Abstract: Techniques are provided for autonomous caching of hierarchical data. In one technique, query log data is stored that comprises multiple entries, each entry (a) corresponding to a different instance of a query that was executed against a database and (b) indicating a tuple (or level grouping) comprising a set of hierarchy levels corresponding to a set of dimensions. Multiple tuples indicated in the query log data are identified. For each tuple: (1) a set of entries that indicate the tuple is identified and the set of entries is associated with the tuple; (2) aggregated performance data is generated for the tuple based on performance data associated with each entry in the set of entries that is associated with the tuple; and (3) based on the aggregated performance data, it is determined whether to create a new auto-cache table or to delete an existing auto-cache table associated with the tuple.

Abstract: Herein are techniques for automatically leveraging metadata of an analytic view to accelerate a relational query. In an embodiment, a computer stores model metadata that defines an analytic view that contains a join operation that is based on a dimension column of a dimension table and a join column of a fact table. The analytic view also contains a measure that is based on an aggregation operation and a measure column of the fact table. Also stored is denormalization metadata that defines a transparency view that is based on the analytic view. In operation, a query that references the transparency view is received. The query does not reference the analytic view. The query that references the transparency view is executed based on: a) the denormalization metadata that defines the transparency view, b) the model metadata that defines the analytic view, and c) the measure that is based on the aggregation operation and the measure column of the fact table.

Abstract: Herein, a computer stores metadata that defines an analytic view that is based on organizing operation(s) that respectively specify organizing column(s) of organizing table(s), including a join operation that specifies a dimension column of a dimension table and a join column of a fact table. An original query is received that does not reference the analytic view. The original query specifies at least one organizing operation that is identical, somewhat similar, or compatible to an organizing operation on which the analytic view is based. Based on that at least one organizing operation that the original query specifies and the metadata that defines the particular analytic view, the original query is determined to satisfy rewrite criterion(s). In response to determining that the original query satisfies the rewrite criteria, a rewritten query that references the analytic view is generated based on the original query that does not reference the analytic view.

Abstract: Herein are techniques for automatically leveraging metadata of an analytic view to accelerate a relational query. In an embodiment, a computer stores model metadata that defines an analytic view that contains a join operation that is based on a dimension column of a dimension table and a join column of a fact table. The analytic view also contains a measure that is based on an aggregation operation and a measure column of the fact table. Also stored is denormalization metadata that defines a transparency view that is based on the analytic view. In operation, a query that references the transparency view is received. The query does not reference the analytic view. The query that references the transparency view is executed based on: a) the denormalization metadata that defines the transparency view, b) the model metadata that defines the analytic view, and c) the measure that is based on the aggregation operation and the measure column of the fact table.

Abstract: Herein, a computer stores metadata that defines an analytic view that is based on organizing operation(s) that respectively specify organizing column(s) of organizing table(s), including a join operation that specifies a dimension column of a dimension table and a join column of a fact table. An original query is received that does not reference the analytic view. The original query specifies at least one organizing operation that is identical, somewhat similar, or compatible to an organizing operation on which the analytic view is based. Based on that at least one organizing operation that the original query specifies and the metadata that defines the particular analytic view, the original query is determined to satisfy rewrite criterion(s). In response to determining that the original query satisfies the rewrite criteria, a rewritten query that references the analytic view is generated based on the original query that does not reference the analytic view.

Abstract: Techniques are provided for autonomous caching of hierarchical data. In one technique, query log data is stored that comprises multiple entries, each entry (a) corresponding to a different instance of a query that was executed against a database and (b) indicating a tuple (or level grouping) comprising a set of hierarchy levels corresponding to a set of dimensions. Multiple tuples indicated in the query log data are identified. For each tuple: (1) a set of entries that indicate the tuple is identified and the set of entries is associated with the tuple; (2) aggregated performance data is generated for the tuple based on performance data associated with each entry in the set of entries that is associated with the tuple; and (3) based on the aggregated performance data, it is determined whether to create a new auto-cache table or to delete an existing auto-cache table associated with the tuple.