Optimal Event Monitoring through Internet Mashup over Multivariate Time Series

TL;DR

This paper introduces a comprehensive Web-Mashup Framework for multivariate time series analytics that integrates domain knowledge and formal learning, enabling effective model management, data monitoring, and decision support, demonstrated through a university microgrid case study.

Contribution

It presents a novel hybrid model and extended query language for multivariate time series analysis, supporting constraints and decision-making in a unified framework.

Findings

Framework successfully supports model definition, querying, and monitoring.

Experimental case study validates the framework's practical effectiveness.

Model accommodates multiple constraints for domain-specific applications.

Abstract

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.