发表机构
School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University（科廷大学电气、计算和数学科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对协作式AIaaS组合缺乏专用数据集的问题，构建了包含25,900个服务和10,000个请求的数据集，并提出基于多臂老虎机的组合算法，支持服务推荐、选择和QoS预测研究。
AI 中文摘要
人工智能即服务（AIaaS）组合是一个新兴的研究领域，使客户能够组合多种人工智能服务以满足复杂需求。该范式的一个近期扩展是协作式AIaaS组合，其中多种人工智能服务被组合以创建统一解决方案。该领域的研究需要包含服务描述、组合请求和相应组合解决方案的数据集。然而，目前没有数据集专门包含协作式AIaaS组合的所有这些信息。现有数据集针对传统Web服务或顺序AI工作流，缺乏现实协作组合所需的AI特定属性。为解决这些挑战，我们提出了一个协作式AIaaS组合数据集，包含来自12个AI任务家族的多个提供商的25,900个AIaaS服务，以及10,000个协作服务请求。我们进一步开发了一种基于多臂老虎机（MAB）的协作组合算法，该算法在组合之前确定候选服务组合的可组合性。实验结果表明，该数据集支持现实的协作式AIaaS组合评估以及服务推荐、选择和QoS预测方面的相关研究。数据集和实现可在以下网址公开获取：this https URL
英文摘要
Artificial Intelligence as a Service (AIaaS) composition is an emerging research area that enables clients to combine multiple AI services to meet complex requirements. A recent extension of this paradigm is collaborative AIaaS composition, where multiple AI services are combined to create a unified solution. Research in this field requires datasets with service descriptions, composition requests, and corresponding composition solutions. However, no dataset contains all this information specifically for collaborative AIaaS composition. Existing datasets target traditional web services or sequential AI workflows and lack the AI-specific attributes required for realistic collaborative composition. To address these challenges, we present a collaborative AIaaS composition dataset containing 25,900 AIaaS services from multiple providers across 12 AI task families, along with 10,000 collaborative service requests. We further develop a Multi-Armed Bandit (MAB)-based collaborative composition algorithm that determines the composability of candidate service combinations prior to composition. Experimental results demonstrate that the dataset supports realistic collaborative AIaaS composition evaluation and related research in service recommendation, selection and QoS prediction. The dataset and implementation are publicly available at: https://github.com/deepakkanneganti9/CAIaaS
