论文标题
通过Internet Mashup在多元时间序列上通过Internet Mashup进行最佳活动监视
Optimal Event Monitoring through Internet Mashup over Multivariate Time Series
论文作者
论文摘要
我们为多元时间序列分析（MTSA）提出了一个Web-Mashup应用程序服务框架，该框架支持模型定义，查询，参数学习，模型评估，数据监视，决策建议和Web门户的服务。该框架保持了结合基于域知识和基于正式学习的方法的优势的优势，并设计用于多元时间序列的更一般的问题类别。更具体地说，我们确定了一个基于一般的Hybrid的模型MTSA参数估计，以解决此类问题，其中将目标函数从最佳决策参数中最大化或最小化，无论特定时间点如何。该模型还允许域专家包括多种类型的约束，例如全局约束和监视约束。我们进一步扩展了MTSA数据模型和查询语言，以支持此类的学习，监视和建议服务。最后，我们为大学校园微电网进行了实验案例研究，以证明我们提出的框架，模型和语言。
We propose a Web-Mashup Application Service Framework for Multivariate Time Series Analytics (MTSA) that supports the services of model definitions, querying, parameter learning, model evaluations, data monitoring, decision recommendations, and web portals. This framework maintains the advantage of combining the strengths of both the domain-knowledge-based and the formal-learning-based approaches and is designed for a more general class of problems over multivariate time series. More specifically, we identify a general-hybrid-based model, MTSA-Parameter Estimation, to solve this class of problems in which the objective function is maximized or minimized from the optimal decision parameters regardless of particular time points. This model also allows domain experts to include multiple types of constraints, e.g., global constraints and monitoring constraints. We further extend the MTSA data model and query language to support this class of problems for the services of learning, monitoring, and recommendation. At the end, we conduct an experimental case study for a university campus microgrid as a practical example to demonstrate our proposed framework, models, and language.
