Abstract: Techniques for enhancing actionable opportunities through machine learning are disclosed. In some embodiments, a system includes an event listener for detecting an opportunity to perform an action using one or more computing resources. When a new opportunity is detected, the system generates a set of search criteria, which is used to search a set of external web resources for current events. The system may then generate, using a machine learning model as a function of one or more features extracted from current events satisfying the search criteria, a score representing a likelihood of success that the action leads to an optimal result. The system may tune the machine learning model based on feedback received that is indicative of how the current events affected the likelihood of success for the action.

Abstract: Techniques are included for intelligently testing a software product using a limited number of test cases that are configured to test portions of the software product that have changed since a prior testing. To determine which test cases are best used for testing the implemented changes, a test case-to-programmatic flow mapping may be generated that relates each test case available for testing the software product with each programmatic flow that is tested by the test case. In addition, a programmatic flow-to-artifact mapping may be created that relates each programmatic flow discovered in the software product to each artifact that is accessed, created, changed, or dependent on the programmatic flow. Using these mappings, specific test cases may be determined for testing only those portions of the software product that have changed since the prior testing.

Abstract: Techniques for enhancing actionable opportunities through machine learning are disclosed. In some embodiments, a system includes an event listener for detecting an opportunity to perform an action using one or more computing resources. When a new opportunity is detected, the system generates a set of search criteria, which is used to search a set of external web resources for current events. The system may then generate, using a machine learning model as a function of one or more features extracted from current events satisfying the search criteria, a score representing a likelihood of success that the action leads to an optimal result. The system may tune the machine learning model based on feedback received that is indicative of how the current events affected the likelihood of success for the action.

Abstract: A system architecture is disclosed that includes a privacy management system. In particular, the privacy management system provides a policy hub for maintaining and managing customer privacy information. The privacy management system maintains a master data database for customer information and customer privacy preferences, and a rules database for privacy rules. The privacy management system captures, synchronizes, and stores customer privacy data. Privacy rules may be authored using a privacy management vocabulary, and can be customized for an enterprise's privacy policies.

Abstract: A system architecture is disclosed that includes a privacy management system. In particular, the privacy management system provides a policy hub for maintaining and managing customer privacy information. The privacy management system maintains a master data database for customer information and customer privacy preferences, and a rules database for privacy rules. The privacy management system captures, synchronizes, and stores customer privacy data. Privacy rules may be authored using a privacy management vocabulary, and can be customized for an enterprise's privacy policies.

Abstract: A system architecture is disclosed that includes a privacy management system. In particular, the privacy management system provides a policy hub for maintaining and managing customer privacy information. The privacy management system maintains a master data database for customer information and customer privacy preferences, and a rules database for privacy rules. The privacy management system captures, synchronizes, and stores customer privacy data. Privacy rules may be authored using a privacy management vocabulary, and can be customized for an enterprise's privacy policies.

Abstract: A system architecture is disclosed that includes a privacy management system.