Offering a Precision-Performance Tradeoff for Aggregation Queries over Replicated Data
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Modeling Uncertainties in Publish/Subscribe Systems
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Processing forecasting queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Value-based notification conditions in large-scale publish/subscribe systems?
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Algorithms for distributed functional monitoring
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
A skip-list approach for efficiently processing forecasting queries
Proceedings of the VLDB Endowment
Multi-dimensional online tracking
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
An adaptive updating protocol for reducing moving object database workload
Proceedings of the VLDB Endowment
F2DB: The Flash-Forward Database System
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Data management in the MIRABEL smart grid system
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Active and accelerated learning of cost models for optimizing scientific applications
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Daisy: the center for data-intensive systems at Aalborg University
ACM SIGMOD Record
Visualizing complex energy planning objects with inherent flexibilities
Proceedings of the Joint EDBT/ICDT 2013 Workshops
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Integrating sophisticated statistical methods into database management systems is gaining more and more attention in research and industry. One important statistical method is time series forecasting, which is crucial for decision management in many domains. In this context, previous work addressed the processing of ad-hoc and recurring forecast queries. In contrast, we focus on subscription-based forecast queries that arise when an application (subscriber) continuously requires forecast values for further processing. Forecast queries exhibit the unique characteristic that the underlying forecast model is updated with each new actual value and better forecast values might be available. However, (re-)sending new forecast values to the subscriber for every new value is infeasible because this can cause significant overhead at the subscriber side. The subscriber therefore wishes to be notified only when forecast values have changed relevant to the application. In this paper, we reduce the costs of the subscriber by optimizing the notifications sent to the subscriber, i.e., by balancing the number of notifications and the notification length. We introduce a generic cost model to capture arbitrary subscriber cost functions and discuss different optimization approaches that reduce the subscriber costs while ensuring constrained forecast values deviations. Our experimental evaluation on real datasets shows the validity of our approach with low computational costs.