Online Index Recommendations for High-Dimensional Databases Using Query Workloads
Usually users are 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.
If the query pattern change, then the query response using the physical database design that is developed based on a static snapshot of the query workload may significantly degrade
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.
Query response does not perform well if query patterns change.
Because it uses static query workload.
Its performance may degrade if the database size gets increased.
Tradition feature selection technique may offer less or no data pruning capability given query attributes.
We develop a flexible index selection frame work to achieve index selection for high dimensional data.
A control feedback technique is introduced for measuring the performance.
Through this a database could benefit from an index change.
The index selection minimizes the cost of the queries in the work load.
Online index selection is designed in the motivation if the query pattern changes over time.
By monitoring the query workload and detecting when there is a change on the query pattern, able to evolve good performance as query patterns evolve.
By creating index we can minimize the searching time.
Index will automatically adjust itself based on the query workloads over time
If the query pattern change it does not provide better result.
Efficiency is less.
Processor :Intel Pentium 4
Key board :102keys
Front End :J2EE
Back End :MS SQL
modules of Online Index Recommendations for High-Dimensional Databases Using Query Workloads.