Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Browser

Advances in Information System Analysis and Modeling (AISAM)

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 48684

Share This Special Issue

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
Special Issues, Collections and Topics in MDPI journals

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 submissions that pass pre-check are 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 2400 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 (12 papers)

Download All Papers

Research

Jump to: Review

28 pages, 9591 KiB

Open AccessArticle

MDA-Based Approach for Blockchain Smart Contract Development

by Mantas Jurgelaitis, Lina Čeponienė, Karolis Butkus, Rita Butkienė and Vaidotas Drungilas

Appl. Sci. 2023, 13(1), 487; https://0-doi-org.brum.beds.ac.uk/10.3390/app13010487 - 30 Dec 2022

Cited by 4 | Viewed by 3089

Abstract

Blockchain smart contracts can support the decentralisation of business processes, but due to smart contracts’ specifics, their development is a complicated process. Introducing model-driven development principles in smart contract development can facilitate requirement specification, design, and implementation activities. This paper presents a model-driven development method MDAsmartCD (Model-Driven Architecture-based Smart Contract Development) to alleviate smart contract development by supporting the complete MDA life cycle, covering the definition of Computation-Independent Model, Platform-Independent Model, and two instances of Platform-Specific Models. In MDAsmartCD, model transformations (model-to-model and model-to-text) are used to produce smart contract code in the Hyperledger Fabric platform Go and the Ethereum platform Solidity programming languages. The method application was demonstrated by implementing the smart contract for the hackathon solution and executing the generated Solidity and Go smart contracts in the workflow of issuing certificates for hackathon participants. During the execution of the workflow, both deployed smart contracts behaved identically and recorded analogous results in respective blockchain data storages. This demonstrated that the MDAsmartCD method enables the generation of compilable and executable smart contract code, ready for deployment on a blockchain platform. Full article

(This article belongs to the Special Issue Advances in Information System Analysis and Modeling (AISAM))

► Show Figures

Figure 1

20 pages, 2016 KiB

Open AccessArticle

Implementation of Demand Forecasting Module of ERP System in Mass Customization Industry—Case Studies

by Katarzyna Grobler-Dębska, Edyta Kucharska, Bartłomiej Żak, Jerzy Baranowski and Adam Domagała

Appl. Sci. 2022, 12(21), 11102; https://0-doi-org.brum.beds.ac.uk/10.3390/app122111102 - 02 Nov 2022

Cited by 2 | Viewed by 2364

Abstract

Mass customization production is a manufacturing process in which mass-produced products are modified according to specific customer needs. An example of such production is furniture production, where multiple options options for various components or features are available. Demand forecasting in mass customization industry is a difficult task for organizations and is both crucial for their profits and for market penetration. Unfortunately, the currently available tools are focused on more standard demand forecasts, which lack interpretable representation of information relevant for mass customization products. This is especially relevant for ERP systems, which are the backbone of modern manufacturing. We propose a forecasting method adapted to the needs of mass customization by focusing on demand prediction—not on finished products, but on key intermediate goods. Moreover, we provide a method for determining such goods. As an additional consequence we provide an implementation strategy that reduces the risk and time consumption when implementing forecasting in ERP systems. All the results are implemented as appropriate functionalities of a IFS Application ERP system. Full article

(This article belongs to the Special Issue Advances in Information System Analysis and Modeling (AISAM))

► Show Figures

Figure 1

27 pages, 42107 KiB

Open AccessArticle

Transforming BPMN Processes to SBVR Process Rules with Deontic Modalities

by Tomas Skersys, Paulius Danenas, Egle Mickeviciute and Rimantas Butleris

Appl. Sci. 2022, 12(18), 8976; https://0-doi-org.brum.beds.ac.uk/10.3390/app12188976 - 07 Sep 2022

Viewed by 1440

Abstract

The Object Management Group (OMG) has put considerable effort into the standardization of various business modeling aspects within the context of model-driven systems development. Indeed, the Business Process Model and Notation (BPMN) is now arguably the most popular process modeling language. At the same time, the Semantics of Business Vocabulary and Business Rules (SBVR), which is a novel and formally sound standard for the specification of virtually any kind of knowledge using controlled natural language, is also gaining its grounds. Nonetheless, the integration between these two very much related standards remains weak. In this paper, we present one such integration effort, namely an approach for the extraction of SBVR process rules from BPMN processes. To accomplish this, we utilized model-to-model transformation technology, which is one of the core features of Model-Driven Architecture. At the core of the presented solution stands a set of model transformation rules and two algorithms specifying the formation of formally defined process rules from process models. Basic implementation aspects, together with the source code of the solution, are also presented in the paper. The experimental results acquired from the automatic model transformation have shown full compliance with the benchmark results and cover the entirety of the specified flow of work defined in the experimental process models. Following this, it is safe to conclude that the set of specified transformation rules and algorithms was sufficient for the given scope of the experiment, providing a solid background for the practical application and future developments of the solution. Full article

(This article belongs to the Special Issue Advances in Information System Analysis and Modeling (AISAM))

► Show Figures

Figure 1

25 pages, 911 KiB

Open AccessArticle

Domain Model Based Design of Business Process Architectures

by Fernanda Gonzalez-Lopez, Guillermo Bustos, Jorge Munoz-Gama and Marcos Sepúlveda

Appl. Sci. 2022, 12(5), 2563; https://0-doi-org.brum.beds.ac.uk/10.3390/app12052563 - 01 Mar 2022

Cited by 3 | Viewed by 2690

Abstract

A business process architecture (BPA) model depicts business processes in an organization and their relations. An artifact for generating BPA models is proposed as the outcome of a design science research project. The proposed artifact consists of a method (i.e., a set of concepts, a proposed notation, and a detailed procedure), which is termed the domain-based BPA (dBPA) method due to using domain models as a starting point. The dBPA method tackles issues of currently available approaches: lack of structured inputs, limited consideration of process relations types, and restricted use of industry-standard modeling languages. The paper formalizes the dBPA method and illustrates its application in the manufacturing industry. Evaluation of the dBPA method revealed that practitioners perceived it as useful to achieve its goal with the benefits of being objective and clear and allowing to create complete and understandable BPA models that enable the integration of processes and the software that automates them. Full article

(This article belongs to the Special Issue Advances in Information System Analysis and Modeling (AISAM))

► Show Figures

Figure 1

19 pages, 2337 KiB

Open AccessArticle

Driver Behavior Classification System Analysis Using Machine Learning Methods

by Raymond Ghandour, Albert Jose Potams, Ilyes Boulkaibet, Bilel Neji and Zaher Al Barakeh

Appl. Sci. 2021, 11(22), 10562; https://0-doi-org.brum.beds.ac.uk/10.3390/app112210562 - 10 Nov 2021

Cited by 16 | Viewed by 4318

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 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))

► Show Figures

Figure 1

18 pages, 3978 KiB

Open AccessArticle

Smart Mobility and Aspects of Vehicle-to-Infrastructure: A Data Viewpoint

by Boris Bučko, Martin Michálek, Katarína Papierniková and Katarína Zábovská

Appl. Sci. 2021, 11(22), 10514; https://0-doi-org.brum.beds.ac.uk/10.3390/app112210514 - 09 Nov 2021

Cited by 10 | Viewed by 2874

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 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))

► Show Figures

Figure 1

12 pages, 712 KiB

Open AccessArticle

The EU-SENSE System for Chemical Hazards Detection, Identification, and Monitoring

by Małgorzata Gawlik-Kobylińska, Grzegorz Gudzbeler, Łukasz Szklarski, Norbert Kopp, Helge Koch-Eschweiler and Mariusz Urban

Appl. Sci. 2021, 11(21), 10308; https://0-doi-org.brum.beds.ac.uk/10.3390/app112110308 - 03 Nov 2021

Cited by 5 | Viewed by 2674

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 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))

► Show Figures

Figure 1

21 pages, 8020 KiB

Open AccessArticle

Towards an Ontology-Driven Information System for Archaeological Pottery Studies: The Greyware Experience

by Esther Travé Allepuz, Sonia Medina Gordo, Pablo del Fresno Bernal, Joan Vicens Tarré and Alfred Mauri Martí

Appl. Sci. 2021, 11(17), 7989; https://0-doi-org.brum.beds.ac.uk/10.3390/app11177989 - 29 Aug 2021

Cited by 3 | Viewed by 2194

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 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))

► Show Figures

Figure 1

17 pages, 16498 KiB

Open AccessArticle

Blockchain, Enterprise Resource Planning (ERP) and Accounting Information Systems (AIS): Research on e-Procurement and System Integration

by Alessio Faccia and Pythagoras Petratos

Appl. Sci. 2021, 11(15), 6792; https://0-doi-org.brum.beds.ac.uk/10.3390/app11156792 - 23 Jul 2021

Cited by 48 | Viewed by 12008

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 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))

► Show Figures

Figure 1

34 pages, 4163 KiB

Open AccessArticle

Genetic Algorithm for the Retailers’ Shelf Space Allocation Profit Maximization Problem

by Kateryna Czerniachowska, Karina Sachpazidu-Wójcicka, Piotr Sulikowski, Marcin Hernes and Artur Rot

Appl. Sci. 2021, 11(14), 6401; https://0-doi-org.brum.beds.ac.uk/10.3390/app11146401 - 11 Jul 2021

Cited by 6 | Viewed by 2826

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 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))

► Show Figures

Figure 1

16 pages, 5223 KiB

Open AccessArticle

Generating Block-Structured Parallel Process Models by Demonstration

by Julijana Lekić, Dragan Milićev and Dragan Stanković

Appl. Sci. 2021, 11(4), 1876; https://0-doi-org.brum.beds.ac.uk/10.3390/app11041876 - 20 Feb 2021

Cited by 1 | Viewed by 1511

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 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))

► Show Figures

Figure 1

Review

Jump to: Research

36 pages, 4842 KiB

Open AccessReview

On a Certain Research Gap in Big Data Mining for Customer Insights

by Maria Mach-Król and Bartłomiej Hadasik

Appl. Sci. 2021, 11(15), 6993; https://0-doi-org.brum.beds.ac.uk/10.3390/app11156993 - 29 Jul 2021

Cited by 14 | Viewed by 8182

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 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))

► Show Figures