基于数据驱动的多层网络服务生态系统模型构建方法
A Data-driven Approach for Constructing Multilayer Network-based Service Ecosystem Models
摘要：
无论是在互联网上还是在现实世界中，服务都在蓬勃发展。此外，服务之间的相互联系也变得更加紧密，以促进跨境业务协作，为客户创造和交付独特的新价值。各种服务生态系统已成为研究和实践的焦点。然而，由于缺乏广泛认可的服务生态系统模型和构建模型的充足数据，现有的服务生态系统研究局限于非常狭窄的范围，无法有效指导服务生态系统的设计、优化和演化。本文提出了一个基于多层网络的服务生态系统模型，该模型涵盖了各种与服务相关的要素，包括利益相关者、渠道、功能和非功能特征以及领域，特别是它们之间的结构和演化关系。引入“事件”来描述服务生态系统演化的触发因素。我们提出了一个数据驱动的方法来构建MSEM从公共媒体新闻和外部数据源。与现有模型的定性比较表明，MSEM对服务生态系统中的细粒度元素/关系具有更高的覆盖度，语义更丰富，可解释性更高。在真实新闻语料库上的实验表明，与其他方法相比，该方法能够以更低的成本和更高的效率为真实世界的服务生态系统构建大规模模型。
英文摘要：
Services are flourishing drastically both on the Internet and in the real world. Additionally, services have become much more interconnected to facilitate transboundary business collaboration to create and deliver distinct new values to customers. Various service ecosystems have become a focus in both research and practice. However, due to the lack of widely recognized service ecosystem models and sufficient data for constructing such models, existing studies on service ecosystems are limited to very narrow scope and cannot effectively guide the design, optimization, and evolution of service ecosystems. We propose a Multilayer network-based Service Ecosystem Model, which covers a variety of service-related elements, including stakeholders, channels, functional and nonfunctional features, and domains, and especially, structural and evolutionary relations between them. "Events" are introduced to describe the triggers of service ecosystem evolution. We propose a data-driven approach for constructing MSEM from public media news and external data sources. Qualitative comparison with state-of-the-art models shows that MSEM has a higher coverage degree of fine-grained elements/relations in service ecosystems and richer semantics for higher interpretability. Experiments conducted on real news corpora show that compared with other approaches, our approach can construct large-scale models for real-world service ecosystems with lower cost and higher efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
