Abstract: Metadata associated with a first multimedia object in a first medium is used to find a second multimedia object in a second medium. The metadata includes category data.

Abstract: A graph of nodes and edges is used to represent artist influence between different artists. Each node of the graph represents an artist. An edge between two nodes represents an influence relationship between two artists. The relative influence between artists is computed for the artists represented in the graph. Furthermore, a weighted artist influence graph is generated based on the computed influences.

Abstract: A computerized method of representing a dataset with a taxonomy includes representing a plurality of records within a dataset as a plurality of clusters in an initial taxonomy layer; generating a hierarchy of cluster sets based on clusters within the initial taxonomy layer, wherein the structure of the hierarchy is independent of the order of the records within the dataset; and labeling clusters within the hierarchy.

Abstract: Weights are assigned for attributes of multimedia objects by sorting the attributes into preference levels, and computing a weight for each preference level. A similarity value of a multimedia object to an object of interest is computed based on the attribute weights.

Abstract: Records including category data is clustered by representing the data as a plurality of clusters, and generating a hierarchy of clusters based on the clusters. Records including category data are classified into folders according to a predetermined entropic similarity condition.

Abstract: A computerized method of representing a dataset with a taxonomy includes obtaining a dataset comprising a plurality of records, the dataset being characterized by a vocabulary and each of the plurality of records being characterized by at least one term within the vocabulary; identifying nearest neighbors for each term within the vocabulary; imputing a degree of membership for each nearest neighbor identified for each term within the vocabulary; augmenting the obtained dataset with the imputed degree of membership; and generating a taxonomy of the augmented dataset.

Abstract: A graph of nodes and edges is used to represent artist influence between different artists. Each node of the graph represents an artist. An edge between two nodes represents an influence relationship between two artists. The relative influence between artists is computed for the artists represented in the graph. Furthermore, the artists are ordered based on the computed influence.

Abstract: A computerized method of representing a dataset with a taxonomy includes augmenting a dataset containing a plurality of records with a plurality of predetermined exemplars; representing the plurality of records and predetermined exemplars within the augmented dataset as a plurality of clusters in an initial taxonomy layer; generating a truncated hierarchy of cluster sets based on clusters within the initial taxonomy layer, wherein clusters within the truncated hierarchy contain no more than a predetermined number of exemplars; and labeling clusters within the truncated hierarchy.

Abstract: A graph of nodes and edges is used to represent artist influence between different artists. Each node of the graph represents an artist. An edge between two nodes represents an influence relationship between two artists. The relative influence between artists is computed for the artists represented in the graph. Furthermore, a weighted artist influence graph is generated based on the computed influences.

Abstract: A computerized method of representing a dataset with a taxonomy includes representing a plurality of records within a dataset as a plurality of clusters in an initial taxonomy layer; generating a hierarchy of cluster sets based on clusters within the initial taxonomy layer, wherein the structure of the hierarchy is independent of the order of the records within the dataset; and labeling clusters within the hierarchy.

Abstract: Metadata associated with a first multimedia object in a first medium is used to find a second multimedia object in a second medium. The metadata includes category data.

Abstract: Weights are assigned for attributes of multimedia objects by sorting the attributes into preference levels, and computing a weight for each preference level. A similarity value of a multimedia object to an object of interest is computed based on the attribute weights.

Abstract: A category dataset includes names of categories and relation data, where the relation data defines a relationship between the categories and content. The categories for the content are generated by retrieving a web page from a an online community generated web site, such as the, WIKIPEDIA web site, associated with a particular piece of content and analyzing the web page for content metadata. The category data for that piece of content is extracted from the content metadata. In addition, the terms in category dataset are reduced based on the categories and the relation data.

Abstract: Records including category data is clustered by representing the data as a plurality of clusters, and generating a hierarchy of clusters based on the clusters. Records including category data are classified into folders according to a predetermined entropic similarity condition.

Abstract: A graph of nodes and edges is used to represent artist influence between different artists. Each node of the graph represents an artist. An edge between two nodes represents an influence relationship between two artists. The relative influence between artists is computed for the artists represented in the graph. Furthermore, the artists are ordered based on the computed influence.

Abstract: The dimensionality of a content category dataset is reduced based on the categories and the relationship between the content and categories. The category dataset includes names of categories and relation data, where the relation data defines a relationship between the categories and content. The dimensionality of a category dataset is reduced by determining a number of subsets of the category dataset and generating new relation data, where the new relation data defines a relationship between the category dataset subsets and the content.

Abstract: A computerized method of representing a dataset with a taxonomy includes obtaining a dataset comprising a plurality of records, the dataset being characterized by a vocabulary and each of the plurality of records being characterized by at least one term within the vocabulary; identifying nearest neighbors for each term within the vocabulary; imputing a degree of membership for each nearest neighbor identified for each term within the vocabulary; augmenting the obtained dataset with the imputed degree of membership; and generating a taxonomy of the augmented dataset.

Abstract: A computerized method of representing a dataset with a taxonomy includes augmenting a dataset containing a plurality of records with a plurality of predetermined exemplars; representing the plurality of records and predetermined exemplars within the augmented dataset as a plurality of clusters in an initial taxonomy layer; generating a truncated hierarchy of cluster sets based on clusters within the initial taxonomy layer, wherein clusters within the truncated hierarchy contain no more than a predetermined number of exemplars; and labeling clusters within the truncated hierarchy.

Abstract: A method and apparatus for dynamic Web Service composition is described. A user request is received. Multiple Web Services that may need to be performed according to the user request are identified. A Web Service composition is generated from the multiple Web Services. The Web Service composition is expressed in a business process modeling language that incorporates exception handling.