Online Index Recommendations: A Case Study

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Provided by: International Journal on Computer Science and Technology (IJCST)
Topic: Data Management
Format: PDF
Large databases pose a challenge with respect to efficient access. 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. So the authors are going to design one tool to address these issues they introduce a parameterizable technique to recommend indexes based on index types that are frequently used for large data sets. If the users query pattern changed the index will automatically adjust it. To do that they incorporate a query pattern change detection mechanism to determine when the access patterns have changed enough to warrant change in the physical database design.
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