Online Index Recommendations for High-Dimensional Databases Using Query Workloads

High-dimensional databases pose a challenge with respect to efficient access. High-dimensional indexes do not work because of the often-cited "curse of dimensionality." However, users are usually interested in querying data over a relatively small subset of the entire attribute set at a time. A potential solution is to use lower dimensional indexes that accurately represent the user access patterns. A query response using the physical database design that is developed based on a static snapshot of the query workload may significantly degrade if the query patterns change. To address these issues, we introduce a parameterizable technique to recommend indexes based on index types that are frequently used for high-dimensional data sets and to dynamically adjust indexes as the underlying query workload changes. We incorporate a query pattern change detection mechanism to determine when the access patterns have changed enough to warrant change in the physical database design. By adjusting analysis parameters, we trade off analysis speed against analysis resolution. We perform experiments with a number of data sets, query sets, and parameters to show the effect that varying these characteristics has on analysis results.