Paper Info
Title
Database-support for continuous prediction queries over streaming data
Abstract
Prediction is emerging as an essential ingredient for real-time monitoring, planning and decision support applications such as intrusion detection, e-commerce pricing and automated resource management. This paper presents a system that efficiently supports continuous prediction queries (CPQs) over streaming data using seamlessly-integrated probabilistic models. Specifically, we describe how to execute and optimize CPQs using discrete (Dynamic) Bayesian Networks as the underlying predictive model. Our primary contribution is a novel cost-based optimization framework that employs materialization, sharing, and model-specific optimization techniques to enable highly-efficient point- and range-based CPQ execution. Furthermore, we support efficient execution of top-k and threshold-based high probability queries. We characterize the behavior of our system and demonstrate significant performance gains using a prototype implementation operating on real-world network intrusion data and deployed as part of a real-time software-performance monitoring system.
Year
DOI
Venue
2010
10.14778/1920841.1921000
PVLDB
Keywords
Field
DocType
real-time monitoring,optimize cpqs,optimization framework,real-time software-performance monitoring system,model-specific optimization technique,decision support application,efficient execution,range-based cpq execution,continuous prediction query,intrusion detection,decision support,probabilistic model,software performance,model specification,dynamic bayesian network,prediction model,real time,e commerce
Resource management,Data mining,Intrusion,Monitoring system,Computer science,Decision support system,Bayesian network,Streaming data,Probabilistic logic,Intrusion detection system,Database
Journal
Volume
Issue
ISSN
3
1-2
2150-8097
Citations 
PageRank 
References 
7
0.59
15
Authors
3
Name
Order
Citations
PageRank
Mert Akdere121510.45
Uǧur Çetintemel252522.43
Eli Upfal34310743.13