Abstract: A system and method for predicting a performance of a target advertisement placement via a simulation for optimum tuning is disclosed. A simulator receives a set of queries from a production engine, selects a subset of simulation queries from the queries by filtering, modifies query parameters as needed, transmits the simulation queries to the target engine for simulation, collects search results from the target engine, and computes a summary metric, which includes data fields retrieved from the search results and quantities calculated by using a prediction model derived from a production data warehouse. The simulator may further produce a simulation report, an indicator of performance prediction for the target engine, which comprises multiple metrics for the target engine, calculated with varying engine parameters.

Abstract: A system and method for predicting a performance of a target advertisement placement via a simulation for optimum tuning is disclosed. A simulator receives a set of queries from a production engine, selects a subset of simulation queries from the queries by filtering, modifies query parameters as needed, transmits the simulation queries to the target engine for simulation, collects search results from the target engine, and computes a summary metric, which includes data fields retrieved from the search results and quantities calculated by using a prediction model derived from a production data warehouse. The simulator may further produce a simulation report, an indicator of performance prediction for the target engine, which comprises multiple metrics for the target engine, calculated with varying engine parameters.

Abstract: The present disclosure is directed to apparatuses, systems, and methods for automatically managing cost of sale (COS) bidding for merchants (alternatively referred to herein as “vendors”). Described herein are automated COS bidding processes (or automated COS bidding logic, modules, or engines) utilized such that merchants need not actively manage their bids (i.e., fees associated with displaying item listings). As described herein, embodiments automate merchant bids using a formula based, at least in part, on the price of the item, the cost of selling from the advertiser, and historical performance data.

Abstract: A system and method for predicting a performance of a target advertisement placement via a simulation for optimum tuning is disclosed. A simulator receives a set of queries from a production engine, selects a subset of simulation queries from the queries by filtering, modifies query parameters as needed, transmits the simulation queries to the target engine for simulation, collects search results from the target engine, and computes a summary metric, which includes data fields retrieved from the search results and quantities calculated by using a prediction model derived from a production data warehouse. The simulator may further produce a simulation report, an indicator of performance prediction for the target engine, which comprises multiple metrics for the target engine, calculated with varying engine parameters.

Abstract: A plurality of documents are ranked. The documents are returned as the result of a query computed for each search engine of a plurality of search engines. Each document of the plurality of documents is associated with a Uniform Resource Locator (URL). The plurality of documents includes at least one document returned as a result of the query from each of the search engines. For each URL associated with a document from the plurality of documents, a frequency of the URLs is determined. Ranking the plurality of documents includes ranking them based on one or more factors, which are based on the frequency determined for each URL associated with the plurality of documents.

Abstract: A plurality of search engines is selected from multiple search engines based on a search query and one or more criteria. To obtain search results for the search query, one or more search queries are submitted to the plurality of search engines. For each search engine of the plurality of search engines, certain search results are received for the one or more search queries submitted to each search engine. Consolidated search results are generated with consolidation of the certain search results that are received from each search engine of the plurality of search engines.

Abstract: A plurality of documents are ranked. The documents are returned as the result of a query computed for each search engine of a plurality of search engines. Each document of the plurality of documents is associated with a Uniform Resource Locator (URL). The plurality of documents includes at least one document returned as a result of the query from each of the search engines. For each URL associated with a document from the plurality of documents, a frequency of the URLs is determined. Ranking the plurality of documents includes ranking them based on one or more factors, which are based on the frequency determined for each URL associated with the plurality of documents.

Abstract: A plurality of documents are ranked. The documents are returned as the result of a query computed for each search engine of a plurality of search engines. Each document of the plurality of documents is associated with a Uniform Resource Locator (URL). The plurality of documents includes at least one document returned as a result of the query from each of the search engines. For each URL associated with a document from the plurality of documents, a frequency of the URLs is determined. Ranking the plurality of documents includes ranking them based on one or more factors, which are based on the frequency determined for each URL associated with the plurality of documents.

Abstract: A plurality of documents are ranked. The documents are returned as the result of a query computed for each search engine of a plurality of search engines. Each document of the plurality of documents is associated with a Uniform Resource Locator (URL). The plurality of documents includes at least one document returned as a result of the query from each of the search engines. For each URL associated with a document from the plurality of documents, a frequency of the URLs is determined. Ranking the plurality of documents includes ranking them based on one or more factors, which are based on the frequency determined for each URL associated with the plurality of documents.

Abstract: A plurality of search engines is selected from multiple search engines based on a search query and one or more criteria. To obtain search results for the search query, one or more search queries are submitted to the plurality of search engines. For each search engine of the plurality of search engines, certain search results are received for the one or more search queries submitted to each search engine. Consolidated search results are generated with consolidation of the certain search results that are received from each search engine of the plurality of search engines.

Abstract: A method, apparatus and article of manufacture that implements the method, automatically identifies and reuses software libraries. In various embodiments, a first set of specified block names is received. One or more libraries of the source project which contain the blocks associated with the first set of specified block names are automatically identified to provide a first set of libraries for reuse. In some embodiments, one or more depended-upon blocks associated with the blocks of the first set of libraries are also automatically identified; and, one or more depended-upon libraries in the source project, which contain the depended-upon blocks, are automatically identified for reuse. In yet another embodiment, a reuse library is built based on static libraries of the first set of libraries and static libraries of the depended-upon libraries.