Patent application number | Description | Published |
20090327274 | PREFETCHING DATA FOR DOCUMENT RANKING - The subject matter disclosed herein relates to prefetching data for use in ranking of electronic documents via a document ranking component. | 12-31-2009 |
20090328014 | COMPILER BASED CODE MODIFICATION FOR USE IN DOCUMENT RANKING - The subject matter disclosed herein relates to alter an expression of executable instructions via a compiler component for use in ranking of electronic documents. | 12-31-2009 |
20100023474 | Decision Tree Coalescing for Document Ranking - Machine-learned ranking algorithms, e.g. for ranking search results, often use a sequence of decision trees involving decision nodes based on threshold values of features. Modules, systems and methods of optimizing such algorithms involve analyzing threshold feature values to determine threshold intervals for each feature and grouping decision trees according to the feature used in a root decision node. Then coalescing the decision trees within each group to form a coalesced group tree for each group and finally coalescing the coalesced group trees to form a coalesced tree that implements the algorithm. | 01-28-2010 |
20100070457 | Efficient Data Layout Techniques for Fast Machine Learning-Based Document Ranking - A computer readable medium stores a program for optimization for a search, and has sets of instructions for receiving a first decision tree. The first decision tree includes several nodes, and each node is for comparing a feature value to a threshold value. The instructions are for weighting the nodes within the first decision tree, determining the weighted frequency of a first feature within the first decision tree, and determining the weighted frequency of a second feature within the first decision tree. The instructions order the features based on the determined weighted frequencies, and store the ordering such that values of features having higher weighted frequencies are retrieved more often than values of features having lower weighted frequencies within the first decision tree. | 03-18-2010 |
20110055010 | Enabling High Performance Ad Selection - A method and a system are provided for enabling high performance ad selection. In one example, the system receives an ad. A relevance of the ad needs to be determined. The relevance is a function of one or more computational intensive functions. A computational intensive function is a function that requires more than trivial processing. The system identifies one or more arguments of the computational intensive functions that are within a fixed range. The system generates a tableau based on the one or more arguments that are within a fixed range. The tableau is configured to benefit run-time performance of an ad selection process whenever the computer uses the pre-generated tableau during run-time instead of calculating one or more computational intensive functions. | 03-03-2011 |
20110131093 | SYSTEM AND METHOD FOR OPTIMIZING SELECTION OF ONLINE ADVERTISEMENTS - An advanced system and method for optimizing selection of online advertisements is provided. Decision trees with expressions to evaluate feature values for advertisements may be received, and a decision tree similarity matrix of decision tree similarity values between pairs of decision trees may be generated that represent the number of common features between two decision trees. The edges of the decision tree similarity matrix may be sorted in non-increasing order by edge value, and the decision trees of each edge retrieved from the sorted order may be placed in an optimized sequence order for evaluation. In response to a request to serve advertisements, advertisements may be scored by evaluating the decision trees of advertisements in the optimized sequence order. The advertisements may then be ranked in descending order by score, and advertisement with the highest scores may be sent for display. | 06-02-2011 |
20120197626 | Method and System for Predicting Performance of Software Applications on Prospective Hardware Architecture - A system and method for identifying optimal system architectures for a reference application are provided. The system and method comprise executing a reference application and a plurality of test applications on a current system architecture and sampling performance data for each of the applications. The performance data is used to compute an application signature for each application. A similarity element is derived from the application signatures that illustrates the similarity between each application and every other application. Using a similarity threshold and an algorithm, a subset of test applications that are similar to the reference application are derived. | 08-02-2012 |
20120278793 | SYSTEM AND METHOD FOR ANALYZING DYNAMIC PERFORMANCE OF COMPLEX APPLICATIONS - A system and method for monitoring the performance and execution flow of a target application and generating a corresponding data model are provided. The system and method comprise attaching to a thread or process of a target application and tracking the execution of subroutines using instrumentation commands. Data representing the execution flow of the various subroutines, subroutine calls, and their performance is gathered and used to generate data models representing the threads and processes of the application. The data models are optionally merged and/or pruned. A visualization of the data models is generated indicating relevant points of interest within the target application's execution flow. | 11-01-2012 |
20140350912 | METHOD AND SYSTEM FOR PREDICTING PERFORMANCE OF SOFTWARE APPLICATIONS ON PROSPECTIVE HARDWARE ARCHITECTURE - A system and method for identifying optimal system architectures for a reference application are provided. The system and method comprise executing a reference application and a plurality of test applications on a current system architecture and sampling performance data for each of the applications. The performance data is used to compute an application signature for each application. A similarity element is derived from the application signatures that illustrates the similarity between each application and every other application. Using a similarity threshold and an algorithm, a subset of test applications that are similar to the reference application are derived. | 11-27-2014 |