Abstract: Disclosed embodiments provide techniques for automatic software defect correction of a computer program. Computer program log files are scanned to identify runtime errors, corresponding to software defects. The software defects are analyzed to determine an error type, and identify the source file/code that caused the error. A solution template repository is searched for a solution template corresponding to the identified error type. If a solution template is found, the source code is checked out from the identified source repository. The template is applied to the “original” checked out source file to create a new source file with the fix, which is then uploaded back to the repository. A new software distribution is automatically built with the new source file, and the new software distribution is automatically deployed to the devices that experienced the error. Thus, defects can be automatically detected, repaired, and deployed without human intervention.

Abstract: Disclosed embodiments provide techniques for automatic software defect correction of a computer program. Computer program log files are scanned to identify runtime errors, corresponding to software defects. The software defects are analyzed to determine an error type, and identify the source file/code that caused the error. A solution template repository is searched for a solution template corresponding to the identified error type. If a solution template is found, the source code is checked out from the identified source repository. The template is applied to the “original” checked out source file to create a new source file with the fix, which is then uploaded back to the repository. A new software distribution is automatically built with the new source file, and the new software distribution is automatically deployed to the devices that experienced the error. Thus, defects can be automatically detected, repaired, and deployed without human intervention.

Abstract: A computer processor determines a schema that enables splitting of one or more elements of an XML file. The computer processor determines an XML file as a split candidate, based on one or more attributes of the one or more elements of the XML file. The computer processor splits the XML file at run-time into a plurality of subsets of the XML file, based on the one or more attributes of the one or more elements of the XML file, and the computer processor distributes the plurality of subsets of the XML file to a plurality of computing nodes of a computer processing system.

Abstract: A method, system, and computer program product for selectively disseminating content in social media are provided in the illustrative embodiments. Prior to a dissemination of the content, the content being prepared for the dissemination is detected. Using Natural Language Processing (NLP), the content is analyzed to identify a theme and a subject matter of the content. A set of data comprising data related to a set of social connections of a user in the social media is analyzed. A second theme resulting from analyzing a first data in the set of data is compared with the theme, the first data corresponding to a first social connection in the set of social connections. When the second theme matches the theme within a threshold, the first social connection is added to a selected sharing group. The content is disseminated to the selected sharing group.

Abstract: A computer processor determines a schema that enables splitting of one or more elements of an XML file. The computer processor determines an XML file as a split candidate, based on one or more attributes of the one or more elements of the XML file. The computer processor splits the XML file at run-time into a plurality of subsets of the XML file, based on the one or more attributes of the one or more elements of the XML file, and the computer processor distributes the plurality of subsets of the XML file to a plurality of computing nodes of a computer processing system.

Abstract: A computer processor determines a schema that enables splitting of one or more elements of an XML file. The computer processor determines an XML file as a split candidate, based on one or more attributes of the one or more elements of the XML file. The computer processor splits the XML file at run-time into a plurality of subsets of the XML file, based on the one or more attributes of the one or more elements of the XML file, and the computer processor distributes the plurality of subsets of the XML file to a plurality of computing nodes of a computer processing system.

Abstract: A method, system, and computer program product for selectively disseminating content in social media are provided in the illustrative embodiments. Prior to a dissemination of the content, the content being prepared for the dissemination is detected. Using Natural Language Processing (NLP), the content is analyzed to identify a theme and a subject matter of the content. A set of data comprising data related to a set of social connections of a user in the social media is analyzed. A second theme resulting from analyzing a first data in the set of data is compared with the theme, the first data corresponding to a first social connection in the set of social connections. When the second theme matches the theme within a threshold, the first social connection is added to a selected sharing group. The content is disseminated to the selected sharing group.

Abstract: A computer processor determines a schema that enables splitting of one or more elements of an XML file. The computer processor determines an XML file as a split candidate, based on one or more attributes of the one or more elements of the XML file. The computer processor splits the XML file at run-time into a plurality of subsets of the XML file, based on the one or more attributes of the one or more elements of the XML file, and the computer processor distributes the plurality of subsets of the XML file to a plurality of computing nodes of a computer processing system.

Abstract: A method and associated system of cross-retail marketing based on analysis of multichannel clickstream data that comprises a client application capturing, aggregating, and analyzing multiple clickstreams of a user. These clickstreams may be captured from multiple unrelated or competing sales or distribution channels and from multiple electronic platforms. The analysis may use methods of artificial intelligence, text analytics, semantic analytics, or other analytical methods to infer characteristics of the user, of the user's online commercial behavior and other commercial activities, and of products or services that the user may be interested in purchasing. The output of this analysis is forwarded to other channels or platforms visited by the user in order to allow those other channels or platforms to perform targeted commercial marketing functions related to the user's prior activities. In preferred embodiments, this method may be require an active consent or other authorization from the user.

Abstract: A method, system, and computer program product for assisting buying decisions using customer behavior analysis are provided in the illustrative embodiments. Product information comprising a set of product attributes is received about a grouping of products. Customer behavior information about a behavior of a customer is received from which a set of customer buying behavior factors is extracted. A customer buying behavior factor comprises an inferred preference of the customer for buying a product from the grouping of products. A weight is assigned to a customer buying behavior factor. A set of weighted customer buying behavior factors is mapped to a subset of the product attributes. At least one product is selected from the grouping of products such that the at least one product includes a subset of product attributes, and an overall weighted score of the at least one product exceeds a threshold.