Abstract: Incremental training data is used to supplement a trained model to provide personalized recommendations for a user. The personalized recommendations can be made by taking into account the user's behavior, such as, without limitation, the user's short and long term web page interactions, to identify item recommendations. A trained model is generated from training data indicative of the web page interaction data collected from a plurality of users. Incremental training data indicative of other web page interaction data can be used to supplement the trained model, or in place of the trained model. Incremental training data can be indicative of user behavior collected more recently than the data used to train the model, for example.

Abstract: A system and method are disclosed for optimizing the performance of an advertisement. The advertisement may be targeted based on correlations between advertisements and/or users. The correlations may be used to improve the click-through rate of advertisements. As data is collected and feedback is received, the correlation between ads and users may be updated, so that an advertiser's campaign can optimize its targeting of users.

Abstract: A method and apparatus for accelerating processing of a structured document. A hardware XML accelerator includes one or more processors (e.g., CMT processors), one or more hardware XML parser units, one or more cryptographic units and various interfaces (e.g., to memory, a network, a communication bus). An XML document may be processed in its entirety or may be parsed in segments (e.g., as it is received). A parser unit parses a document or segment character by character, validates characters, assembles tokens from the document, extracts data, generates token headers (to describe tokens and data) and forwards the token headers and data for consumption by an application. A cryptographic unit may enforce web security, XML security or some other security scheme, by providing encryption/decryption functionality, computing digital signatures, etc. Software processing, bus utilization and latencies (e.g.

Abstract: Incremental training data is used to supplement a trained model to provide personalized recommendations for a user. The personalized recommendations can be made by taking into account the user's behavior, such as, without limitation, the user's short and long term web page interactions, to identify item recommendations. A trained model is generated from training data indicative of the web page interaction data collected from a plurality of users. Incremental training data indicative of other web page interaction data can be used to supplement the trained model, or in place of the trained model. Incremental training data can be indicative of user behavior collected more recently than the data used to train the model, for example.

Abstract: Incremental training data is used to supplement a trained model to provide personalized recommendations for a user. The personalized recommendations can be made by taking into account the user's behavior, such as, without limitation, the user's short and long term web page interactions, to identify item recommendations. A trained model is generated from training data indicative of the web page interaction data collected from a plurality of users. Incremental training data indicative of other web page interaction data can be used to supplement the trained model, or in place of the trained model. Incremental training data can be indicative of user behavior collected more recently than the data used to train the model, for example.

Abstract: Incremental training data is used to supplement a trained model to provide personalized recommendations for a user. The personalized recommendations can be made by taking into account the user's behavior, such as, without limitation, the user's short and long term web page interactions, to identify item recommendations. A trained model is generated from training data indicative of the web page interaction data collected from a plurality of users. Incremental training data indicative of other web page interaction data can be used to supplement the trained model, or in place of the trained model. Incremental training data can be indicative of user behavior collected more recently than the data used to train the model, for example.

Abstract: A method and apparatus for accelerating processing of a structured document. A hardware XML accelerator includes one or more processors (e.g., CMT processors), one or more hardware XML parser units, one or more cryptographic units and various interfaces (e.g., to memory, a network, a communication bus). An XML document may be processed in its entirety or may be parsed in segments (e.g., as it is received). A parser unit parses a document or segment character by character, validates characters, assembles tokens from the document, extracts data, generates token headers (to describe tokens and data) and forwards the token headers and data for consumption by an application. A cryptographic unit may enforce web security, XML security or some other security scheme, by providing encryption/decryption functionality, computing digital signatures, etc. Software processing, bus utilization and latencies (e.g.

Abstract: A method and apparatus for accelerating processing of a structured document. A hardware XML accelerator includes one or more processors (e.g., CMT processors), one or more hardware XML parser units, one or more cryptographic units and various interfaces (e.g., to memory, a network, a communication bus). An XML document may be processed in its entirety or may be parsed in segments (e.g., as it is received). A parser unit parses a document or segment character by character, validates characters, assembles tokens from the document, extracts data, generates token headers (to describe tokens and data) and forwards the token headers and data for consumption by an application. A cryptographic unit may enforce web security, XML security or some other security scheme, by providing encryption/decryption functionality, computing digital signatures, etc. Software processing, bus utilization and latencies (e.g.

Abstract: A system and method are disclosed for optimizing the performance of an advertisement. The advertisement may be targeted based on correlations between advertisements and/or users. The correlations may be used to improve the click-through rate of advertisements. As data is collected and feedback is received, the correlation between ads and users may be updated, so that an advertiser's campaign can optimize its targeting of users.

Abstract: A method and apparatus for accelerating processing of a structured document. A hardware XML accelerator includes one or more processors (e.g., CMT processors), one or more hardware XML parser units, one or more cryptographic units and various interfaces (e.g., to memory, a network, a communication bus). An XML document may be processed in its entirety or may be parsed in segments (e.g., as it is received). A parser unit parses a document or segment character by character, validates characters, assembles tokens from the document, extracts data, generates token headers (to describe tokens and data) and forwards the token headers and data for consumption by an application. A cryptographic unit may enforce web security, XML security or some other security scheme, by providing encryption/decryption functionality, computing digital signatures, etc. Software processing, bus utilization and latencies (e.g.

Abstract: A method and apparatus for parallel execution of trigger actions. One or more embodiments of the invention comprise providing a C++ class (hereinafter referred to as “dispatcher”) that can take an SQL query or trigger action and start parallel execution of the trigger action. The trigger action is optimized and parallelized. The dispatcher executes the trigger action, sets up the communication links between the various operators in the trigger action, and ensures that all the results are sent back to the trigger.

Abstract: A method, apparatus, article of manufacture, and a memory structure for providing access to an array-based data object to a client is disclosed. The method comprises the steps of receiving a database query from a client; generating a first execution plan from the database query; transmitting at least a portion of the first execution plan to a data server; compiling a query result from the execution of the first execution plan; and transmitting the query result to the client on a first communication path wherein the query result comprises an identification for a master data object responsive to the database query and the master data object comprises an identification for a plurality of array-based objects associated with the master data object. Further, the method comprises the establishment of a second communication path between the data server and the client and the transmission of the master data object to the client via this second communication path.

Abstract: A method, apparatus, and an article of manufacture for parallel execution of SQL operations from within user defined functions. One or more embodiments of the invention provide the user defined function (UDF) with a C++ class (hereinafter referred to as “dispatcher”) that can take an SQL query and start parallel execution of the query. The query is optimized and parallelized. The dispatcher executes the query, sets up the communication links between the various operators in the query, and ensures that all the results are sent back to the data-server that originated the query request. Further, the dispatcher merges the results of the parallel execution and produces a single stream of tuples that is fed to the calling UDF. To provide the single stream to the calling UDF, one or more embodiments of the invention utilize a class that provides the UDF with a simple and easy-to-use interface to access the results of the nested SQL execution.

Abstract: A method, apparatus, and an article of manufacture for parallel execution of SQL operations from within user defined functions. One or more embodiments of the invention provide the user defined function (UDF) with a C++ class (hereinafter referred to as “dispatcher”) that can take an SQL query and start parallel execution of the query. The query is optimized and parallelized. The dispatcher executes the query, sets up the communication links between the various operators in the query, and ensures that all the results are sent back to the data-server that originated the query request. Further, the dispatcher merges the results of the parallel execution and produces a single stream of tuples that is fed to the calling UDF. To provide the single stream to the calling UDF, one or more embodiments of the invention utilize a class that provides the UDF with a simple and easy-to-use interface to access the results of the nested SQL execution.

Abstract: A method, apparatus, and an article of manufacture for parallel execution of SQL operations from stored procedures. One or more embodiments of the invention provide the stored procedure (stored procedure) with a C++ class (hereinafter referred to as “dispatcher”) that can take an SQL query and start parallel execution of the query. The query is optimized and parallelized. The dispatcher executes the query, sets up the communication links between the various operators in the query, and ensures that all the results are sent back to the data-server that originated the query request. Further, the dispatcher merges the results of the parallel execution and produces a single stream of tuples that is fed to the calling stored procedure. To provide the single stream to the calling stored procedure, one or more embodiments of the invention utilize a class that provides the stored procedure with a simple and easy-to-use interface to access the results of the nested SQL execution.

Abstract: A method, apparatus, and an article of manufacture for parallel execution of SQL operations from stored procedures. One or more embodiments of the invention provide the stored procedure (stored procedure) with a C++ class (hereinafter referred to as “dispatcher”) that can take an SQL query and start parallel execution of the query. The query is optimized and parallelized. The dispatcher executes the query, sets up the communication links between the various operators in the query, and ensures that all the results are sent back to the data-server that originated the query request. Further, the dispatcher merges the results of the parallel execution and produces a single stream of tuples that is fed to the calling stored procedure. To provide the single stream to the calling stored procedure, one or more embodiments of the invention utilize a class that provides the stored procedure with a simple and easy-to-use interface to access the results of the nested SQL execution.