Paper Info
Title
Optimal Event Monitoring through Internet Mashup over Multivariate Time Series
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.
Year
DOI
Venue
2013
10.4018/jdsst.2013040104
IJDSST
Keywords
Field
DocType
optimal event monitoring,multivariate time series,particular time point,optimal decision parameter,model evaluation,decision recommendation,general class,model definition,internet mashup,general-hybrid-based model,parameter learning,mtsa data model
Event monitoring,Data mining,Mashup,Query language,Optimal decision,Computer science,Decision support system,Artificial intelligence,Analytics,Data model,Machine learning,The Internet
Journal
Volume
Issue
ISSN
5
2
1941-6296
Citations 
PageRank 
References 
0
0.34
7
Authors
2
Name
Order
Citations
PageRank
Chun-Kit Ngan143.33
Alexander Brodsky251092.99