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Information for Business and Management–Software Development for Data Processing and...
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Information for Business and Management–Software Development for Data Processing and Management, 2nd Edition

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4557

Special Issue Editor

Prof. Dr. Aneta Poniszewska-Maranda

E-Mail Website
Guest Editor
Institute of Information Technology, Lodz University of Technology, 90-924 Lodz, Poland
Interests: software engineering; information systems security; multi-agent-based systems; cloud computing; internet of things; mobile security; blockchain; data analysis; machine learning; data processing; distributed systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, data and information are among the most important resources in various aspects of our life and economy. Data and information are created, generated, collected, stored, and then processed and shared in various ways. All these activities are performed with the participation of contemporary software, applications, IT systems and their components.

Thus, in addition to creating the software itself, it is becoming increasingly crucial to manage the data and information that the software uses, processes and stores. Hence, not only are the software itself and the process of its development vital, but also information management at the appropriate level and the maintenance of a sufficiently high level of data protection, information and its flow.

The process of software development and information management are becoming increasingly interconnected and dependent, striving to develop and support a modern society based on knowledge and modern technologies.

Therefore, this Special Issue aims to exhibit various aspects of software creation and development, which are designed for the rapid, easy and secure processing and management of data and information.

The areas of interest for this Special Issue include the following topics: software analysis and design for the processing and management of data and information, software deployment for data processing, business analysis, business rules, requirements engineering, software development process, information management system, knowledge management solutions, software for security and privacy of data, software for data mining, and software for knowledge management.

Prof. Dr. Aneta Poniszewska-Maranda
Guest Editor

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. Information is an international peer-reviewed open access monthly 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.

Keywords

  • software engineering for data
  • requirements engineering for information management
  • data processing and management
  • business analysis
  • knowledge management
  • security and privacy of data
  • software for data mining

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Related Special Issue

Published Papers (3 papers)

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Research

22 pages, 6452 KiB  
Article
Consumer Satisfaction Benchmarking Analysis Using Group Decision Support System (GDSS) PROMETHEE Methodology in a GIS Environment
by Anastasia S. Saridou and Athanasios P. Vavatsikos
Information 2024, 15(11), 694; https://doi.org/10.3390/info15110694 - 3 Nov 2024
Viewed by 721
Abstract
In today’s competitive environment, multi-branch companies allocate their stores with the aim of expanding their territorial coverage to attract new customers and increase their market share. Consumer satisfaction surveys either produce global performance results or they are not able to differentiate consumer perceptions [...] Read more.
In today’s competitive environment, multi-branch companies allocate their stores with the aim of expanding their territorial coverage to attract new customers and increase their market share. Consumer satisfaction surveys either produce global performance results or they are not able to differentiate consumer perceptions using location analytics. This research develops a novel framework to assist multi-branch companies in mapping the consumer satisfaction performance of their stores, expanding conventional customer relationship management to the spatial context. The framework developed proposes a decision model that combines the Group Decision Support extension of the PROMETHEE and CRITIC methods in a GIS environment to generate satisfaction performance mappings. The developed decision-making framework converts consumer responses into satisfaction performance maps, allowing the company’s stores and their competitors to be evaluated. Moreover, it provides insight into the potential opportunities and threats for each store. The performance of the proposed framework is highlighted through a case study involving a multi-branch coffeehouse company in a Greek city. Finally, a tool developed to assist the computational part of the framework is presented. Full article
(This article belongs to the Special Issue Information for Business and Management–Software Development for Data Processing and Management, 2nd Edition)
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28 pages, 1806 KiB  
Article
Dynamic Workload Management System in the Public Sector
by Konstantinos C. Giotopoulos, Dimitrios Michalopoulos, Gerasimos Vonitsanos, Dimitris Papadopoulos, Ioanna Giannoukou and Spyros Sioutas
Information 2024, 15(6), 335; https://doi.org/10.3390/info15060335 - 6 Jun 2024
Viewed by 1134
Abstract
Workload management is a cornerstone of contemporary human resource management with widespread applications in private and public sectors. The challenges in human resource management are particularly pronounced within the public sector: particularly in task allocation. The absence of a standardized workload distribution method [...] Read more.
Workload management is a cornerstone of contemporary human resource management with widespread applications in private and public sectors. The challenges in human resource management are particularly pronounced within the public sector: particularly in task allocation. The absence of a standardized workload distribution method presents a significant challenge and results in unnecessary costs in terms of man-hours and financial resources expended on surplus human resource utilization. In the current research, we analyze how to deal with the “race condition” above and propose a dynamic workload management model based on the response time required to implement each task. Our model is trained and tested using comprehensive employee data comprising 450 records for training, 100 records for testing, and 88 records for validation. Approximately 11% of the initial data are deemed either inaccurate or invalid. The deployment of the ANFIS algorithm provides a quantified capability for each employee to handle tasks in the public sector. The proposed idea is deployed in a virtualized platform where each employee is implemented as an independent node with specific capabilities. An upper limit of work acceptance is proposed based on a documented study and laws that suggest work time frames in each public body, ensuring that no employee reaches the saturation level of exhaustion. In addition, a variant of the “slow start” model is incorporated as a hybrid congestion control mechanism with exceptional outcomes, offering a gradual execution window for each node under test and providing a smooth and controlled start-up phase for new connections. The ultimate goal is to identify and outline the entire structure of the Greek public sector along with the capabilities of its employees, thereby determining the organization’s executive capacity. Full article
(This article belongs to the Special Issue Information for Business and Management–Software Development for Data Processing and Management, 2nd Edition)
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15 pages, 1033 KiB  
Article
Two Lot-Sizing Algorithms for Minimizing Inventory Cost and Their Software Implementation
by Marios Arampatzis, Maria Pempetzoglou and Athanasios Tsadiras
Information 2024, 15(3), 167; https://doi.org/10.3390/info15030167 - 15 Mar 2024
Viewed by 2071
Abstract
Effective inventory management is crucial for businesses to balance minimizing holding costs while optimizing ordering strategies. Monthly or sporadic orders over time may lead to high ordering or holding costs, respectively. In this study, we introduce two novel algorithms designed to optimize ordering [...] Read more.
Effective inventory management is crucial for businesses to balance minimizing holding costs while optimizing ordering strategies. Monthly or sporadic orders over time may lead to high ordering or holding costs, respectively. In this study, we introduce two novel algorithms designed to optimize ordering replenishment quantities, minimizing total replenishment, and holding costs over a planning horizon for both partially loaded and fully loaded trucks. The novelty of the first algorithm is that it extends the classical Wagner–Whitin approach by incorporating various additional cost elements, stock retention considerations, and warehouse capacity constraints, making it more suitable for real-world problems. The second algorithm presented in this study is a variation of the first algorithm, with its contribution being that it incorporates the requirement of several suppliers to receive order quantities that regard only fully loaded trucks. These two algorithms are implemented in Python, creating the software tool called “Inventory Cost Minimizing tool” (ICM). This tool takes relevant data inputs and outputs optimal order timing and quantities, minimizing total costs. This research offers practical and novel solutions for businesses seeking to streamline their inventory management processes and reduce overall expenses. Full article
(This article belongs to the Special Issue Information for Business and Management–Software Development for Data Processing and Management, 2nd Edition)
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