Abstract: Techniques are provided for a metasearch infrastructure. The metasearch infrastructure supports one or more of the following: hotel matching, backend support of user queries, frontend support for user queries, a cache layer that groups or organizes cached data in storage based on hotel location, scoring and ranking on the client side, experiments and rankers, duplicate booking prevention, blacklisting hotels, and indexing that utilizes a document-independent data structure to store index values.

Abstract: Techniques are provided for a metasearch infrastructure. The metasearch infrastructure supports one or more of the following: hotel matching, backend support of user queries, frontend support for user queries, a cache layer that groups or organizes cached data in storage based on hotel location, scoring and ranking on the client side, experiments and rankers, duplicate booking prevention, blacklisting hotels, and indexing that utilizes a document-independent data structure to store index values.

Abstract: In a method of generating a search boost vector, a first set of content location identifiers for first content are provided. A second set of content location identifiers for second content are identified. The second content is related to the first content in accordance with one or more predefined criteria. A search boost vector is generated by determining weights corresponding to content location identifiers in the second set of content location identifiers. The search boost vector may also be generated by determining weights corresponding to content location identifiers in the first set of content location identifiers. The predefined criteria may include one or more additional content location identifiers that are co-visited with one or more content location identifiers for the first set of content location identifiers. Co-visits are by a community of users during an interval of time.

Abstract: A server system accesses, respectively, historical query information for queries that have search results corresponding to first information items and second information items and classification data of the first information items. Initially, the first information items are classified and the second information items are unclassified. Based on the classification data of the first information items and the historical query information, the server system generates classification data for the second information items and stores the generated classification data therein. In response to requests for service from client devices, the server system provides customized services to the client devices using the second information items and the corresponding classification data generated for the second information items.

Abstract: A method of processing a search query includes, for each search context of a plurality of search contexts, for each scoring primitive of a plurality of scoring primitives, and for a set of previously executed search queries that are consistent with the search context, determining a correlation between the scoring primitive and actual user selections of results of the previously executed search queries by a plurality of users. For each search context, machine learning is performed on the correlations to identify a predicted performance function comprising a weighted subset of the scoring primitives that meet predefined predictive quality criteria. Executing a user submitted search query includes associating the user submitted search query with a respective search context, and ordering at least a portion of the search results in accordance with the predicted performance function for the search context for the user submitted search query.

Abstract: A server system receives a question from an inquirer at a client. In response, the server system identifies a plurality of information items corresponding to the question. At least one of the information items has associated classification data profiling the information therein. The server system generates a profile for the question based on the classification data of the information items and identifies one or more consultants for the question among a plurality of candidates in accordance with a comparison of the question profile and the candidates' respective user profiles. The server system submits the question to at least one of the consultants for answer. After receiving an answer to the question from the consultant, the server system forwards the answer to a client for display to the inquirer.

Abstract: A server system accesses, respectively, historical query information for queries that have search results corresponding to first information items and second information items and classification data of the first information items. Initially, the first information items are classified and the second information items are unclassified. Based on the classification data of the first information items and the historical query information, the server system generates classification data for the second information items and stores the generated classification data therein. In response to requests for service from client devices, the server system provides customized services to the client devices using the second information items and the corresponding classification data generated for the second information items.

Abstract: A server system accesses, respectively, historical query information for queries that have search results corresponding to first information items and second information items and classification data of the first information items. Initially, the first information items are classified and the second information items are unclassified. Based on the classification data of the first information items and the historical query information, the server system generates classification data for the second information items and stores the generated classification data therein. In response to requests for service from client devices, the server system provides customized services to the client devices using the second information items and the corresponding classification data generated for the second information items.

Abstract: In a method of profiling a website, an information server receives multiple search queries from a website submitted by different users. Different search results responsive to the search queries are provided to the requesting users. The information server monitors activities of the users on the search results and generates a profile for the website using the search queries and the user activities. When the information server receives a same search query from two different websites, it identifies a plurality of information items associated with the search query. The information server uses profiles of the two websites to customize the information items into two different orders and serves the information items to the two websites in the two different orders.

Abstract: In a method of profiling a website, an information server receives multiple search queries from a website submitted by different users. Different search results responsive to the search queries are provided to the requesting users. The information server monitors activities of the users on the search results and generates a profile for the website using the search queries and the user activities. When the information server receives a same search query from two different websites, it identifies a plurality of information items associated with the search query. The information server uses profiles of the two websites to customize the information items into two different orders and serves the information items to the two websites in the two different orders.

Abstract: This document discusses, among other things, systems, devices and methods for fluid flow analysis for example, in an education environment. The light source, for example, a laser, is housed to illuminate particles in a fluid while minimizing exposure to the user. A control unit is provided that is remote from the fluid flow device. The fluid flow device further includes a removable fluid obstacle such that different fluid flow effects can be obtained. A computational unit is provided to perform computational fluid flow dynamics analysis on fluid flow models. The computed data can then be compared to the test data from the fluid flow analysis device.

Abstract: A server system accesses, respectively, historical query information for queries that have search results corresponding to first information items and second information items and classification data of the first information items. Initially, the first information items are classified and the second information items are unclassified. Based on the classification data of the first information items and the historical query information, the server system generates classification data for the second information items and stores the generated classification data therein. In response to requests for service from client devices, the server system provides customized services to the client devices using the second information items and the corresponding classification data generated for the second information items.

Abstract: This document discusses, among other things, systems, devices and methods for fluid flow analysis for example, in an education environment. The light source, for example, a laser, is housed to illuminate particles in a fluid while minimizing exposure to the user. A control unit is provided that is remote from the fluid flow device. The fluid flow device further includes a removable fluid obstacle such that different fluid flow effects can be obtained.

Abstract: This document discusses, among other things, systems, devices and methods for fluid flow analysis for example, in an education environment. The light source, for example, a laser, is housed to illuminate particles in a fluid while minimizing exposure to the user. A control unit is provided that is remote from the fluid flow device. The fluid flow device further includes a removable fluid obstacle such that different fluid flow effects can be obtained. A computational unit is provided to perform computational fluid flow dynamics analysis on fluid flow models. The computed data can then be compared to the test data from the fluid flow analysis device.

Abstract: This document discusses, among other things, systems, devices and methods for fluid flow analysis for example, in an education environment. The light source, for example, a laser, is housed to illuminate particles in a fluid while minimizing exposure to the user. A control unit is provided that is remote from the fluid flow device. The fluid flow device further includes a removable fluid obstacle such that different fluid flow effects can be obtained.

Abstract: In a method of generating a search boost vector, a first set of content location identifiers for first content are provided. A second set of content location identifiers for second content are identified. The second content is related to the first content in accordance with one or more first categories corresponding to the first content. The one or more first categories may be determined by applying a text classifier to the first content. The second content may also be identified based on one or more user profiles and/or one or more content site profiles. The search boost vector is generated using weights corresponding to the content location identifiers in the first set of content location identifiers and/or the second set of content location identifiers. The one or more search boost vectors may be used to boost one or more search results.