Special Issue "Advances in Information System Analysis and Modeling (AISAM)"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 22 January 2022.

Special Issue Editors

Prof. Dr. Malgorzata Pankowska
E-Mail Website
Guest Editor
Department of Informatics, University of Economics in Katowice, 40-287 Katowice, Poland
Interests: information system analysis and modeling; e-business strategies and systems; IT management; data governance; enterprise architecture modeling; ICT project management
Prof. Dr. Emilio Insfran
E-Mail Website
Guest Editor
Department of Computer Systems and Computation, Universitat Politècnica de València, 46019 Valencia, Spain
Interests: information system design and operations; requirements engineering; model-driven engineering; cloud service architectures; software quality (Usability, UX)

Special Issue Information

Dear Colleagues,

Information system analysis and modeling is a research field in which analysts continually create and implement new modeling frameworks, languages, techniques, software tools, and research methods. System analysis aims to support information system development more effectively, efficiently, and reliably. All information systems development projects move through the same phases of planning, analysis, design, implementation, deployment, and maintenance. 

In all these phases, project stakeholders focus on system artifact conceptualization, modeling, design, implementation, and evaluation. Analysts are needed to elicit or gather requirements, model the business needs, and create blueprints for how the system should be built. Information systems are always developed and implemented in a certain context. Therefore, analysts are also asked to consider the social, economic, legal, and technology contexts. These contexts can also be a subject of modeling. 

Today, the cost of developing modern information systems is composed primarily of the cost associated with the development activity and not the infrastructure and computers in which these systems will be deployed and operated. Therefore, the proposed approaches of analysis and modeling are required to include this cost-optimization aspect or controlling these costs. Beyond, information systems development and operations (IS DevOps) is also considered an area of interest since analysis and design activities should be aligned with operation decisions to facilitate the continuous delivery of value to customers in order to respond to the continuously changing requirements and needs. 

Traditionally, information system analysis and modeling focus on the development of transactional information systems. However, the increasing volume and complexity of business data is driving the adoption of business intelligence, artificial intelligence, machine learning, and digital analytics, which need to be smoothly integrated in the whole process of actual information systems development. Therefore, this Special Issue will also pay special attention to how all these emerging AI-based trends affect information system analysis and modeling and how they can be used toward more affective decision making along system development activities and during system operation and maintenance. 

Our Special Issue, therefore, reflects that information system analysis and modeling is a critical phase in the development life cycle and that information management, given the exponential growth in volume and complexity, is a current challenge that needs to be addressed from the early phases of the development. 

This Special Issue invites papers covering a wide range of topics, from system requirement conceptualization to system analysis, design and operations, as well as maintenance and reverse engineering. 

We will accept papers for peer review in the following areas of interest:

  • System analysis methodologies, frameworks, and languages;
  • Requirement engineering;
  • Orientations in system analysis and modeling;
  • System analysis for business intelligence, digital analytics, machine learning, e-business systems;
  • System analysis in reverse engineering
  • Analytical thinking and problem solving
  • Information systems development and operations (IS DevOps)

Prof. Dr. Malgorzata Pankowska
Prof. Dr. Emilio Insfran
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. Applied Sciences 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 2000 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.

Keywords

  • system analysis
  • modeling language
  • business information systems
  • system analysis performance indicators
  • business analysis techniques and technologies
  • digital analytics requirements
  • conflict resolution
  • constraints on the solution
  • continuous improvement of analysis
  • DevOps

Published Papers (8 papers)

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Research

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Article
Driver Behavior Classification System Analysis Using Machine Learning Methods
Appl. Sci. 2021, 11(22), 10562; https://doi.org/10.3390/app112210562 (registering DOI) - 10 Nov 2021
Viewed by 222
Abstract
Distraction while driving occurs when a driver is engaged in non-driving activities. These activities reduce the driver’s attention and focus on the road, therefore increasing the risk of accidents. As a consequence, the number of accidents increases and infrastructure is damaged. Cars are [...] Read more.
Distraction while driving occurs when a driver is engaged in non-driving activities. These activities reduce the driver’s attention and focus on the road, therefore increasing the risk of accidents. As a consequence, the number of accidents increases and infrastructure is damaged. Cars are now equipped with different safety precautions that ensure driver awareness and attention at all times. The first step for such systems is to define whether the driver is distracted or not. Different methods are proposed to detect such distractions, but they lack efficiency when tested in real-life situations. In this paper, four machine learning classification methods are implemented and compared to identify drivers’ behavior and distraction situations based on real data corresponding to different behaviors such as aggressive, drowsy and normal. The data were randomized for a better application of the methods. We demonstrate that the gradient boosting method outperforms the other used classifiers. Full article
(This article belongs to the Special Issue Advances in Information System Analysis and Modeling (AISAM))
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Article
Smart Mobility and Aspects of Vehicle-to-Infrastructure: A Data Viewpoint
Appl. Sci. 2021, 11(22), 10514; https://doi.org/10.3390/app112210514 - 09 Nov 2021
Viewed by 346
Abstract
The aim of this article is to describe estimates of data difficulty and aspects of the data viewpoint within Vehicle-to-Infrastructure (V2I) communication in the Smart Mobility concept. The historical development of the database system’s architecture, that stores and processes a larger amount of [...] Read more.
The aim of this article is to describe estimates of data difficulty and aspects of the data viewpoint within Vehicle-to-Infrastructure (V2I) communication in the Smart Mobility concept. The historical development of the database system’s architecture, that stores and processes a larger amount of data, is currently sufficient and effective for the needs of today’s society. The goal of vehicle manufacturers is the continual increase in driving comfort and the use of multiple sensors to sense the vehicle’s surroundings, as well as to help the driver in critical situations avoid danger. The increasing number of sensors is directly related to the amount of data generated by the vehicle. In the automotive industry, it is crucial that autonomous vehicles can process data in real time or can locate itself in precise accuracy, for the decision-making process. To meet these requirements, we will describe HD maps as a key segment of autonomous control. It alerts the reader to the need to address the issue of real-time Big Data processing, which represents an important role in the concept of Smart Mobility. Full article
(This article belongs to the Special Issue Advances in Information System Analysis and Modeling (AISAM))
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Article
The EU-SENSE System for Chemical Hazards Detection, Identification, and Monitoring
Appl. Sci. 2021, 11(21), 10308; https://doi.org/10.3390/app112110308 - 03 Nov 2021
Viewed by 294
Abstract
Chemical reconnaissance, defined as hazards detection, identification, and monitoring, requires tools and solutions which provide reliable and precise data. In this field, the advances of artificial intelligence can be applied. This article aims to propose a novel approach for developing a chemical reconnaissance [...] Read more.
Chemical reconnaissance, defined as hazards detection, identification, and monitoring, requires tools and solutions which provide reliable and precise data. In this field, the advances of artificial intelligence can be applied. This article aims to propose a novel approach for developing a chemical reconnaissance system that relies on machine learning, modelling algorithms, as well as the contaminant dispersion model to combine signals from different sensors and reduce false alarm rates. A case study of the European Union Horizon 2020 project–EU-SENSE is used and the main features of the system are analysed: heterogeneous sensor nodes components, chemical agents to be detected, and system architecture design. Through the proposed approach, chemical reconnaissance capabilities are improved, resulting in more effective crisis management. The idea for the system design can be used and developed in other areas, namely, in biological or radiological threat reconnaissance. Full article
(This article belongs to the Special Issue Advances in Information System Analysis and Modeling (AISAM))
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Article
Towards an Ontology-Driven Information System for Archaeological Pottery Studies: The Greyware Experience
Appl. Sci. 2021, 11(17), 7989; https://doi.org/10.3390/app11177989 - 29 Aug 2021
Viewed by 349
Abstract
The archaeological analysis of medieval and modern pottery has benefited from the consolidation of archaeometry in the domain of Medieval Archaeology in the past few decades. As part of an ongoing research project devoted to the characterization of pottery production, distribution processes and [...] Read more.
The archaeological analysis of medieval and modern pottery has benefited from the consolidation of archaeometry in the domain of Medieval Archaeology in the past few decades. As part of an ongoing research project devoted to the characterization of pottery production, distribution processes and technological transfer, we deal with a considerable amount of data that are very diverse in origin and nature and must be exploited within an integrated information system in order to provide information for historical knowledge. The Greyware system has been designed to fulfil this goal and provides the main categories for pottery analysis within a shareable and reusable scenario. Its development and application prove that a little semantics goes a long way and that the creation of domain ontologies for archaeological research is an iterative process under development, as long as several projects sharing data, resources and time can develop a collaborative framework to maximize the assets of individual expertise and collaborative work. In this paper, we discuss the requirements of the system, the challenge of developing strategies for normalized data management and their potential for exploiting historical vestiges from an integrated perspective. Full article
(This article belongs to the Special Issue Advances in Information System Analysis and Modeling (AISAM))
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Article
Blockchain, Enterprise Resource Planning (ERP) and Accounting Information Systems (AIS): Research on e-Procurement and System Integration
Appl. Sci. 2021, 11(15), 6792; https://doi.org/10.3390/app11156792 - 23 Jul 2021
Viewed by 1056
Abstract
Accounting information systems (AISs), the core module of any enterprise resource planning (ERP) system, are usually designed as centralised systems. Nowadays, the continuous development and applications of blockchain, or more broadly—distributed ledger technology (DLT), can change the architecture, overcome and improve some limitations [...] Read more.
Accounting information systems (AISs), the core module of any enterprise resource planning (ERP) system, are usually designed as centralised systems. Nowadays, the continuous development and applications of blockchain, or more broadly—distributed ledger technology (DLT), can change the architecture, overcome and improve some limitations of centralised systems, most notably security and privacy. An increasing number of authors are suggesting the application of blockchain technologies in management, accounting and ERPs. This paper aims to examine the emerging literature on this field, and an immediate result is that blockchain applications can have significant benefits. The paper’s innovative contribution and considerable objective are to examine if blockchain can be successfully integrated with AIS and ERPs. We find that blockchain can facilitate integration at multiple levels and better serve various purposes as auditing compliance. To demonstrate that, we analyse e-procurement systems and operations using case study research methodology. The findings suggest that DLT, decentralised finance (DeFI), and financial technology (FinTech) applications can facilitate integrating AISs and ERP systems and yield significant benefits for efficiency, productivity and security. Full article
(This article belongs to the Special Issue Advances in Information System Analysis and Modeling (AISAM))
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Article
Genetic Algorithm for the Retailers’ Shelf Space Allocation Profit Maximization Problem
Appl. Sci. 2021, 11(14), 6401; https://doi.org/10.3390/app11146401 - 11 Jul 2021
Viewed by 540
Abstract
This paper discusses the problem of retailers’ profit maximization regarding displaying products on the planogram shelves, which may have different dimensions in each store but allocate the same product sets. We develop a mathematical model and a genetic algorithm for solving the shelf [...] Read more.
This paper discusses the problem of retailers’ profit maximization regarding displaying products on the planogram shelves, which may have different dimensions in each store but allocate the same product sets. We develop a mathematical model and a genetic algorithm for solving the shelf space allocation problem with the criteria of retailers’ profit maximization. The implemented program executes in a reasonable time. The quality of the genetic algorithm has been evaluated using the CPLEX solver. We determine four groups of constraints for the products that should be allocated on a shelf: shelf constraints, shelf type constraints, product constraints, and virtual segment constraints. The validity of the developed genetic algorithm has been checked on 25 retailing test cases. Computational results prove that the proposed approach allows for obtaining efficient results in short running time, and the developed complex shelf space allocation model, which considers multiple attributes of a shelf, segment, and product, as well as product capping and nesting allocation rule, is of high practical relevance. The proposed approach allows retailers to receive higher store profits with regard to the actual merchandising rules. Full article
(This article belongs to the Special Issue Advances in Information System Analysis and Modeling (AISAM))
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Article
Generating Block-Structured Parallel Process Models by Demonstration
Appl. Sci. 2021, 11(4), 1876; https://doi.org/10.3390/app11041876 - 20 Feb 2021
Cited by 1 | Viewed by 509
Abstract
Programming by demonstration (PBD) is a technique which allows end users to create, modify, accommodate, and expand programs by demonstrating what the program is supposed to do. Although the ideal of common-purpose programming by demonstration or by examples has been rejected as practically [...] Read more.
Programming by demonstration (PBD) is a technique which allows end users to create, modify, accommodate, and expand programs by demonstrating what the program is supposed to do. Although the ideal of common-purpose programming by demonstration or by examples has been rejected as practically unrealistic, this approach has found its application and shown potentials when limited to specific narrow domains and ranges of applications. In this paper, the original method of applying the principles of programming by demonstration in the area of process mining (PM) to interactive construction of block-structured parallel business processes models is presented. A technique and tool that enable interactive process mining and incremental discovery of process models have been described in this paper. The idea is based on the following principle: using a demonstrational user interface, a user demonstrates scenarios of execution of parallel business process activities, and the system gives a generalized model process specification. A modified process mining technique with the α|| algorithm applied on weakly complete event logs is used for creating parallel business process models using demonstration. Full article
(This article belongs to the Special Issue Advances in Information System Analysis and Modeling (AISAM))
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Review

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Review
On a Certain Research Gap in Big Data Mining for Customer Insights
Appl. Sci. 2021, 11(15), 6993; https://doi.org/10.3390/app11156993 - 29 Jul 2021
Cited by 1 | Viewed by 982
Abstract
The main purpose of this paper is to provide a theoretically grounded discussion on big data mining for customer insights, as well as to identify and describe a research gap due to the shortcomings in the use of the temporal approach in big [...] Read more.
The main purpose of this paper is to provide a theoretically grounded discussion on big data mining for customer insights, as well as to identify and describe a research gap due to the shortcomings in the use of the temporal approach in big data analyzes in scientific literature sources. This article adopts two research methods. The first method is the systematic search in bibliographic repositories aimed at identifying the concepts of big data mining for customer insights. This method has been conducted in four steps: search, selection, analysis, and synthesis. The second research method is the bibliographic verification of the obtained results. The verification consisted of querying the Scopus database with previously identified key phrases and then performing trend analysis on the revealed Scopus results. The main contributions of this study are: (1) to organize knowledge on the role of advanced big data analytics (BDA), mainly big data mining in understanding customer behavior; (2) to indicate the importance of the temporal dimension of customer behavior; and (3) to identify an interesting research gap: mining of temporal big data for a complete picture of customers. Full article
(This article belongs to the Special Issue Advances in Information System Analysis and Modeling (AISAM))
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