Siebel Systems, Inc.

Oracle International Corporation

Edward Shaw-Lee Suen

Donovan Schneider

Kazi Atif-Uz Zaman

Sunil Prabhakar Dixit

James P Rogers

Ashish Mittal

Samar Abbas Lotia

Eric Alan Musser

Techniques to improve query caching performance by efficiently selecting queries stored in a cache for evaluation and increasing the cache hit rate by allowing for inexact matches. A list of candidate queries stored in the cache that potentially could be used to answer a new query is first determined. This list may include all cached queries, cached queries containing exact matches for select list items, or cached queries containing exact and/or inexact matches. Each of at least one candidate query is then evaluated to determine whether or not there is a cache hit, which indicates that the candidate query could be used to answer the new query. The evaluation is performed using a set of rules that allows for inexact matches of aggregates, if any, in the new query. A query plan is generated for the new query based on a specific candidate query with a cache hit.

Detecting and processing cache hits for queries with aggregates

Techniques to dynamically select data sources for users and to direct queries for the users to the proper data sources. Initially, user information (e.g., user name) for each user is received and used to determine a data source designated for the user. The designated data source may be determined via a look-up table or by running a query with the user information to obtain the identity (e.g., the logical name) of the data source. A data source variable for each user can then be set to the identity of the designated data source. Thereafter, queries for each user are directed to the user's designated data source by determining database connection information for the designated data source based on the data source variable and using this information to direct the queries.

Multi-user system with dynamic data source selection

A method and apparatus for performing a distinct count metric by utilizing aggregate navigation. For a distinct count query of an identifier of a detail table, where the query specifies zero or more restrictions, an aggregate table associated with the detail table is identified. It is then determined whether the aggregate table contains the identifier that is being distinct counted, and if so, whether the identifier is different in every row of the aggregate table. If it is, then the rows of the aggregate table that satisfy the specified restrictions are plain counted to obtain a result for the distinct count query. If the identifier is not different in every row of the aggregate table, a distinct count operation is performed on the rows of the aggregate table that satisfy the specified restrictions to obtain a result for the distinct count query.

Optimization for aggregate navigation for distinct count metrics

Techniques to provide dynamic multi-lingual support for software applications such as analytics, which typically rely on two different types of data, underlying data and metadata, to generate reports. The metadata defines the underlying data and a presentation layer displays the reports, which may be generated using presentation and metadata layer objects. Each such object may be defined by a language independent representation, which may in turn be associated with multiple display or language dependent representations (i.e., translations or translated strings) for multiple supported languages. One of the languages may be selected for use in a given session, and the display representations for the selected language are then bound to the language independent representations. The binding may be dynamically performed and is session-specific.

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Edward Shaw-Lee Suen