Abstract: Some embodiments provide a program. The program receives from a client device a request specifying a plurality of variables comprising set of sources and a set of objects. The program further receives a set of values from the set of sources. Each value in the set of values specifies an object in set of objects. The program also receives a selection of a set of defined scenarios for the request and the set of values. The program further generates a set of linear programming models based on the set of defined scenarios, the request, and the set of values.

Abstract: In an example embodiment, identifications of user actions are received from a first user device, the user actions being actions related to identifying data from a multi-tenant database to view. A portion of a first search query is received from a first user device corresponding to a first tenant in a multi-tenant architecture. A list of permissions for the first user device is then obtained. A plurality of fields is retrieved from a multi-tenant database based on the portion of the first search query, the fields obtained from data stored by multiple different tenants in the multi-tenant database, the plurality of fields retrieved being limited to fields for which the first user device has permission to view. Then a plurality of autocomplete suggestions are identified from the plurality of retrieved fields, the identifying based on the user actions.

Abstract: In an example embodiment, a listing of factors to evaluate during calculating of an index score for documents in a database is received, as well as a ranking of each factor in the listing of factors. A relevance multiplication factor is also obtained. Then a document to index in the database is received and the document is indexed. An index score is calculated for the document based on the listing of factors and the ranking of each factor. Then a search query on the database is received. A search score for the document is calculated based on the search query. Then an overall relevance score is calculated for the document for the search query by combining the index score and the search score using the relevance multiplication factor.

Abstract: In an example embodiment, identifications of user actions are received from a first user device, the user actions being actions related to identifying data from a multi-tenant database to view. A portion of a first search query is received from a first user device corresponding to a first tenant in a multi-tenant architecture. A list of permissions for the first user device is then obtained. A plurality of fields is retrieved from a multi-tenant database based on the portion of the first search query, the fields obtained from data stored by multiple different tenants in the multi-tenant database, the plurality of fields retrieved being limited to fields for which the first user device has permission to view. Then a plurality of autocomplete suggestions are identified from the plurality of retrieved fields, the identifying based on the user actions.

Abstract: In an example embodiment, a listing of factors to evaluate during calculating of an index score for documents in a database is received, as well as a ranking of each factor in the listing of factors. A relevance multiplication factor is also obtained. Then a document to index in the database is received and the document is indexed. An index score is calculated for the document based on the listing of factors and the ranking of each factor. Then a search query on the database is received. A search score for the document is calculated based on the search query. Then an overall relevance score is calculated for the document for the search query by combining the index score and the search score using the relevance multiplication factor.