With the rapid advancement of information technology, the open source paradigm has become popular in multiple domains, including geographic information system (GIS). Developing an open, collaborative, and sustainable open source GIS ecosystem can promote GIS technology innovation, lower implementation costs, and foster development within the field. This study systematically investigates methods for developing an open source GIS ecosystem and its future trends, addressing four main aspects: ① reviewing the development history of open source GIS and the current technological landscape to refine a four-stage evolutionary framework; ② from a GIS perspective and based on the existing open source foundation, proposing a multi-layered ecosystem construction model specifically tailored to GIS; ③ introducing HyperCRX to perform quantitative analysis and visualization of four metrics—OpenRank, Activity, Contributors, and Participants—for eight representative open source GIS projects, thereby revealing differences in their influence, activity levels, and community engagement to reflect the current state of the ecosystem; and ④ summarizing the challenges faced by the open source GIS ecosystem in terms of public perception, talent cultivation, governance mechanisms, data–software coordination, and sustainable business models, as well as outlining future development directions and research hotspots in the era of large-scale models. It is hoped that this study will provide useful references for future research and practical applications.
Collaborative relationships among open-source projects are becoming increasingly complex, involving multiple reuse mechanisms such as dependency co-usage, language consistency, and contributor overlap. Traditional graph models struggle to represent these heterogeneous structures in a unified manner, limiting their ability to identify potential collaboration links. This paper proposes an analytical framework that integrates multilayer graph modeling with structure-aware link prediction, tailored to open-source ecosystems. A three-layer unweighted graph is constructed to capture different types of collaborations, and two structural enhancements—layer overlap modulation and community-aware scoring—are introduced to improve structural perception and semantic interpretability. Experimental results on multiple real-world datasets show that the proposed method consistently outperforms mainstream link prediction algorithms, particularly in networks with high structural heterogeneity. Further analysis reveals that the predicted links exhibit strong community consistency and semantic recoverability. Overall, the proposed approach effectively uncovers latent collaboration paths among open-source projects and provides structural support for reuse modeling and community evolution analysis.
This study systematically analyzes the evolutionary characteristics and research hotspots of open- source innovation in China. A dataset comprising 732 valid journal articles, with “open-source” in the title, was retrieved from the China National Knowledge Infrastructure (CNKI) for the period 2005–2024. A bibliometric approach was employed to examine such dimensions as annual publication volume, disciplinary distribution, keyword co-occurrence and clustering, burst keywords, and timeline evolution. The results indicate that research in this field has progressed through three stages, initial exploration, steady development, and rapid growth, with a significant surge in publications over the past five years. Disciplinary distribution analysis reveals a multidisciplinary landscape centered on library and information science, computer science, and industrial technology, which extends to fields such as education, management, and law. Keyword clustering analysis identifies nine core research areas, accompanied by a review of the representative literature within each cluster. Timeline evolution analysis suggests that future research will likely focus on the deep integration of artificial intelligence (AI) and open-source ecosystems, the evolution of collaboration and governance models in open-source communities, open-source software security and supply chain risk identification, and open-source law and intellectual property protection. On the basis of these findings, we propose several recommendations to foster the sustainable development of open-source innovation in China, including strengthening the synergistic governance of AI and open-source ecosystems, enhancing supply chain security systems, advancing innovations in legal and licensing frameworks, and constructing a digital open-source infrastructure oriented toward industrial and public services.
In the context of identifying GitHub’s long-term active, high-star repositories—critical for assisting the development of robust open-source communities and vital digital infrastructure—we propose a novel method for evaluating the long-term activity of these repositories. This method is firmly based on a time series prediction model, which excels in forecasting repository activity metrics rather than being specifically designed for this purpose. A key innovation of our method is the first-time use of the developer activity cycle as a pivotal feature. This improves the accuracy of predictions for repository development trends and provides a more nuanced understanding of project evolution. After meticulously modeling and mining the time series data of various activity indicators, we developed a new activity calculation formula: development trend-based activity (DTA). This formula allows a precise quantitative evaluation of a repository's true activity level. To rigorously validate our methodology, we designed and curated a comprehensive benchmark dataset with fine time granularity and broad coverage. Subsequently, we systematically evaluated the performance of multiple prediction models against this dataset, eventually identifying the best model for forecasting open-source repository activity. The experimental results conclusively demonstrate the effectiveness of our proposed method in accurately predicting the long-term activity of repositories. Consequently, using DTA to evaluate repository activity can enable open-source participants to effectively identify repositories poised for long-term engagement, strategically determine their participation focus, and thereby significantly promote the sustained development of open-source communities and critical digital infrastructure.
The open source ecosystem, as a critical component of the modern software industry, has garnered increasing attention from both academia and industry regarding its evaluation challenges. However, existing evaluation methods face issues such as inconsistent evaluation standards, lack of theoretical grounding, and poor comparability of evaluation results. Guided by foundational theories of evaluatology, this study introduced a novel interdisciplinary research domain, open source evaluatology, for the first time. It established a theoretical framework and methodological system for evaluating the open source ecosystem. The primary contributions of this paper include the following. Developing the theoretical foundation of open source evaluatology based on the five axioms of evaluatology and defining fundamental concepts, evaluation dimensions, and standards for open source ecosystem evaluation. Designing an evaluation conditions framework comprising five levels: problem definition, task instances, algorithm mechanisms, implementation examples, and supporting systems. A hybrid evaluation model combining statistical and network metrics was proposed. Based on the experiments conducted using the GitHub dataset, this study validated the proposed method from three dimensions: open source repositories, developers, and communities. The results demonstrated that the proposed evaluation model exhibited strong applicability and explanatory power in open source scenarios.
In the context of the new industrial revolution, with increasing computational complexity and intensified demand for customization, open source hardware has emerged as a key approach to overcome the limitations of closed architectures and enhance technological autonomy. This study focused on the Reduced Instruction Set Computer-Five（RISC-Ⅴ）open instruction set architecture and systematically reviewed its ecosystem advantages and industrial value. It compared major domestic and international open source projects in terms of design openness, system flexibility, and collaborative innovation mechanisms. From a temporal perspective, this study analyzed the development trend of open source hardware, evolving from low-level architectural innovation to heterogeneous integration and scenario-based expansion. The findings indicated that open source hardware had broad application prospects in critical areas such as intelligent manufacturing, edge computing, and immersive terminals. This could significantly improve computational efficiency and reduce development complexity and system costs. Open source hardware drives the chip design from a closed paradigm toward a shared model, offering new support for industrial intelligence upgrades and strategic technology security.
An open source ecosystem abounds with valuable data, yet extracting insights requires innovative data infrastructure and analytical methods. To address this, OSS Insight was developed that innovatively used the hybrid transactional analytical processing(HTAP) database for efficient storage and query of billions of GitHub event data and offered real-time exploration via a visual interface. It delved into spatiotemporal data analysis, modeling developer behaviors and ecosystem evolution, such as visualizing global contribution patterns. Integrated with large language models(LLMs), it enabled natural language to structured query language(SQL) conversion for intelligent querying. A case study of Kubernetes showcased its capabilities in analyzing developers, project evolution, and organizational collaboration. Experiments proved that OSS Insight efficiently analyzed large-scale open source data, and its LLM-driven interaction simplified data analysis and provided automated insights.
中国综合性科技类核心期刊(北大核心)
