Optimal Event Monitoring through Internet Mashup over Multivariate Time Series

  • Authors:

  • Chun-Kit Ngan;Alexander Brodsky

  • Affiliations:

  • Department of Computer Science, George Mason University, Fairfax, VA, USA;Department of Computer Science, George Mason University, Fairfax, VA, USA

  • Venue:
  • International Journal of Decision Support System Technology
  • Year:
  • 2013

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Abstract

The authors 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, the authors 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. The authors 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, the authors conduct an experimental case study for a university campus microgrid as a practical example to demonstrate our proposed framework, models, and language.