Abstract: Techniques for implementing a deep neural network architecture for search are disclosed herein. In some embodiments, the deep neural network architecture comprises: an item neural network configured to, for each one of a plurality of items, generate an item vector representation based on item data of the one of the plurality of items; a query neural network configured to generate a query vector representation for a query based on the query, the query neural network being distinct from the item neural network; and a scoring neural network configured to, for each one of the plurality of items, generate a corresponding score for a pairing of the one of the plurality of items and the query based on the item vector representation of the one of the plurality of items and the query vector representation, the scoring neural network being distinct from the item neural network and the query neural network.

Abstract: A method and system for collecting statistics on database tables. In one embodiment, the method includes identifying a plurality of tables for statistics collection, wherein each table of the plurality of tables are remote tables; examining at least one attribute for each table identified for statistics collection; prioritizing the tables for statistics collection based on the at least one attribute; and collecting statistics on the identified tables in a priority sequence.

Abstract: A method and system for collecting statistics on database tables. In one embodiment, the method includes identifying a plurality of tables for statistics collection, wherein each table of the plurality of tables are remote tables; examining at least one attribute for each table identified for statistics collection; prioritizing the tables for statistics collection based on the at least one attribute; and collecting statistics on the identified tables in a priority sequence.