Proactive Index Maintenance: Using Prediction Models for Improving Index Maintenance in Databases


  • André Medeiros IBM - Brasil
  • Aristênio Saraiva Universidade Estadual do Ceará
  • Gustavo Campos Universidade Estadual do Ceará
  • Pedro Holanda Universidade Federal do Ceará
  • José Maria Monteiro Universidade Federal do Ceará˜monteiro
  • Ângelo Brayner Universidade de Fortaleza
  • Sérgio Lifschitz Pontifícia Universidade Católica do Rio de Janeiro



prediction models, neural network, linear regression, index maintenance


This article presents a mechanism, denoted Proactive Index Maintenance (PIM, for short), for proactive index management based on the use of prediction models. The main objective of the proposed mechanism is to predict when a time-consuming query q will be executed, in order to proactively create index structures which reduce q's response time. After q is executed, PIM drops the created indexes for avoiding the overhead of updating index structures. Thus, indexes are automatically created and dropped by PIM in a proactive manner. PIM is DBMS-independent, runs continuously and with no DBA intervention. Experiments show that PIM presents low overhead, can be effectively deployed to predict time-consuming query execution and provides significant performance gain during time-consuming query execution. Different prediction models have been evaluated: neural networks (Multi-Layer Perceptron - MLP and Radial Basis Function - RBF) and Linear Regression. The results indicate that the prediction model is query-specific, i.e., it should be defined according to the statistical distribution (normal, poisson, binomial) of the query execution history.



Download data is not yet available.

Author Biography

José Maria Monteiro, Universidade Federal do Ceará

Professor Adjunto do Departamento de Computação.




How to Cite

Medeiros, A., Saraiva, A., Campos, G., Holanda, P., Monteiro, J. M., Brayner, Ângelo, & Lifschitz, S. (2012). Proactive Index Maintenance: Using Prediction Models for Improving Index Maintenance in Databases. Journal of Information and Data Management, 3(3), 255.



SBBD Articles