Abstract: Method and systems can provide a scalable conversational experience for citizen developers for build automation generation. A knowledge graph is identified that represents entities, entity attributes, and entity relationships of entities used in application programming interfaces of an enterprise software system. A natural language model is generated based on information in the knowledge graph and a chatbot is configured with the natural language model to generate automations for automating enterprise processes in the enterprise software system. Chatbot logic is generated based on information in the knowledge graph. The chatbot participates in a conversation with a user of the enterprise software system and the chatbot determines, from the conversation, using the natural language model, parameters of a requested automation for automating a process of the enterprise software system. The chatbot provides the determined parameters to an automation tool for creation of the automation.

Abstract: Methods, systems, and computer-readable storage media for determining a demand for private cloud resources including hardware resources and software resources, initiating a workflow for retrieving the private cloud resources, determining an availability of the private cloud resources, providing an instruction to provision service instances according to the demand for the private cloud resources, and providing an access to the service instances using the private cloud resources.

Abstract: The present disclosure relates to computer-implemented methods, software, and systems that can use a language model trained for conversational interaction using natural language prompts. A prompt is received to request a resource from a corporate network environment. First input can be provided to a language model to determine an interface of an application from a set of applications associated with the corporate network environment. In response to determining the interface by the language model, a requirement description of the interface is obtained. Second input is provided to the language model that comprises the obtained requirement description of the interface and the received prompt, and is used as context for generating a network request definition for a request to be directed to the interface. The request is sent to the interface based on the network request definition provided by the language model to obtain the requested resource.

Abstract: In an example embodiment, a solution is provided that performs the rotation of access keys of cloud components that are shared in on-premise software components (such as in a hybrid deployment environment) such that a minimal number of keys are needed. The solution also provides for having a specified validity of the access key, so that the hybrid or on-premise components can retain access to the software components while the new access keys are being generated.

Abstract: Mechanisms are disclosed for machine learning-based diagram label recognition in connection with diagrams represented by unstructured images. An unstructured image of the diagram is received. A plurality of text blocks associated with the diagram is detected. The text blocks are associated with labels contained within the diagram. Textual contents of the text blocks and the labels are recognized. The labels are associated with corresponding edges and shapes within the diagram.

Abstract: Provided is a system and method which perform an antivirus scan of incoming files via a file management application of a file system. Infected files can be prevented from being stored to the file system. In one example, the method may include receiving, via a first application contained in a first data container, a data file that is uploaded for storage to a file system, storing the data file in a temporary storage, transmitting a location of the data file in the temporary storage to a second application contained in a second data container, and receiving, via the first application contained in the first data container, a response from the second application contained in the second container, indicating results of a security scan performed on the data file.

Abstract: In an example embodiment, a machine learning-based solution for generating synthetic CVs that preserve the statistical properties of the original corpus is provided, while providing strong privacy guarantees. As synthetic data do not refer to any natural person and can be generated from anonymized data, they are not subject to data protection regulations.

Abstract: The present disclosure involves systems, software, and computer implemented methods for dynamically balancing different interests in data privacy integration protocols. One example method includes receiving, by different applications in a multiple-application landscape, consent information from data subjects that indicates consent or objection to processing performed by a data controller for a specified purpose. Consent information received by different applications is distributed to the different applications in the landscape. Each application synchronizes consent information obtained by the application and consent information obtained by other applications and distributed to the application to generate synchronized consent information. A processing action to be performed by a first application for a first purpose for a data subject is identified.

Abstract: A system of configuring a database which is distributed across multiple nodes according to a table distribution, e.g., by storing respective tables of the database at respective nodes. A graph partitioning procedure is applied to a graph of the distributed database, with vertices representing tables and edges representing cross-table operations. A distribution of the tables across the nodes is determined based on the partitioning. The storage of the tables is configured according to the determined distribution.

Abstract: Systems and methods include generation of a set of n-grams of different lengths from each of a plurality of text portions, determination, for each set of n-grams, of matching topic variants of a domain ontology, determination of a topic associated with each of the matching topic variants, wherein a determined topic is associated with the text portion from which n-grams matching an associated topic variant were generated, generation of first n-grams of different lengths from a first text portion, determination, for each of a plurality of the first n-grams, of first matching topic variants of the domain ontology, determination of a first topic associated with each of the first matching topic variants, and determination of mappings between the first text portion and the plurality of text portions based on the topics associated with the plurality of text portions and the determined first topics.

Abstract: An ingress component may receive, from a client, an HTTP URL and HTTP header information for an incoming protocol message (e.g., an AS2 message). An endpoint selector may determine the HTTP header information along with an endpoint address associated with the incoming protocol message. Based on the incoming HTTP header information and the endpoint address of the incoming protocol message, the endpoint selector may dynamically resolve an appropriate deployed endpoint and output an indication of the dynamically resolved appropriate deployed endpoint. A runtime component of an integration platform can then execute the incoming protocol message and interface with the appropriate deployed endpoint.

Abstract: Example methods and systems are directed to automatically generating and executing intelligent test plans. When a file containing source code for an application is changed, an intelligent test plan is generated that selects a subset of the available tests for the application. A dependency graph may be generated that shows which other files depend on the modified file, directly or indirectly. By running the tests for the modified file and all files that depend on the modified file, without running all tests for the application, errors introduced by the changes to the modified file can be detected while reducing the time taken to run tests.

Abstract: Disclosed herein are system, method, and computer program product embodiments for securely performing a password change. An embodiment operates by receiving a password change request from a user. The password change request comprises an encrypted version of a new password for the user, a cleartext version of the new password, and a login name for the user. The embodiment then executes a command from a password rotator user account with the cleartext version of the new password, the encrypted version of the new password, and the login name. The embodiment then retrieves a public key associated with the login name. The embodiment then determines, based on the public key, that the password change request comes from the user and that the cleartext version of the new password has not been modified. The embodiment then sets the password of a user login associated with the user to the new password.

Abstract: In an example embodiment, a solution for matching entities in a query table with one or more entities in a target table, in the presence of a constraint on the value sum of the matching targets (hereinafter called the “value constraint”), using machine learning techniques, is provided. Specifically, a non-linear objective function is converted to a linear objective function and a machine learning model is trained using the linear objective function, allowing for the use of solver functions from libraries in order to speed matching over existing methods.

Abstract: Systems and methods include storage of a plurality of objects in a cache, determination, for each of the plurality of objects, of a value based on a quality of service level associated with a tenant to which the object belongs and on at least one time at which the object was accessed from the cache, determination of one of the plurality of objects to delete from the cache based on the determined values, and deletion of the determined one of the plurality of objects from the cache.

Abstract: The present disclosure involves systems, software, and computer implemented methods for user-specific access control for metadata tables. An example method includes receiving, from a user, a query that queries a metadata table. For each metadata table row, a determination is made as to whether the user owns the object represented by the metadata table row. If the user owns the object, the row is included in a result set for the query. If the user does not own the object, a determination is made as to whether the user has access permission to the object. If the user has access permission to the object, the row is included in the result set. If the user does not have access permission to the object, the row is excluded from the result set. After all metadata tables rows are processed, the result set is provided in response to the query.

Abstract: Provided are systems and methods that can generate a simplified application programming interface for a custom software system that includes a pre-built application programming interface. In one example, a method may include receiving source code of an application programming interface (API) of a custom software system with built-in functionality for accessing the custom software system, identifying a subset of program components within the API which are not active based on a configuration of the software system, removing the subset of program components from the API to generate a simplified API for the custom software system, and transferring the simplified API to the storage.

Abstract: In an example embodiment, a framework is provided to allow an LLM to regenerate an intermediate representation based on natural language feedback that is provided on the compilable computer code that was generated using the programmatic component from an earlier intermediate representation. The user is able to provide this feedback without having any knowledge of the intermediate representation and the framework is designed to allow the LLM to incorporate this feedback into future generation requests without the LLM being aware of the compilable computer code that was created from its prior iterations.