At a cloud platform, a class of feed is received for an external feed corresponding to an information source, as are an instruction corresponding to a create operation for the external feed, and a dictionary input corresponding to parameters expected by the information source. The external feed produces a corresponding class of events. At the cloud platform, a handler is selected based on the received class of feed and the received create operation; the input dictionary is transferred to the handler; and the handler generates a unique destination to receive events for the class of events. The handler on the cloud platform generates a unique request to the information source to generate events of the class of feed to the unique destination and sends the request to the information source. Events generated from the information source responsive to the unique request are received at the unique destination.

For one embodiment of the present invention, methods and systems for accelerating data operations with efficient memory management in native code and native dynamic class loading mechanisms are disclosed. In one embodiment, a data processing system comprises memory and a processing unit coupled to the memory. The processing unit is configured to receive input data, to execute a domain specific language (DSL) for a DSL operation with a native implementation, to translate a user defined function (UDF) into the native implementation by translating user defined managed software code into native software code, to execute the native software code in the native implementation, and to utilize a native memory management mechanism for the memory to manage object instances in the native implementation.

There is provided expert systems and methods for determining the status of an entity in relation to one or more legal provisions. The one or more legal provisions are defined by one or more rules, which are evaluated using input data from a user which comprises attribute data related to the entity. The status of the entity is determined using the one or more rules and input data and is returned to the user.

A system and method to apply deep learning techniques to an automation engineering environment are provided. Big code files and automation coding files are retrieved by the system from public repositories and private sources, respectively. The big code files include examples general software structure examples to be utilized by the method and system to train advanced automation engineering software. The system represents the coding files in a common space as embedded graphs which a neural network of the system uses to learn patterns. Based on the learning, the system can predict patterns in the automation coding files. From the predicted patterns executable automation code may be created to augment the existing automation coding files.

Computer-implemented techniques for deriving many idiomatic programming language interfaces. The techniques allow a programmer to provide idiomatic interfaces in many different programming languages without extra per-language effort. The techniques provide a solution to technical problems involved in providing idiomatic interfaces in many different programming languages. In particular, the techniques solve the problem of providing idiomatic interfaces that use the different definitional elements required by different programming languages, and in a way that programmers experienced in the language expect.

Systems, methods, and computer program products for identifying a fraudulent device. A device analytics engine receives device data from a computing device, the device data including parameters associated with the computing device. The device analytics engine selects a set of rules in a plurality of rules that indicate at least one parameter in the plurality of parameters in the device data for determining a device identifier. The set of rules are evaluated in an order until the device identifier is determined from the at least one parameter indicated in the set of rules, the device data, and previously stored data from multiple computing devices. A score is generated for the computing device using one or more of the device identifier, device data, a set of rules, and previously receive device data that corresponds to the device identifier. A computing device is identified as a fraudulent computing device based on the score.

Techniques to determine a programming language of a set of code based on learned programming language patterns. One technique includes receiving a set of code, generating a pattern map of discovered string patterns from the set of code, comparing the string patterns included within the pattern map against learned programming language patterns included with a master voting map to identify one or more profiled programming languages that utilize the learned programming, language patterns that match the string patterns, generating a score card for the set of code by tallying scores for the one or more profiled programming languages based on the comparing, and determining one or more programming languages used to write the set of code based on the score card.

Methods, apparatus, systems and articles of manufacture are disclosed for intentional programming for heterogeneous systems. An example non-transitory computer readable storage medium includes instructions that, when executed, cause processor circuitry to at least identify a first code block having a first algorithmic purpose based on a second code block having a second algorithmic purpose, the second algorithmic purpose corresponding to the first algorithmic purpose, translate the first code block into executable domain specific language code, and output the executable domain specific language code.

A system and a method for application transformation to cloud by conversion of an application source code to a cloud native code is provided. A first and a second transformation recommendation path is received and a set of remediation templates are applied based on the first and the second transformation recommendation paths where the set of remediation steps comprises pre-defined parameterized actions. The system comprises a microservices unit configured to optimize assessment and implementation of microservices code for multiple target cloud platforms by determining a count of microservices anti-patterns in a microservices code, wherein the anti-patterns represent a pattern of the microservices code and ascertaining a current state of the microservices code by determining a maturity score. A set of repeatable steps associated with microservices code development are provided in a bundled form for accelerated implementation of changes in the microservices code for deployment on the multiple target cloud platforms.

Machine-trained models are generated based on a model description that defines parameters for training the model and that can inherit parameters from parent model descriptions. When a parent model description changes, the changes made to the parent model description are applied to the model description automatically. When a target model is re-generated, a description of the set of parameters for generating the target model is received. The parent model is then identified from the received description, and a description of the set of parameters for generating the parent model is retrieved. Using the description for the target model and the parent model, a pipeline for generating the target model is generated. Finally, the pipeline is executed to generate the target model.