A multi-threaded architecture for prefetching in object bases
EDBT '94 Proceedings of the 4th international conference on extending database technology: Advances in database technology
Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Algorithms for deferred view maintenance
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Efficient view maintenance at data warehouses
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Self-tuning histograms: building histograms without looking at data
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Approximate computation of multidimensional aggregates of sparse data using wavelets
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Multi-dimensional selectivity estimation using compressed histogram information
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
How to roll a join: asynchronous incremental view maintenance
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Approximating multi-dimensional aggregate range queries over real attributes
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Congressional samples for approximate answering of group-by queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
STHoles: a multidimensional workload-aware histogram
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
A robust, optimization-based approach for approximate answering of aggregate queries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Dynamic multidimensional histograms
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Dynamic Maintenance of Wavelet-Based Histograms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
LEO - DB2's LEarning Optimizer
Proceedings of the 27th International Conference on Very Large Data Bases
Selectivity Estimation Without the Attribute Value Independence Assumption
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Fast Incremental Maintenance of Approximate Histograms
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
A Prefetching Technique for Object-Oriented Databases
BNCOD 15 Proceedings of the 15th British National Conferenc on Databases: Advances in Databases
Analysing Object Relationships to Predict Page Access for Prefetching
Proceedings of the 8th International Workshop on Persistent Object Systems (POS8) and Proceedings of the 3rd International Workshop on Persistence and Java (PJW3): Advances in Persistent Object Systems
DB2 Advisor: An Optimizer Smart Enough to Recommend its own Indexes
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Dynamic sample selection for approximate query processing
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
WhatNext: A Prediction System for Web Requests using N-gram Sequence Models
WISE '00 Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 1 - Volume 1
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
Foundations and Trends in Databases
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Many state-of-the-art selectivity estimation methods use query feedback to maintain histogram buckets, thereby using the limited memory efficiently. However, they are ''reactive'' in nature, that is, they update the histogram based on queries that have come to the system in the past for evaluation. In some applications, future occurrences of certain queries may be predicted and a ''proactive'' approach can bring much needed performance gain, especially when combined with the reactive approach. For these applications, this paper provides a method that builds customized proactive histograms based on query prediction and mergers them into reactive histograms when the predicted future arrives. Thus, the method is called the proactive and reactive histogram (PRHist). Two factors affect the usefulness of the proactive histograms and are dealt with during the merge process: the first is the predictability of queries and the second is the extent of data updates. PRHist adjusts itself to be more reactive or more proactive depending on these two factors. Through extensive experiments using both real and synthetic data and query sets, this paper shows that in most cases, PRHist outperforms STHoles, the state-of-the-art reactive method, even when only a small portion of the queries are predictable and a significant portion of data is updated.