Abstract: A pair of records is tokenized to form a normalized representation of an entity represented by each record. The tokens are correlated to a machine learning system by determining whether a learned resolution already exists for the two entities. If not, the normalized records are compared to generate a comparison measure to determine whether the records match. The normalized records can also be used to perform a web search and web search results can be normalized and used as additional records for matching. When a match is found, the records are updated to indicate that they match, and the match is provided to the machine learning system to update the learned resolutions.

Abstract: A pair of records is tokenized to form a normalized representation of an entity represented by each record. The tokens are correlated to a machine learning system by determining whether a learned resolution already exists for the two entities. If not, the normalized records are compared to generate a comparison measure to determine whether the records match. The normalized records can also be used to perform a web search and web search results can be normalized and used as additional records for matching. When a match is found, the records are updated to indicate that they match, and the match is provided to the machine learning system to update the learned resolutions.

Abstract: Explicit and implicit feedback information, that is indicative of an issue in a deployed computing system, is collected. Information identifying attempted resolutions for the issue is collected as well. A knowledge base is generated that identifies issues and successful resolutions for those issues. During runtime, issues are detected, either explicitly or implicitly, and the knowledge base is accessed to determine whether a resolution has already been identified. If so, it can be proactively provided to the computing system to address the issue.

Abstract: Explicit and implicit feedback information, that is indicative of an issue in a deployed computing system, is collected. Information identifying attempted resolutions for the issue is collected as well. A knowledge base is generated that identifies issues and successful resolutions for those issues. During runtime, issues are detected, either explicitly or implicitly, and the knowledge base is accessed to determine whether a resolution has already been identified. If so, it can be proactively provided to the computing system to address the issue.

Abstract: Explicit and implicit feedback information, that is indicative of an issue in a deployed computing system, is collected. Information identifying attempted resolutions for the issue is collected as well. A knowledge base is generated that identifies issues and successful resolutions for those issues. During runtime, issues are detected, either explicitly or implicitly, and the knowledge base is accessed to determine whether a resolution has already been identified. If so, it can be proactively provided to the computing system to address the issue.

Abstract: A user interaction is detected that identifies an environment from which data is to be pulled for deployment in a solution represented by a solution package. Setup data is automatically extracted from an instance of an application that is to be replaced by the solution. An editable representation of the setup data is displayed for user configuration or modification. Application data is then extracted from the instance of the application according to the setup data, including any user modifications represented in the editable representation.

Abstract: A pair of records is tokenized to form a normalized representation of an entity represented by each record. The tokens are correlated to a machine learning system by determining whether a learned resolution already exists for the two entities. If not, the normalized records are compared to generate a comparison measure to determine whether the records match. The normalized records can also be used to perform a web search and web search results can be normalized and used as additional records for matching. When a match is found, the records are updated to indicate that they match, and the match is provided to the machine learning system to update the learned resolutions.

Abstract: A pair of records is tokenized to form a normalized representation of an entity represented by each record. The tokens are correlated to a machine learning system by determining whether a learned resolution already exists for the two entities. If not, the normalized records are compared to generate a comparison measure to determine whether the records match. The normalized records can also be used to perform a web search and web search results can be normalized and used as additional records for matching. When a match is found, the records are updated to indicate that they match, and the match is provided to the machine learning system to update the learned resolutions.

Abstract: A user interaction is detected which indicates that the user wishes to build and test a customized computing system. A build system is automatically generated to build models in the customized system. Test code is automatically generated and included as part of source code for the build. When a build is triggered, the build is generated and automatically tested. Build and test execution results are surfaced for user visualization.

Abstract: An upgrade or update that is available to a deployed computing system (or deployment) under analysis is identified. Performance improvement metrics measured at similar deployments, that incorporated the identified update, are accessed to estimate a performance improvement metric corresponding to incorporating the identified update at the deployment under analysis.

Abstract: A solution package, that has configured computing system assets from a base computing system, is received and analyzed to verify that it meets a set of predetermined verification criteria. A request is received to view the solution package. A user interface component is controlled to restrict access to the solution found in the solution package.

Abstract: A computer-implemented method of updating a system of customized software is provided. The method includes receiving an update request and collecting contextual information relative to the system of customized software. A query is generated for updates applicable to the system of customized software based on the contextual information. A query response is received indicative of at least one applicable update. A selection relative to the at least one applicable update is received. At least one update is selectively applied based on the selection.

Abstract: A solution package is configured for a given solution with computing system components from a base computing system. A data package is generated and included with the solution package for deployment to a given environment. A user deploy input is detected and the configured solution package is automatically deployed, along with the data package, to the given environment.

Abstract: A user interaction is detected that identifies an environment from which data is to be pulled for deployment in a solution represented by a solution package. Setup data is automatically extracted from an instance of an application that is to be replaced by the solution. An editable representation of the setup data is displayed for user configuration or modification. Application data is then extracted from the instance of the application according to the setup data, including any user modifications represented in the editable representation.

Abstract: Explicit and implicit feedback information, that is indicative of an issue in a deployed computing system, is collected. Information identifying attempted resolutions for the issue is collected as well. A knowledge base is generated that identifies issues and successful resolutions for those issues. During runtime, issues are detected, either explicitly or implicitly, and the knowledge base is accessed to determine whether a resolution has already been identified. If so, it can be proactively provided to the computing system to address the issue.

Abstract: A solution package, that has configured computing system assets from a base computing system, is received and analyzed to verify that it meets a set of predetermined verification criteria. A request is received to view the solution package. A user interface component is controlled to restrict access to the solution found in the solution package.

Abstract: An upgrade or update that is available to a deployed computing system (or deployment) under analysis is identified. Performance improvement metrics measured at similar deployments, that incorporated the identified update, are accessed to estimate a performance improvement metric corresponding to incorporating the identified update at the deployment under analysis.

Abstract: Package configuration user input mechanisms are displayed to a user and are actuated to pre-configure a solution package, that is a customized form of a base computing system, to be a pre-configured industry-specific package. The pre-configured package can be transmitted to a distribution system, where it is verified and can be accessed by end user organizations. A customized solution package can be prepared, from the pre-configured package, for deployment based on a structure of the end user organization. Data packages can be generated from a source system at the end user organization and linked to the customized solution package that is deployed at the end user organization.

Abstract: A user interaction is detected which indicates that the user wishes to build and test a customized computing system. A build system is automatically generated to build models in the customized system. Test code is automatically generated and included as part of source code for the build. When a build is triggered, the build is generated and automatically tested. Build and test execution results are surfaced for user visualization.

Abstract: And update installer provides an ISV user interface display that allows an ISV to download manufacturer updates to a computer system. The update installer also allows the ISV to subsequently upload ISV updates that incorporate the manufacturer's updates and any additional ISV updates. The ISV update package is saved on a lifecycle system that can be accessed by a customer for installation of the ISV updates on the customer's system.