Efficiently updating materialized views
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
Complexity of answering queries using materialized views
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Optimizing queries using materialized views: a practical, scalable solution
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Continuously adaptive continuous queries over streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Optimizing mpf queries: decision support and probabilistic inference
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Processing forecasting queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A skip-list approach for efficiently processing forecasting queries
Proceedings of the VLDB Endowment
Content-based filtering for efficient online materialized view maintenance
Proceedings of the 17th ACM conference on Information and knowledge management
Context-aware parameter estimation for forecast models in the energy domain
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Efficient in-database maintenance of ARIMA models
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
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Forecasts are important to decision-making and risk assessment in many domains. There has been recent interest in integrating forecast queries inside a DBMS. Answering a forecast query requires the creation of forecast models. Creating a forecast model is an expensive process and may require several scans over the base data as well as expensive operations to estimate model parameters. However, if forecast queries are issued repeatedly, answer times can be reduced significantly if forecast models are reused. Due to the possibly high number of forecast queries, existing models need to be found quickly. Therefore, we propose a model index that efficiently stores forecast models and allows for the efficient reuse of existing ones. Our experiments illustrate that the model index shows a negligible overhead for update transactions, but it yields significant improvements during query execution.