Special Issue "Information Systems Modeling Based on Graph Theory"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editors

Prof. András Benczúr
Guest Editor
Sciences of Hungarian Academy of Sciences, Faculty of Informatics, Eötvös Loránd University, Budapest, Hungary
Interests: database, data and document management systems, big data, cloud, modeling of information systems, information theory
Prof. Dr. Domenico Ursino
Guest Editor
Department of Information Engineering (DII), Università Politecnica delle Marche( Università degli Studi di Ancona), Ancona, Italy
Interests: social network analysis, biomedical engineering, data lakes, innovation management and internet of things
Prof. Dr. Bálint Molnár
Guest Editor
Information Systems Department, Faculty of Informatics, Eötvös Loránd University, Budapest, Hungary
Interests: modeling of information systems using formal methods, methods for analyzing and designing information systems, expert systems, application of data science on the field of enterprise information systems

Special Issue Information

Dear Colleagues, 

We are pleased to announce this Special Issue of the journal Mathematics, entitled "Information Systems Modelling Based on Graph Theory." This initiative focuses on the topic of the application of graphs and graph theories in any aspect of information systems, including information system design and modeling in organizations and large systems of information collection in science, medicine, administration, etc.

We aim to include articles containing novel applications of theoretical results on different graph models, including ordinary graphs, hypergraphs, and any kind of labeled or colored digraphs.

Another trend in today's technology is IoT (the Internet of Things), which generates and supplies an enormous amount of data for information systems that apply recent technologies, such as data warehouses and data lakes.

The IoT networks, fog, and edge computing can be considered as systems where computing devices are interrelated and can exchange data between themselves over a communication network. These enormous amounts of data are collected and processed in information systems. The effective design of this complex architecture requires rigorous mathematical- based models, especially graph-based approaches for checking consistency, integrity, and security of models.

Enterprise information systems support and control operational business processes that embrace everything from simple internal back-office processes to complex inter-organizational processes. Technologies such as business process modelling and management, workflow management (WFM), enterprise application integration (EAI), enterprise resource planning (ERP), service-oriented architectures (SoA), and web services (WS) have a sound mathematical background that underlies the semi-formal, visual languages to represent business processes. The methods for Model-to-Program (M-2-P) exploits the fact that the descriptive languages are grounded in mathematics, especially various graph-based approaches. The algorithms that transform the representation of business processes to web services and executable programs rely on formal and graph-theoretic approaches to create reliable operational systems.

Process mining aims to extract information from event logs to capture the business process as it is being executed. To achieve effective and efficient interpretation of the data stored in logs and to exploit the recent methods of data science, one of the reasonable directions is to apply adequate graph representation and pattern matching methodologies.

This Special Issue welcomes theoretical and applied contributions that address graph-theoretic algorithms, technologies, and practices. This Special Issue is devoted to recent advances in information systems and application of graphs in the realm of information and data architecture and graph data models, including but not limited to those in the following areas:

  • Representations of business and data processing
  • The aspect of models in information systems
  • The architecture of information systems
  • Analyses of models represented by finite-state machines and automation
  • Knowledge representation and integration
  • Model transformation
  • Consistency checking of IS models (data and information architecture)
  • Graph databases in IS
  • Querying large models by graph algorithms
  • Applications of ranking graphs
  • Graph representation and logic languages
  • Applications of data science on graph representation of information systems
  • Analytics of real, large graphs and networks
  • Graph pattern search and matching
  • Semantic unification and harmonization
  • Social network analysis and its applications
  • Social Internet of Things (SioT) and its applications
  • Multiple Internet of Things (MIoT) and its applications
  • Horn logic and hypergraphs

As a response to the recent advancements, the objective of this Special Issue is to present a collection of notable methods and applications of graph theoretical approaches in the modeling of information systems. We invite scientists from all around the world to contribute to developing a comprehensive collection of papers on the progressive and high impact of modeling of information systems exploiting properties of various graph-based approaches.

We thus encourage you to send high-quality articles disseminating novel research achievements for this issue.

Prof. Dr. Domenico Ursino
Prof. András Benczúr
Prof. Dr. Bálint Molnár
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • information systems
  • business data processing
  • finite state machines
  • knowledge representation
  • model transformation
  • graph databases
  • ranking graphs
  • logic languages
  • large networks
  • graph pattern matching
  • social networks
  • IoT
  • hypergraphs
  • graph theory
  • graph-algorithms
  • meta-graphs

Published Papers (1 paper)

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Open AccessArticle
Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases
Mathematics 2021, 9(4), 450; https://doi.org/10.3390/math9040450 - 23 Feb 2021
Triplestores or resource description framework (RDF) stores are purpose-built databases used to organise, store and share data with context. Knowledge extraction from a large amount of interconnected data requires effective tools and methods to address the complexity and the underlying structure of semantic [...] Read more.
Triplestores or resource description framework (RDF) stores are purpose-built databases used to organise, store and share data with context. Knowledge extraction from a large amount of interconnected data requires effective tools and methods to address the complexity and the underlying structure of semantic information. We propose a method that generates an interpretable multilayered network from an RDF database. The method utilises frequent itemset mining (FIM) of the subjects, predicates and the objects of the RDF data, and automatically extracts informative subsets of the database for the analysis. The results are used to form layers in an analysable multidimensional network. The methodology enables a consistent, transparent, multi-aspect-oriented knowledge extraction from the linked dataset. To demonstrate the usability and effectiveness of the methodology, we analyse how the science of sustainability and climate change are structured using the Microsoft Academic Knowledge Graph. In the case study, the FIM forms networks of disciplines to reveal the significant interdisciplinary science communities in sustainability and climate change. The constructed multilayer network then enables an analysis of the significant disciplines and interdisciplinary scientific areas. To demonstrate the proposed knowledge extraction process, we search for interdisciplinary science communities and then measure and rank their multidisciplinary effects. The analysis identifies discipline similarities, pinpointing the similarity between atmospheric science and meteorology as well as between geomorphology and oceanography. The results confirm that frequent itemset mining provides an informative sampled subsets of RDF databases which can be simultaneously analysed as layers of a multilayer network. Full article
(This article belongs to the Special Issue Information Systems Modeling Based on Graph Theory)
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