Abstract: A computer-readable medium comprises data structure for providing information about levels of similarity between pairs of N documents. The data structure comprises a plurality of entries of similarity values representing levels of similarity for a plurality of pairs of the documents. Each of the similarity values represents a level of similarity of one document of a given pair relative to the other document of the given pair. The similarity value of each entry is greater than a threshold similarity value that is greater than zero. The plurality of similarity-value entries are fewer than N2?N in number if the similarity values are asymmetric with regard to document pairing, and the plurality of similarity-value entries are fewer than N 2 - N 2 in number if the similarity values are symmetric with regard to document pairing. A method and apparatus for generating the data structure are described.

Abstract: A computer-readable medium comprises data structure for providing information about levels of similarity between pairs of N documents. The data structure comprises a plurality of entries of similarity values representing levels of similarity for a plurality of pairs of the documents. Each of the similarity values represents a level of similarity of one document of a given pair relative to the other document of the given pair. The similarity value of each entry is greater than a threshold similarity value that is greater than zero. The plurality of similarity-value entries are fewer than N2?N in number if the similarity values are asymmetric with regard to document pairing, and the plurality of similarity-value entries are fewer than N 2 - N 2 in number if the similarity values are symmetric with regard to document pairing. A method and apparatus for generating the data structure are described.

Abstract: A computer-readable medium comprises data structure for providing information about levels of similarity between pairs of N documents. The data structure comprises a plurality of entries of similarity values representing levels of similarity for a plurality of pairs of the documents. Each of the similarity values represents a level of similarity of one document of a given pair relative to the other document of the given pair. The similarity value of each entry is greater than a threshold similarity value that is greater than zero. The plurality of similarity-value entries are fewer than N2?N in number if the similarity values are asymmetric with regard to document pairing, and the plurality of similarity-value entries are fewer than N 2 - N 2 in number if the similarity values are symmetric with regard to document pairing. A method and apparatus for generating the data structure are described.

Abstract: The delivery ratio of r (which is a fraction between 0 and 1) partitions a stream of documents into a section of top scoring r-fraction of documents and the remainder. This way a set of successively bigger delivery ratios, r1, r2, r3, . . . sections the stream into tiers. Any given document is assigned to a tier according to how many delivery ratio thresholds it matched or surpassed and how many it failed to reach. This creates a scoring structure which reflects the specificity of the document with respect to a profile in terms of density of relevant documents in the stream. In other words, a document in the kth tier is such that it failed to be classified in the top rk ratio of the stream (thus rk fraction of the stream is more relevant to the given profile than the document under consideration). At the same time this document was classified as being in the top rk?1 part of the stream.

Abstract: An information need can be modeled by a binary classifier such as support vector machine (SVM). SVMs can exhibit very conservative precision oriented behavior when modeling information needs. This conservative behavior can be overcome by adjusting the position of the hyperplane, the geometric representation of a SVM. The present invention describes a couple of automatic techniques for adjusting the position of an SVM model based upon a beta-gamma thresholding procedure, cross fold validation and retrofitting. This adjustment technique can also be applied to other types of learning strategies.

Abstract: A method and system for generating a workflow graph from empirical data of a process are described. A processing system obtains data corresponding to multiple instances of a process, the process including a set of tasks, the data including information about order of occurrences of the tasks. The processing system analyzes the occurrences of the tasks to identify order constraints. The processing system partitions nodes representing tasks into subsets based upon the order constraints, wherein the subsets are sequence ordered with respect to each other such that all nodes associated with a given subset either precede or follow all nodes associated with another subset. The processing system partitions nodes representing tasks into subgroups, wherein each subgroup includes one or more nodes that occur without order constraints relative to nodes associated with other subgroups. A workflow graph representative of the process is constructed wherein nodes are connected by edges.

Abstract: A computer-readable medium comprises data structure for providing information about levels of similarity between pairs of N documents. The data structure comprises a plurality of entries of similarity values representing levels of similarity for a plurality of pairs of the documents. Each of the similarity values represents a level of similarity of one document of a given pair relative to the other document of the given pair. The similarity value of each entry is greater than a threshold similarity value that is greater than zero. The plurality of similarity-value entries are fewer than N2?N in number if the similarity values are asymmetric with regard to document pairing, and the plurality of similarity-value entries are fewer than N 2 - N 2 in number if the similarity values are symmetric with regard to document pairing. A method and apparatus for generating the data structure are described.

Abstract: An information need can be modeled by a binary classifier such as support vector machine (SVM). SVMs can exhibit very conservative precision oriented behavior when modeling information needs. This conservative behavior can be overcome by adjusting the position of the hyperplane, the geometric representation of a SVM. The present invention describes a couple of automatic techniques for adjusting the position of an SVM model based upon a beta-gamma thresholding procedure, cross fold validation and retrofitting. This adjustment technique can also be applied to other types of learning strategies.