Pub Date : 2024-11-04DOI: 10.1016/j.softx.2024.101938
Sylwester Czmil, Jacek Kluska, Anna Czmil
We present the first major release of the Classification Algorithms Comparison Pipeline (CACP). The proposed software enables one to compare newly developed classification algorithms in Python with other classifiers to evaluate classification performance and ensure both outcomes’ reproducibility and statistical reliability. CACP simplifies and accelerates the entire classifier evaluation process considerably and helps prepare the professional documentation of the experiments conducted. The upgrade introduces enhancements to existing tools and adds new features: (1) - support for River machine learning library datasets in incremental learning, (2) - capability to include user-defined datasets, (3) - use of River classifiers for incremental learning, (4) - use of River metrics for incremental learning, (5) - flexibility to create user-defined metrics, (6) - record-by-record testing for incremental learning, (7) - enhanced summary of incremental testing results with dynamic visualization of the learning process, (8) - Graphical User Interface (GUI).
我们推出了分类算法比较管道（CACP）的第一个重要版本。通过该软件，人们可以将新开发的 Python 分类算法与其他分类器进行比较，以评估分类性能，确保结果的可重复性和统计可靠性。CACP 可大大简化和加快整个分类器评估过程，并有助于准备所进行实验的专业文档。此次升级对现有工具进行了改进，并增加了新功能：(1) - 在增量学习中支持 River 机器学习库数据集，(2) - 能够包含用户定义的数据集，(3) - 在增量学习中使用 River 分类器，(4) - 在增量学习中使用 River 指标，(5) - 灵活创建用户定义的指标，(6) - 在增量学习中逐条记录测试，(7) - 增强的增量测试结果汇总，学习过程动态可视化，(8) - 图形用户界面 (GUI)。
{"title":"Version [1.0.3] — [CACP: Classification Algorithms Comparison Pipeline]","authors":"Sylwester Czmil, Jacek Kluska, Anna Czmil","doi":"10.1016/j.softx.2024.101938","DOIUrl":"10.1016/j.softx.2024.101938","url":null,"abstract":"<div><div>We present the first major release of the Classification Algorithms Comparison Pipeline (CACP). The proposed software enables one to compare newly developed classification algorithms in Python with other classifiers to evaluate classification performance and ensure both outcomes’ reproducibility and statistical reliability. CACP simplifies and accelerates the entire classifier evaluation process considerably and helps prepare the professional documentation of the experiments conducted. The upgrade introduces enhancements to existing tools and adds new features: (1) - support for River machine learning library datasets in incremental learning, (2) - capability to include user-defined datasets, (3) - use of River classifiers for incremental learning, (4) - use of River metrics for incremental learning, (5) - flexibility to create user-defined metrics, (6) - record-by-record testing for incremental learning, (7) - enhanced summary of incremental testing results with dynamic visualization of the learning process, (8) - Graphical User Interface (GUI).</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101938"},"PeriodicalIF":2.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-04DOI: 10.1016/j.softx.2024.101952
Gabriel Spadon , Jay Kumar , Jinkun Chen , Matthew Smith , Casey Hilliard , Sarah Vela , Romina Gehrmann , Claudio DiBacco , Stan Matwin , Ronald Pelot
Efficiently handling Automatic Identification System (AIS) data is vital for enhancing maritime safety and navigation, yet is hindered by the system’s high volume and error-prone datasets. This paper introduces the Automatic Identification System Database (AISdb), a novel tool designed to address the challenges of processing and analyzing AIS data. AISdb is a comprehensive, open-source platform that enables the integration of AIS data with environmental datasets, thus enriching analyses of vessel movements and their environmental impacts. By facilitating AIS data collection, cleaning, and spatio-temporal querying, AISdb significantly advances AIS data research. Utilizing AIS data from various sources, AISdb demonstrates improved handling and analysis of vessel information, contributing to enhancing maritime safety, security, and environmental sustainability efforts.
{"title":"Maritime tracking data analysis and integration with AISdb","authors":"Gabriel Spadon , Jay Kumar , Jinkun Chen , Matthew Smith , Casey Hilliard , Sarah Vela , Romina Gehrmann , Claudio DiBacco , Stan Matwin , Ronald Pelot","doi":"10.1016/j.softx.2024.101952","DOIUrl":"10.1016/j.softx.2024.101952","url":null,"abstract":"<div><div>Efficiently handling Automatic Identification System (AIS) data is vital for enhancing maritime safety and navigation, yet is hindered by the system’s high volume and error-prone datasets. This paper introduces the Automatic Identification System Database (AISdb), a novel tool designed to address the challenges of processing and analyzing AIS data. AISdb is a comprehensive, open-source platform that enables the integration of AIS data with environmental datasets, thus enriching analyses of vessel movements and their environmental impacts. By facilitating AIS data collection, cleaning, and spatio-temporal querying, AISdb significantly advances AIS data research. Utilizing AIS data from various sources, AISdb demonstrates improved handling and analysis of vessel information, contributing to enhancing maritime safety, security, and environmental sustainability efforts.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101952"},"PeriodicalIF":2.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-04DOI: 10.1016/j.softx.2024.101939
María Asunción Padilla-Rascón , Pedro González , Cristóbal J. Carmona
SDRDPy is a desktop application that allows experts an intuitive graphic and tabular representation of the knowledge extracted by any supervised descriptive rule discovery algorithm. The application is able to provide an analysis of the data showing the relevant information of the data set and the relationship between the rules, data and the quality measures associated for each rule regardless of the tool where algorithm has been executed. All of the information is presented in a user-friendly application in order to facilitate expert analysis and also the exportation of reports in different formats.
{"title":"SDRDPy: An application to graphically visualize the knowledge obtained with supervised descriptive rule algorithms","authors":"María Asunción Padilla-Rascón , Pedro González , Cristóbal J. Carmona","doi":"10.1016/j.softx.2024.101939","DOIUrl":"10.1016/j.softx.2024.101939","url":null,"abstract":"<div><div>SDRDPy is a desktop application that allows experts an intuitive graphic and tabular representation of the knowledge extracted by any supervised descriptive rule discovery algorithm. The application is able to provide an analysis of the data showing the relevant information of the data set and the relationship between the rules, data and the quality measures associated for each rule regardless of the tool where algorithm has been executed. All of the information is presented in a user-friendly application in order to facilitate expert analysis and also the exportation of reports in different formats.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101939"},"PeriodicalIF":2.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-04DOI: 10.1016/j.softx.2024.101956
Steve Yuwono, Detlef Arend, Andreas Schwung
Game theory, a fundamental aspect of mathematical economics and strategic decision-making, has been increasingly applied to various fields, including economics, biology, computer science, and engineering. Despite its growing importance, there is a significant lack of flexible and user-friendly tools for standardized modeling of them, particularly for real-world applications. Hence, we developed MLPro-GT as part of our open-source MLPro project, which offers modular and standardized yet flexible components, extensive documentation, and a variety of examples. MLPro-GT allows researchers and practitioners to easily incorporate game theory into their applications while lowering the entry barrier for students. This makes individual work more reproducible, shareable, and reusable.
{"title":"MLPro-GT-game theory and dynamic games modeling in Python","authors":"Steve Yuwono, Detlef Arend, Andreas Schwung","doi":"10.1016/j.softx.2024.101956","DOIUrl":"10.1016/j.softx.2024.101956","url":null,"abstract":"<div><div>Game theory, a fundamental aspect of mathematical economics and strategic decision-making, has been increasingly applied to various fields, including economics, biology, computer science, and engineering. Despite its growing importance, there is a significant lack of flexible and user-friendly tools for standardized modeling of them, particularly for real-world applications. Hence, we developed MLPro-GT as part of our open-source MLPro project, which offers modular and standardized yet flexible components, extensive documentation, and a variety of examples. MLPro-GT allows researchers and practitioners to easily incorporate game theory into their applications while lowering the entry barrier for students. This makes individual work more reproducible, shareable, and reusable.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101956"},"PeriodicalIF":2.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces the Computational Fluid Dynamics High-Order Writer Library (CFD-HOWL), which performs I/O operations on solution data from high-order CFD simulations. The library centers around the novel mesh upgrade algorithm, which is coupled with the functionalities of the CGNS file system. Even though the library is in Python, users can readily apply this library as-is by integrating APIs that read their specific data formats. The flexible nature of the library allows for straightforward integration with existing CFD solvers. We integrate CFD-HOWL with the in-house COOLFluiD CFD solver and demonstrate a reduction in I/O operation times. The results show that the CFD-HOWL consistently outperforms other conventional writers, thus addressing a key bottleneck with the higher-order CFD data post-processing.
{"title":"Input/Output Library for Higher-Order Computational Fluid Dynamics Data","authors":"Rayan Dhib , Vatsalya Sharma , Andrea Lani , Stefaan Poedts","doi":"10.1016/j.softx.2024.101943","DOIUrl":"10.1016/j.softx.2024.101943","url":null,"abstract":"<div><div>This paper introduces the Computational Fluid Dynamics High-Order Writer Library (CFD-HOWL), which performs I/O operations on solution data from high-order CFD simulations. The library centers around the novel mesh upgrade algorithm, which is coupled with the functionalities of the CGNS file system. Even though the library is in Python, users can readily apply this library <em>as-is</em> by integrating APIs that read their specific data formats. The flexible nature of the library allows for straightforward integration with existing CFD solvers. We integrate CFD-HOWL with the in-house COOLFluiD CFD solver and demonstrate a reduction in I/O operation times. The results show that the CFD-HOWL consistently outperforms other conventional writers, thus addressing a key bottleneck with the higher-order CFD data post-processing.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101943"},"PeriodicalIF":2.4,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.softx.2024.101951
Bei Zhou , Klas Markström , Søren Riis
In this paper we introduce CDL, a software library designed for the analysis of permutations and linear orders subject to various structural restrictions. Prominent examples of these restrictions include pattern avoidance, a topic of interest in both computer science and combinatorics, and never conditions, utilized in social choice and voting theory. CDL offers a range of fundamental functionalities, including identifying the permutations that meet specific restrictions and determining the isomorphism of such sets. To facilitate the exploration of large permutation sets or domains, CDL incorporates multiple search strategies and heuristics.
{"title":"CDL: A fast and flexible library for the study of permutation sets with structural restrictions","authors":"Bei Zhou , Klas Markström , Søren Riis","doi":"10.1016/j.softx.2024.101951","DOIUrl":"10.1016/j.softx.2024.101951","url":null,"abstract":"<div><div>In this paper we introduce CDL, a software library designed for the analysis of permutations and linear orders subject to various structural restrictions. Prominent examples of these restrictions include pattern avoidance, a topic of interest in both computer science and combinatorics, and never conditions, utilized in social choice and voting theory. CDL offers a range of fundamental functionalities, including identifying the permutations that meet specific restrictions and determining the isomorphism of such sets. To facilitate the exploration of large permutation sets or domains, CDL incorporates multiple search strategies and heuristics.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101951"},"PeriodicalIF":2.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1016/j.softx.2024.101950
Damian Frąszczak, Edyta Frąszczak
NSDLib, short for Network Source Detection Library, is an advanced package designed to detect the sources of propagation in networks. It is easy to integrate and offers a range of algorithms for source detection, including evaluating node importance, identifying outbreaks, and reconstructing propagation graphs. This library serves as a comprehensive repository, promoting collaboration among researchers and developers worldwide to combat disinformation warfare. By enabling the implementation and comparison of new techniques, NSDLib aims to enhance the understanding and mitigation of misinformation and improve propagation analysis. This paper provides an overview of NSDLib's capabilities, emphasizing its role in bridging the gap between theoretical research and practical application.
{"title":"NSDLib: A comprehensive python library for network source detection and evaluation","authors":"Damian Frąszczak, Edyta Frąszczak","doi":"10.1016/j.softx.2024.101950","DOIUrl":"10.1016/j.softx.2024.101950","url":null,"abstract":"<div><div>NSDLib, short for Network Source Detection Library, is an advanced package designed to detect the sources of propagation in networks. It is easy to integrate and offers a range of algorithms for source detection, including evaluating node importance, identifying outbreaks, and reconstructing propagation graphs. This library serves as a comprehensive repository, promoting collaboration among researchers and developers worldwide to combat disinformation warfare. By enabling the implementation and comparison of new techniques, NSDLib aims to enhance the understanding and mitigation of misinformation and improve propagation analysis. This paper provides an overview of NSDLib's capabilities, emphasizing its role in bridging the gap between theoretical research and practical application.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101950"},"PeriodicalIF":2.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-31DOI: 10.1016/j.softx.2024.101958
Yuan-Ze Tang , Xian-Cheng Zhang , Hang-Hang Gu , Chang-Qi Hong , Shan-Tung Tu , Run-Zi Wang
Probabilistic reliability assessment is an important part of life management for critical equipment, but it can be costly due to the need for extensive data. To further implement reliability assessment in engineering, it is essential to reduce both economic costs and the learning curve for engineers. This paper presents a surrogate model-based probabilistic reliability assessment plug-in for ABAQUS that does not depend on any third-party software. The plug-in can automatically perform stochastic finite element method considering multiple uncertainty sources including material, geometry, and load. By using the data obtained from FEM, the plug-in trains the surrogate model and completes the reliability assessment and visualization. This paper illustrates the theoretical basis, design concepts, and functionalities of the plug-in, along with an example demonstrating its effectiveness and efficiency. The free plug-in serves as a valuable tool for engineers, facilitating easy and efficient reliability assessments.
{"title":"CFre: An ABAQUS plug-in for creep-fatigue reliability assessment considering multiple uncertainty sources","authors":"Yuan-Ze Tang , Xian-Cheng Zhang , Hang-Hang Gu , Chang-Qi Hong , Shan-Tung Tu , Run-Zi Wang","doi":"10.1016/j.softx.2024.101958","DOIUrl":"10.1016/j.softx.2024.101958","url":null,"abstract":"<div><div>Probabilistic reliability assessment is an important part of life management for critical equipment, but it can be costly due to the need for extensive data. To further implement reliability assessment in engineering, it is essential to reduce both economic costs and the learning curve for engineers. This paper presents a surrogate model-based probabilistic reliability assessment plug-in for ABAQUS that does not depend on any third-party software. The plug-in can automatically perform stochastic finite element method considering multiple uncertainty sources including material, geometry, and load. By using the data obtained from FEM, the plug-in trains the surrogate model and completes the reliability assessment and visualization. This paper illustrates the theoretical basis, design concepts, and functionalities of the plug-in, along with an example demonstrating its effectiveness and efficiency. The free plug-in serves as a valuable tool for engineers, facilitating easy and efficient reliability assessments.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101958"},"PeriodicalIF":2.4,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142560893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1016/j.softx.2024.101944
Jakub Adamczyk, Piotr Ludynia
In this work, we present scikit-fingerprints, a Python package for computation of molecular fingerprints for applications in chemoinformatics. Our library offers an industry-standard scikit-learn interface, allowing intuitive usage and easy integration with machine learning pipelines. It is also highly optimized, featuring parallel computation that enables efficient processing of large molecular datasets. Currently, scikit-fingerprints stands as the most feature-rich library in the open source Python ecosystem, offering over 30 molecular fingerprints. Our library simplifies chemoinformatics tasks based on molecular fingerprints, including molecular property prediction and virtual screening. It is also flexible, highly efficient, and fully open source.
{"title":"Scikit-fingerprints: Easy and efficient computation of molecular fingerprints in Python","authors":"Jakub Adamczyk, Piotr Ludynia","doi":"10.1016/j.softx.2024.101944","DOIUrl":"10.1016/j.softx.2024.101944","url":null,"abstract":"<div><div>In this work, we present <em>scikit-fingerprints</em>, a Python package for computation of molecular fingerprints for applications in chemoinformatics. Our library offers an industry-standard scikit-learn interface, allowing intuitive usage and easy integration with machine learning pipelines. It is also highly optimized, featuring parallel computation that enables efficient processing of large molecular datasets. Currently, <em>scikit-fingerprints</em> stands as the most feature-rich library in the open source Python ecosystem, offering over 30 molecular fingerprints. Our library simplifies chemoinformatics tasks based on molecular fingerprints, including molecular property prediction and virtual screening. It is also flexible, highly efficient, and fully open source.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101944"},"PeriodicalIF":2.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1016/j.softx.2024.101942
Victor Lamas, David de Castro, Alejandro Cortiñas, Miguel R. Luaces
Geographic Information Systems (GIS) are complex systems that store, organize, process, and present geographically referenced data. Developing GIS requires specialized knowledge of algorithms, data structures, and geospatial concepts, along with the ability to implement scalable and efficient solutions for managing massive volumes of spatial data from various sources and providing user-friendly interfaces. This article introduces GIS-Publisher, a tool built using the Software Product Line (SPL) approach, which is a method of systematically creating a family of software products from shared core assets managing similarities and controlling variability. With GIS-Publisher, users without software development expertise can quickly and easily create web applications from directories containing shapefiles, a popular format for geographic data. The tool automates system deployment across various environments, including local computers, Secure Shell (SSH) remote servers, and Amazon Web Services (AWS) instances. Additionally, GIS-Publisher enables users to specify different styles using Styled Layer Descriptions (SLDs) for each shapefile, providing complete control over the visual representation of geographic data. This study details the features, benefits, and implementation of GIS-Publisher, demonstrating how it can accelerate GIS development and deployment.
{"title":"GIS-Publisher: Simplifying web-based GIS application development for enhanced data dissemination","authors":"Victor Lamas, David de Castro, Alejandro Cortiñas, Miguel R. Luaces","doi":"10.1016/j.softx.2024.101942","DOIUrl":"10.1016/j.softx.2024.101942","url":null,"abstract":"<div><div>Geographic Information Systems (GIS) are complex systems that store, organize, process, and present geographically referenced data. Developing GIS requires specialized knowledge of algorithms, data structures, and geospatial concepts, along with the ability to implement scalable and efficient solutions for managing massive volumes of spatial data from various sources and providing user-friendly interfaces. This article introduces GIS-Publisher, a tool built using the Software Product Line (SPL) approach, which is a method of systematically creating a family of software products from shared core assets managing similarities and controlling variability. With GIS-Publisher, users without software development expertise can quickly and easily create web applications from directories containing <em>shapefiles</em>, a popular format for geographic data. The tool automates system deployment across various environments, including local computers, Secure Shell (SSH) remote servers, and Amazon Web Services (AWS) instances. Additionally, GIS-Publisher enables users to specify different styles using Styled Layer Descriptions (SLDs) for each shapefile, providing complete control over the visual representation of geographic data. This study details the features, benefits, and implementation of GIS-Publisher, demonstrating how it can accelerate GIS development and deployment.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101942"},"PeriodicalIF":2.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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