Abstract: Methods for automatic selection of degrees of parallelism for efficient execution of queries in a database system are performed by systems and devices. An incoming query associated with a query system is received and features of the incoming query are determined. A system state of the query system and a set of executing queries are also determined, along with a query state of each executing query in the set. At runtime of the incoming query, allocation of a degree of parallelism for executing the query is determined by calculating different possible execution times for the incoming query at least partially concurrently with the set of executing queries. Execution times are calculated for different parallel thread options and based on query features, system state, or query states of executing queries. The execution of the incoming query is initialized with the parallel thread option corresponding to a specific execution completion time.

Abstract: Methods for automatic selection of degrees of parallelism for efficient execution of queries in a database system are performed by systems and devices. An incoming query associated with a query system is received and features of the incoming query are determined. A system state of the query system and a set of executing queries are also determined, along with a query state of each executing query in the set. At runtime of the incoming query, allocation of a degree of parallelism for executing the query is determined by calculating different possible execution times for the incoming query at least partially concurrently with the set of executing queries. Execution times are calculated for different parallel thread options and based on query features, system state, or query states of executing queries. The execution of the incoming query is initialized with the parallel thread option corresponding to a specific execution completion time.

Abstract: The building of an index in a hybrid data system that includes both relational data associated with a relational database system, and external data that is outside of the relational database system in an external data source. The building of the index includes the generating of an external data index of unit portions of the external data, the unit portion being measured common to multiple different formats of the external data. The external data index is usable to more quickly find unit portions that match one or more search criteria directed to the relational database system even though the external data source is outside of the relational database system. Queries may be evaluated in the context of this index to identify relational data and unit portion sets that are relevant to the query.

Abstract: Large numbers of lists of entities may be mined for similar entities to related searches. A representation for each list may be determined to provide for a comparison between lists and to support membership checks. A score for an element in a list may be computed that represents the validity of an item in the corpus of lists. Thus, a spurious element would receive a very low score, where a valid element would receive a higher score. A list weight is then computed using the constituent element weights, and the element and list weight are used to compute the nearest neighbors of a given query element.

Abstract: Data from a click log may be used to generate training data for a search engine. The pages clicked as well as the pages skipped by a user may be used to assess the relevance of a page to a query. Labels for training data may be generated based on data from the click log. The labels may pertain to the relevance of a page to a query.

Abstract: A social network may be used to determine a rating of a user with no prior history. The ratings for unrated nodes may be inferred from the existing ratings of users associated with the unrated node in either or both the underlying social network or other social networks. Additionally in some implementations, the effect of the rating of a rated node to an unrated node diminishes as the strength of their relationships decreases. In some cases, a social network may be modeled as an electrical network, and ratings may be modeled as voltages on the nodes of the social network, relationships in the social network may be modeled as connections in the electrical network, and in some cases the strength of relationships may be modeled as conductance of the connections. Ratings for nodes may be determined using Kirchhoff's Law and in some cases by solving a set of linear equations or by propagating positive and negative ratings using a random walk with absorbing states.

Abstract: Large numbers of lists of entities may be mined for similar entities to related searches. A representation for each list may be determined to provide for a comparison between lists and to support membership checks. A score for an element in a list may be computed that represents the validity of an item in the corpus of lists. Thus, a spurious element would receive a very low score, where a valid element would receive a higher score. A list weight is then computed using the constituent element weights, and the element and list weight are used to compute the nearest neighbors of a given query element.