Abstract: Methods, apparatus, computer program products for tracking sensitive data are provided. A method for tracking sensitive data comprises identifying, by one or more processing units, for a type of sensitive data, at least one key interface that carries the type of sensitive data and recording the at least one key interface. The method further comprises generating, by one or more processing units, for the type of sensitive data, for each type of sensitive data, a series of service nodes based on the at least one key interface, and monitoring, by one or more processing units, for the type of sensitive data, corresponding data traffic flowing through corresponding series of service nodes, based on the identified at least one key interface.

Abstract: Training a predict model with network traffic and data change messages generated by an existing web application running in a production environment. The predict model being is trained to predict data changes resulted from API calls embodied in network traffic. A stream of network traffic of the existing web application is replayed with an upgraded version of the existing web application to generate real data changes. The stream of network traffic is applied to the predict model to generate predicted data change messages. The predicted data change messages are comparing with real data change messages representing the real data changes. One or more existing APIs is identified as being possibly functionally degraded based on any inconsistency of the predicted data change messages with the real data change messages.

Abstract: Training a predict model with network traffic and data change messages generated by an existing web application running in a production environment. The predict model being is trained to predict data changes resulted from API calls embodied in network traffic. A stream of network traffic of the existing web application is replayed with an upgraded version of the existing web application to generate real data changes. The stream of network traffic is applied to the predict model to generate predicted data change messages. The predicted data change messages are comparing with real data change messages representing the real data changes. One or more existing APIs is identified as being possibly functionally degraded based on any inconsistency of the predicted data change messages with the real data change messages.

Abstract: Training a predict model with network traffic and data change messages generated by an existing web application running in a production environment. The predict model being is trained to predict data changes resulted from API calls embodied in network traffic. A stream of network traffic of the existing web application is replayed with an upgraded version of the existing web application to generate real data changes. The stream of network traffic is applied to the predict model to generate predicted data change messages. The predicted data change messages are comparing with real data change messages representing the real data changes. One or more existing APIs is identified as being possibly functionally degraded based on any inconsistency of the predicted data change messages with the real data change messages.

Abstract: Monitoring of an environmental location to detect waste disposal includes receiving, by a computer, a collection of images from aerial data acquisition sources, the collection of images corresponding to the environmental location and the waste disposed in the environmental location. The collection of images are processed by the computer to extract properties of the waste and first properties of the environmental location. Subsequently, the properties of the waste disposed in the environmental location are classified according to a class. Additional information from external data sources including second properties of the environmental location is received by the computer. The computer determines a behavior of waste disposal in the environmental location over a period of time based on the classified properties of the disposed waste and the first and second properties of the environmental location, and generated a remediation plan based on the determined behavior.

Abstract: Methods, apparatus, computer program products for tracking sensitive data are provided. A method for tracking sensitive data comprises identifying, by one or more processing units, for a type of sensitive data, at least one key interface that carries the type of sensitive data and recording the at least one key interface. The method further comprises generating, by one or more processing units, for the type of sensitive data, for each type of sensitive data, a series of service nodes based on the at least one key interface, and monitoring, by one or more processing units, for the type of sensitive data, corresponding data traffic flowing through corresponding series of service nodes, based on the identified at least one key interface.

Abstract: Monitoring of an environmental location to detect waste disposal includes receiving, by a computer, a collection of images from aerial data acquisition sources, the collection of images corresponding to the environmental location and the waste disposed in the environmental location. The collection of images are processed by the computer to extract properties of the waste and first properties of the environmental location. Subsequently, the properties of the waste disposed in the environmental location are classified according to a class. Additional information from external data sources including second properties of the environmental location is received by the computer. The computer determines a behavior of waste disposal in the environmental location over a period of time based on the classified properties of the disposed waste and the first and second properties of the environmental location, and generated a remediation plan based on the determined behavior.