Abstract: Under the present invention, a best practice violation (BPV) engine resource is first built. This typically occurs by: receiving sets of source code from a plurality of sources; detecting a programming language of each of the sets of source code; extracting code patterns from the sets of source code; defining meta data for each code pattern that indicates a quality thereof, classifying each code pattern; and assigning a rank to each of the code pattern based on it's corresponding meta data as well as a skill level and an experience level of the developer thereof. Once the BPV engine resource is built, it will be used to detect best practice violations in other sets of source code.

Abstract: Under the present invention, a best practice violation (BPV) engine resource is first built. This typically occurs by: receiving sets of source code from a plurality of sources; detecting a programming language of each of the sets of source code; extracting code patterns from the sets of source code; defining meta data for each code pattern that indicates a quality thereof, classifying each code pattern; and assigning a rank to each of the code pattern based on it's corresponding meta data as well as a skill level and an experience level of the developer thereof. Once the BPV engine resource is built, it will be used to detect best practice violations in other sets of source code.

Abstract: Search time is reduced with a search engine that includes a bi-directional inverted index facility which can be accessed with a keyword search in one of a number of languages and provide a listing of documents contained in all of those languages. The keywords in all supported languages are preferably stored in an inverted index lookup table cross referenced to documents in those language containing the keywords. Keywords with the same meaning in different languages are accessible together when that keyword in one of the languages is queried. The search engine containing the table can identify pertinent documents either in a selected language, a second language or in all supported languages, as determined by the user. Information about each document can include not only the identity of the document but also information used in ranking the documents such as the number of times that a keyword appears in that document, and the keywords proximity to other keywords.

Abstract: Euclidean analysis is used to define queries in terms of a multi-axis query space where each of the keywords T1, T2, . . . Ti, . . . Tn is assigned an axis in that space. Sets of test queries St for each one from one of a plurality of server sources, are plotted in the query space. Clusters of the search terms are identified based on the proximity of the plotted query vectors to one another. Predominant servers are identified for each of the clusters. When a search query Ss is received, the location of its vector is determined and the servers accessed by the search query Ss are those that are predominant in the cluster which its vector may fall or is in closest proximity to.

Abstract: Query routing is based on identifying the preeminent search systems and data sources for each of a number of information domains. This involves assigning a weight to each search system or data source for each of the information domains. The greater the weight, the more preeminent a search system or data source is in a particular information domain. These weights Wi{1=0, 1,2, . . . N] are computed through a recursive learning process employing meta processing. The meta learning process involves simultaneous interrogation of multiple search systems to take advantage of the cross correlation between the search systems and data sources. In this way, assigning a weight to a search system takes into consideration results obtained about other search systems so that the assigned weights reflect the relative strengths of each of the systems or sources in a particular information domain. In the present process, a domain dataset used as an input to query generator.

Abstract: Query routing is based on identifying the preeminent search systems and data sources for each of a number of information domains. This involves assigning a weight to each search system or data source for each of the information domains. The greater the weight, the more preeminent a search system or data source is in a particular information domain. These weights Wi{1=0, 1,2, . . . N] are computed through a recursive learning process employing meta processing. The meta learning process involves simultaneous interrogation of multiple search systems to take advantage of the cross correlation between the search systems and data sources. In this way, assigning a weight to a search system takes into consideration results obtained about other search systems so that the assigned weights reflect the relative strengths of each of the systems or sources in a particular information domain. In the present process, a domain dataset used as an input to query generator.

Abstract: Euclidean analysis is used to define queries in terms of a multi-axis query space where each of the keywords T1, T2, . . . Ti, . . . Tn is assigned an axis in that space. Sets of test queries St each one from one of a plurality of server sources are plotted in the query space. Clusters of the search terms are identified based on the proximity of the plotted query vectors to one another. Predominant servers are identified for each of the clusters. When a search query Ss is received, the location of its vector is determined and the servers accessed by the search query Ss are those that are predominant in the cluster which its vector may fall or is in closest proximity to.

Abstract: Search time is reduced with a search engine that includes a bi-directional inverted index facility which can be accessed with a keyword search in one of a number of languages and provide a listing of documents contained in all of those languages. The keywords in all supported languages are preferably stored in an inverted index lookup table cross referenced to documents in those language containing the keywords. Keywords with the same meaning in different languages are accessible together when that keyword in one of the languages is queried. The search engine containing the table can identify pertinent documents either in a selected language, a second language or in all supported languages, as determined by the user. Information about each document can include not only the identity of the document but also information used in ranking the documents such as the number of times that a keyword appears in that document, and the keywords proximity to other keywords.