Abstract: Methods and arrangements for automatically finding the dependency of a software product on other software products or components. From an install image or directory, a signature is found by deriving the same from a directory structure of the software. Further, a directory tree structure is built and an approximate sub-tree matching algorithm is applied to find commonalities across software products.

Abstract: Methods and arrangements for automatically finding the dependency of a software product on other software products or components. From an install image or directory, a signature is found by deriving the same from a directory structure of the software. Further, a directory tree structure is built and an approximate sub-tree matching algorithm is applied to find commonalties across software products.

Abstract: A novel domain adaption/transfer learning method applied to the problem of classifying abbreviated documents, e.g., short text messages, instant messages, tweets. The method uses a large number of multi-labeled examples (source domain) to improve the learning on the partial observations (target domain). Specifically, a hidden, higher-level abstraction space is learned that is meaningful for the multi-labeled examples in the source domain. This is done by simultaneously minimizing the document reconstruction error and the error in a classification model learned in the hidden space using known labels from the source domain. The partial observations in the target space are then mapped to the same hidden space, and classified into the label space determined by the source domain.

Abstract: A novel domain adaption/transfer learning method applied to the problem of classifying abbreviated documents, e.g., short text messages, instant messages, tweets. The method uses a large number of multi-labeled examples (source domain) to improve the learning on the partial observations (target domain). Specifically, a hidden, higher-level abstraction space is learned that is meaningful for the multi-labeled examples in the source domain. This is done by simultaneously minimizing the document reconstruction error and the error in a classification model learned in the hidden space using known labels from the source domain. The partial observations in the target space are then mapped to the same hidden space, and classified into the label space determined by the source domain.

Abstract: Product data pertaining to a plurality of products is gathered from a plurality of sources. Dependency information for the plurality of products is extracted from the product data. The dependency information is analyzed to determine dependencies for each product of the plurality of products. The dependencies for each product of the plurality of products are displayed to a user.

Abstract: A method, a system and a computer program product for analyzing a document are disclosed. In response to receiving the document, the document is partitioned into a plurality of segments using a set of pre-defined attributes. The plurality of segments of the document is mapped with corresponding segments of at least one template selected from a set of stored templates. A first template from the set of stored templates is selected and a group of segments in the first template is identified by computing at least one of a structural similarity and a textual similarity associated with the group of segments compared with the plurality of segments of the document. A subset of segments from the group of segments is aligned with corresponding segments from the plurality of segments of the document. A set of scores is computed using a set of pre-defined criteria, in response to the mapping. The document is analyzed based on the computed set of scores.

Abstract: A first mapping function automatically maps a plurality of documents each with a concept of ontology to create a documents-to-ontology distribution. An ontology-to-class distribution that maps concepts in the ontology to class labels, respectively, is received, and a classifier is generated that labels a selected document with an associated class identified based on the documents-to-ontology distribution and the ontology-to-class distribution.

Abstract: A first mapping function automatically maps a plurality of documents each with a concept of ontology to create a documents-to-ontology distribution. An ontology-to-class distribution that maps concepts in the ontology to class labels, respectively, is received, and a classifier is generated that labels a selected document with an associated class identified based on the documents-to-ontology distribution and the ontology-to-class distribution.

Abstract: System, method and computer program product provides a novel domain adaption/transfer learning approach applied to the problem of classifying abbreviated documents, e.g., short text messages, instant messages, tweets. The proposed method uses a large number of multi-labeled examples (source domain) to improve the learning on the partial observations (target domain). Specifically, a hidden, higher-level abstraction space is learned that is meaningful for the multi-labeled examples in the source domain. This is done by simultaneously minimizing the document reconstruction error and the error in a classification model learned in the hidden space using known labels from the source domain. The partial observations in the target space are then mapped to the same hidden space, and classified into the label space determined by the source domain. Exemplary results provided for a Twitter dataset demonstrate that the method identifies meaningful hidden topics and provides useful classifications of specific tweets.

Abstract: Methods and arrangements for automatically finding the dependency of a software product on other software products or components. From an install image or directory, a signature is found by deriving the same from a directory structure of the software. Further, a directory tree structure is built and an approximate sub-tree matching algorithm is applied to find commonalities across software products.

Abstract: System, method and computer program product provides a novel domain adaption/transfer learning approach applied to the problem of classifying abbreviated documents, e.g., short text messages, instant messages, tweets. The proposed method uses a large number of multi-labeled examples (source domain) to improve the learning on the partial observations (target domain). Specifically, a hidden, higher-level abstraction space is learned that is meaningful for the multi-labeled examples in the source domain. This is done by simultaneously minimizing the document reconstruction error and the error in a classification model learned in the hidden space using known labels from the source domain. The partial observations in the target space are then mapped to the same hidden space, and classified into the label space determined by the source domain. Exemplary results provided for a Twitter dataset demonstrate that the method identifies meaningful hidden topics and provides useful classifications of specific tweets.

Abstract: A method, a system and a computer program product for analyzing a document are disclosed. In response to receiving the document, the document is partitioned into a plurality of segments using a set of pre-defined attributes. The plurality of segments of the document is mapped with corresponding segments of at least one template selected from a set of stored templates. A first template from the set of stored templates is selected and a group of segments in the first template is identified by computing at least one of a structural similarity and a textual similarity associated with the group of segments compared with the plurality of segments of the document. A subset of segments from the group of segments is aligned with corresponding segments from the plurality of segments of the document. A set of scores is computed using a set of pre-defined criteria, in response to the mapping. The document is analyzed based on the computed set of scores.

Abstract: Methods and arrangements for automatically finding the dependency of a software product on other software products or components. From an install image or directory, a signature is found by deriving the same from a directory structure of the software. Further, a directory tree structure is built and an approximate sub-tree matching algorithm is applied to find commonalties across software products.

Abstract: Product data pertaining to a plurality of products is gathered from a plurality of sources. Dependency information for the plurality of products is extracted from the product data. The dependency information is analyzed to determine dependencies for each product of the plurality of products. The dependencies for each product of the plurality of products are displayed to a user.

Abstract: A plurality of data attributes are displayed to a user. The user makes a selection of at least two of the attributes. An initial one of the selected attributes is displayed, together with all possible values for the initial one of the selected attributes. The user selects at least one of the possible values for the initial one of the selected attributes.