rules. The legal environment and the ethics that are deduced from the legal rules touch the local and public administrations that operate the cities through advanced IT systems.},
	keywords = {Public administration; Enterprise Architecture; blockchain; business processes; XAI; Large language models; Lakehouse; Governance of Cities},
	year = {2024},
	pages = {753-761},
	orcid-numbers = {Molnár, Bálint/0000-0001-5015-8883}
}

@CONFERENCE{MTMT:34824234,
	title = {Business Intelligence Reporting by Linguistic Summaries for Smart Cities: A Case on Explaining Bicycle Sharing Patterns},
	url = {https://m2.mtmt.hu/api/publication/34824234},
	author = {Mináriková, Erika and Pisoni, Galena and Molnár, Bálint and Kristín Skaftadottir, Hanna},
	booktitle = {Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 2,},
	unique-id = {34824234},
	abstract = {An increasing number of intelligent urban services rely on the use of Information and Communication Technologies (ICT). Data-driven approach is often considered for supporting sustainable cities, provided the pervasive nature of the Internet of Things (IoT) like sensors, and their capabilities to collect data for elaborating to the cities. This paper focuses on an intelligent business reporting approach explaining the bicycle sharing patterns by linguistic summaries in order to provide relevant insights for decision makers and citizens. We explored the developments in bicycle sharing stations in different periods of the day for months and seasons. The business intelligence query operations of drill-down and roll-up are often used in data reporting and analysis. In this work, these operations are realized by linguistic summaries. The main aim is to propose an approach for analysis and visualization in an understandable and interpretable way for diverse user categories. Experiments were conducted on the Dublin bicycle sharing data set. Finally, a way how cities can set in place the collection of data coming from different sources, as well as relevant enterprise infrastructures and data analytic pipelines for such service are discussed. a},
	keywords = {Smart cities; Enterprise architectures; Linguistic summaries; Business Intelligence Reporting; Drill-Down and Roll-up Summaries},
	year = {2024},
	pages = {762-768},
	orcid-numbers = {Molnár, Bálint/0000-0001-5015-8883}
}

@article{MTMT:34760161,
	title = {AI-Based Solution for Sustainability Tracing for Companies},
	url = {https://m2.mtmt.hu/api/publication/34760161},
	author = {Pisoni, Galena and Molnár, Bálint},
	doi = {10.4018/IJKM.340723},
	journal-iso = {IJKM},
	journal = {INTERNATIONAL JOURNAL OF KNOWLEDGE MANAGEMENT},
	volume = {20},
	unique-id = {34760161},
	issn = {1548-0666},
	abstract = {Many companies look for novel ways to trace their operational sustainability. The application of AI to analyze and make sense of the big data the company holds represents one promising approach for this aim. The authors study how one can set and design an AI-based solution for improving the sustainability of complex business processes and decision-making in companies of different types. First, they provide a general analysis of current frameworks for measuring adherence to sustainability goals for companies, then they present a conceptual framework and architecture design for an AI-enabled sustainability service for companies. The implications of our research suggest that AI can provide distinct functions: (a) automation: taking big data from different departments and analyzing them with the aim of tracing the sustainability of the company; (b) support: to help decision-making and create relevant insights for stakeholders that are coherent with defined sustainability decision criteria. To the authors' knowledge, no previous research has provided analysis and design of such AI solution for companies.},
	year = {2024},
	eissn = {1548-0658},
	pages = {1-17},
	orcid-numbers = {Pisoni, Galena/0000-0002-3266-1773; Molnár, Bálint/0000-0001-5015-8883}
}

@CONFERENCE{MTMT:34741958,
	title = {REDUCING COMPLEXITY BY USING LINGUISTIC SUMMARIES FOR BUSINESS INTELLIGENCE REPORTING},
	url = {https://m2.mtmt.hu/api/publication/34741958},
	isbn = {9789898704566},
	author = {Galena, Pisoni and Erika, Mináriková and Miroslav, Hudec and Hanna, Kristin Skaftadottir and Molnár, Bálint and Miljan, Vučetić and Denisa, Sedláková},
	booktitle = {17th IADIS INTERNATIONAL CONFERENCE INFORMATION SYSTEMS 2024},
	unique-id = {34741958},
	year = {2024},
	pages = {174-178},
	orcid-numbers = {Molnár, Bálint/0000-0001-5015-8883}
}

@CONFERENCE{MTMT:34741953,
	title = {SUSTAINABILITY TRACING FOR COMPANIES: GOVERNANCE, REGULATION, ECONOMICAL AND ETHICAL CONSIDERATIONS},
	url = {https://m2.mtmt.hu/api/publication/34741953},
	author = {Galena, Pisoni and Molnár, Bálint and Szabolcs, Korba and Maria, Moloney},
	booktitle = {17th IADIS INTERNATIONAL CONFERENCE INFORMATION SYSTEMS 2024},
	unique-id = {34741953},
	year = {2024},
	pages = {136-140},
	orcid-numbers = {Molnár, Bálint/0000-0001-5015-8883}
}

@misc{MTMT:34741944,
	title = {SUSTAINABILITY TRACING FOR COMPANIES: GOVERNANCE, REGULATION, ECONOMICAL AND ETHICAL CONSIDERATIONS},
	url = {https://m2.mtmt.hu/api/publication/34741944},
	isbn = {9789898704566},
	author = {Galena, Pisoni and Molnár, Bálint and Szabolcs, Korba and Maria, Moloney},
	publisher = {International Association for Development of the Information Society Press},
	unique-id = {34741944},
	year = {2024},
	orcid-numbers = {Molnár, Bálint/0000-0001-5015-8883}
}

@article{MTMT:34536179,
	title = {Review: Quick start guide to large language models: strategies and best practices for using ChatGPT and other LLMs},
	url = {https://m2.mtmt.hu/api/publication/34536179},
	author = {Molnár, Bálint},
	journal-iso = {COMP REV - ACM},
	journal = {COMPUTING REVIEWS - ASSOCIATION FOR COMPUTING MACHINERY},
	volume = {2024},
	unique-id = {34536179},
	year = {2024},
	eissn = {1530-6585},
	orcid-numbers = {Molnár, Bálint/0000-0001-5015-8883}
}

@inproceedings{MTMT:34548975,
	title = {An Analysis of the Concept of Cognitive Enterprise Architecture Foundation of Cognitive Enterprise Archtecture},
	url = {https://m2.mtmt.hu/api/publication/34548975},
	author = {Molnár, Bálint},
	booktitle = {14th IEEE International Conference on Cognitive Infocommunications},
	doi = {10.1109/CogInfoCom59411.2023.10397536},
	unique-id = {34548975},
	year = {2023},
	pages = {107-112},
	orcid-numbers = {Molnár, Bálint/0000-0001-5015-8883}
}

@inproceedings{MTMT:34472933,
	title = {Data Management and Enterprise Architectures for Responsible AI Services},
	url = {https://m2.mtmt.hu/api/publication/34472933},
	author = {Pisoni, Galena and Molnár, Bálint},
	booktitle = {Open Science in Engineering},
	doi = {10.1007/978-3-031-42467-0_83},
	unique-id = {34472933},
	abstract = {Big data is becoming a reality. Complex and difficult-to-understand data may be found in a wide range of industries. Big data is a critical component of enterprise services and technology architectures. Data science techniques and methodologies can be applied in many aspects of the working of companies. In this paper, first, as a background, we provide an overview of knowledge management practices and data analysis strategies and techniques in the daily operations of companies working towards development of AI agents. Furthermore, we look at the need in particular companies to develop human centric AI solutions; then, we discuss the basics for cross-disciplinary research, in which we stress the need to rethink development processes of AI services and make them more responsible. Thereby, we define research questions to investigate the problem. As the research proposal discusses, companies and public institutions can create and develop new responsible, ethical, and transparent AI services.},
	year = {2023},
	pages = {879-884},
	orcid-numbers = {Pisoni, Galena/0000-0002-3266-1773; Molnár, Bálint/0000-0001-5015-8883}
}

@article{MTMT:34411978,
	title = {Knowledge Management and Data Analysis Techniques for Data-Driven Financial Companies},
	url = {https://m2.mtmt.hu/api/publication/34411978},
	author = {Pisoni, Galena and Molnár, Bálint and Tarcsi, Ádám},
	doi = {10.1007/s13132-023-01607-z},
	journal-iso = {J KNOWL ECON},
	journal = {JOURNAL OF THE KNOWLEDGE ECONOMY},
	volume = {1},
	unique-id = {34411978},
	issn = {1868-7865},
	abstract = {In today’s fast-paced financial industry, knowledge management and data-driven decision making have become essential for the success of financial technology (FinTech) companies. Big data (BD) is a prevalent phenomenon that can be found across many industries, including finance. Despite its complexity and difficulty to comprehend, big data is a critical component of financial services enterprises and technology architectures. We examine BD from various aspects, considering data science (DS) techniques and methodologies that can be applied during the operation of an enterprise. Our aim is to provide an overview of knowledge management (KM) practices and data analysis (DA) strategies and techniques in the daily operations of financial companies. We address the role of knowledge management, data analytics in a financial institution. The paper demonstrates financial institutions’ enablement for new services resulting from technological advancements.},
	year = {2023},
	eissn = {1868-7873},
	orcid-numbers = {Pisoni, Galena/0000-0002-3266-1773; Molnár, Bálint/0000-0001-5015-8883}
}