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Molnar, Balint
Eötvös Loránd University of Budapest
Budapest, Hungary
 
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Bálint Molnár is an associate professor at Eötvös Loránd University’s Faculty of Informatics in Budapest, Hungary. He teaches courses on methodologies of information systems development, enterprise resource planning (ERP), integrated systems, web technologies for enterprise information systems, and service science.

Bálint’s research areas include formal and mathematical methods for information systems modeling, ERP systems, complex information systems, business process modeling, the semantic web, enterprise architectures, service-oriented architecture (SOA), service science, and the application of data science methods to the world of information systems. He participates in research projects at the university and supervises several doctoral students. His work on information systems and their formal modeling led to the introduction of hypergraphs and homology into the formal modeling of business processes and information system architecture.

Bálint has published more than 250 papers and serves on the editorial boards for the Electronic Journal of Information Systems Evaluation and the European Journal of Applied Economics. He is a regular reviewer for several scientific conferences and journals, including the Conference on Enterprise Information Systems (CENTERIS), Journal of Computing and Information Technology (CIT), and IEEE Access, and a member of many professional organizations (János Bolyai Mathematical Society, John von Neumann Computer Society, ISACA, the ACM). He formerly worked as a consultant and project manager in the Hungarian Public Administration.

Bálint has been a reviewer for Computing Reviews since 2014. He has written more than 50 reviews.

 
 
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- 4 of 4 reviews

   
   Mathematical foundations of big data analytics
Shikhman V., Müller D., Springer International Publishing, New York, NY, 2021. 288 pp.  Type: Book (978-3-662625-20-0), Reviews: (2 of 2)

Data science--and big data analytics as its most recent subdiscipline--is a fashionable research area and teaching subject. Many open-access libraries and source codes provide the opportunity to play with the availabl...

Aug 19 2022  
   Data-intensive workflow management: for clouds and data-intensive and scalable computing environments
de Oliveira D., Liu J., Pacitti E., Morgan&Claypool Publishers, San Rafael, CA, 2019. 180 pp.  Type: Book (978-1-681735-57-3)

Data-intensive workflows turn up in scientific domains where the most current information technologies find application areas. The “differentia specifica” between business and scientific workflows is the importance ...

Sep 16 2020  
   Data exploration using example-based methods
Lissandrini M., Mottin D., Palpanas T., Velegrakis Y., Morgan&Claypool Publishers, San Rafael, CA, 2019. 164 pp.  Type: Book (978-1-681734-55-2)

This book provides a comprehensive overview and description of a broad range of search algorithms. Because the authors are experts at example-based searching methods, the book discusses the application of example-based approaches to se...

Nov 18 2019  
   Research for practice: troubling trends in machine-learning scholarship
Lipton Z., Steinhardt J. Communications of the ACM 62(6): 45-53, 2019.  Type: Article

The article reviews the attention paid to an important issue in machine learning research, but the problem raised can be generalized to other fields within computer science and informatics....

Aug 20 2019  
 
 
   
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