Abstract: The present disclosure relates to computer-implemented methods, software, and systems for an automatic recovery job execution through a scheduling framework in a cloud environment. One or more recovery jobs are scheduled to be performed periodically for one or more registered service components included in a service instance running on a cluster node of a cloud platform. Each recovery job is associated with a corresponding service component of the service instance. A health check operation is invoked at a service component based on executing a recovery job at the scheduling framework corresponding to the service component. In response to determining that the service component needs a recovery measure based on a result from the health check operation, a recovery operation is invoked as part of executing a set of scheduled routines of the recovery job. Implemented logic for the recovery operation is stored and executed at the service component.

Abstract: A data structure called a product version map is introduced. The product version map contains a catalog of available application versions to be used by the application orchestration services. It can describe the available versions. For each version, there is an image, such as a Docker® container image, that can be used to instantiate the application instance. Furthermore, in some example embodiments, an expiration date can be given, which could be used to enforce upgrades of application instances when their used version expires.

Abstract: The present disclosure pertains to provisioning of credentials, and in particular to provisioning of authentication credentials to a computer device for accessing a cloud platform computer system. The computer device obtains sensor data and sends a request including a device identifier to a provisioning server using a provisioning server network address. The computer device receives a response, from the provisioning server, including a platform credential and a platform server network address of a platform server. The computer device stores the platform credential. The computer device sends the sensor data and the platform credential to the platform server using the platform server network address.

Abstract: A system for generating a mixed query plan including operator alternatives. A method may include generating a mixed query plan including a first operator selected as a pre-compiled operator; generating the mixed query plan including a second operator selected as operator alternatives, the operator alternatives configuring the second operator as pre-compiled or code-generating alternatives; delaying selection of one of the operator alternatives until additional information regarding the mixed query plan becomes available; generating the mixed query plan including a third operator selected as a code-generating operator; and selecting, given the third operator representing the additional information, one of the operator alternatives to enable execution of the mixed query plan using the selected operator alternative. Related methods and articles of manufacture are also described.

Abstract: Embodiments relate to learning and determining user interface (UI) compliance with design guidelines. Those guidelines may enforce operability with requirements of an external UI platform. Embodiments receive as an input, a UI comprising a plurality of elements. An image of the UI is rendered, and a plurality of elements of the UI are identified from the image according to a recursive procedure. The plurality of elements are stored in a first logic tree exhibiting a first hierarchical structure having the plurality of elements as leaf nodes. The first logic tree is compared to a second logic tree exhibiting a second hierarchical structure, that is labeled with semantic metadata and stored within a knowledge base of a database. Input for the comparison may comprise the tree and/or additional meta information. Based upon the comparing, an output is generated indicating a similarity between the first logic tree and the second logic tree.

Abstract: Methods and apparatus are disclosed for controlling distributed software applications implementing asynchronous replication of shared data objects. Each shared data object has a distinct token. A token has respective states (active or inactive) at each computing system sharing the data object. At most one computing system can have the token active at any time; possessing an active token allows a computing system to update the shared data object. Counters maintain state across the computing systems, and semantic checks specific to a class of data objects (or, a single object) ensure consistency of updates across computing systems. Token activation requests and update replication requests are supported. Participating computing systems can have asymmetric or symmetric roles.

Abstract: Techniques and solutions are described for facilitating the use of machine learning techniques. In some cases, filters can be defined for multiple segments of a training data set. Model segments corresponding to respective segments can be trained using an appropriate subset of the training data set. When a request for a machine learning result is made, filter criteria for the request can be determined and an appropriate model segment can be selected and used for processing the request. One or more hyperparameter values can be defined for a machine learning scenario. When a machine learning scenario is selected for execution, the one or more hyperparameter values for the machine learning scenario can be used to configure a machine learning algorithm used by the machine learning scenario.

Abstract: Techniques and solutions are provided to facilitate monitoring and maintaining a computing environment. A current state of one or more parameters, resources, or services of a computing environment is determined. The current state is compared with predefined state information. It is determined that the current state is inconsistent with the predefined state information. It is determined whether an update condition is present. If the update condition is not present, the computing environment is automatically updated such that the current state matches the predefined state information. If the update condition is present, the predefined state information is updated.

Abstract: Provided is a system and method for writing IoT data of an object in transit and business context associated with the IoT data and/or the object to a blockchain. In one example, the method may include receiving sensor data of an object that is in a multi-party transactional process, identifying dynamic context of the object based on a current position of the object within the multi-party transactional process, generating a data block comprising the received sensor data of the object and the identified dynamic context of the object, and storing the data block within a blockchain on an immutable distributed ledger.

Abstract: Methods, systems, and computer-readable storage media for providing, by a machine learning (ML) platform, a first binary classifier, processing, by the first binary classifier a super-set of invoices to provide a plurality of sets of invoices based on matching pairs of invoices in the super-set of invoices, providing, by the ML platform, a second binary classifier, processing, by the second binary classifier, a bank statement and the plurality of sets of invoices to define two or more super-invoices based on aggregate features of invoices in the plurality of sets of invoices, and match the bank statement to a super-invoice of the two or more super-invoices, and outputting a match of the bank statement to the super-invoice.

Abstract: Disclosed herein are system, method, and computer program product embodiments for training a digital assistant with expert knowledge. An organization or individual utilizing a suite of software applications with a harmonized user experience may employ a digital assistant within the user experience to assist users when problems arise. The digital assistant may field questions and requests in informal and unstructured speech and text, analyze this natural language, and determine an appropriate response. The digital assistant may utilize existing suitable knowledge bases and leverage subject-matter experts. The digital assistant may learn from the expert's response to the user in order to apply the knowledge to a similar question or request in the future.

Abstract: A computer-implemented method includes feeding exception log entries from a plurality of exception logs associated with respective heterogenous computing components into an aggregated exception log comprising aggregated exception log entries, mapping an aggregated exception log entry in the aggregated exception log to a corresponding exception identifier, identifying a descriptor describing an exception condition based on the exception identifier, determining one or more destinations corresponding to the aggregated exception log entry, and routing a notification comprising the descriptor to the one or more destinations.

Abstract: Techniques and solutions are provided for integrating master data from multiple applications. Master data from multiple applications can be integrated for use in processing data associated with internet of things (IOT) devices, such as by joining master data with timeseries data (including aggregated values). Integrating master data from multiple applications can include converting master data from a schema used by an application into an analytics schema. New or updated master data can be indicated in a message sent by an application. In processing the message, additional master data, or data used to determine how master data should be processed, can be retrieved.

Abstract: According to some embodiments, methods and systems may be associated with a cloud computing environment having an integration service (e.g., associated with a Software-as-a-Service or a Platform-as-a-Service). A design micro service may have a User Interface (“UI”) framework and UI components in a domain specific language for an integration developer. A custom flow step development kit may receive, from the integration developer via a browser-based graphical UI, information to build logic for a custom flow step associated with a microservice-based integration service. In some embodiments, a new integration component is embedded into an existing set of components for a tenant, and the new custom flow step is deployed in, and re-usable by, other integration services (e.g., via a marketplace).

Abstract: Provided is a method and system for normalizing catalog item data to create higher quality search results. In one example, the method may include receiving a record comprising an unstructured description of an object, identifying a type of the object from among a plurality of object types and identifying a predefined attribute of the identified type of object, extracting a value from the unstructured description corresponding to the predefined attribute and modifying the extracted value to generate a normalized attribute value, and storing a structured record of the object in a structured format comprising a plurality of values of a plurality of attributes of the object from the unstructured description including the normalized attribute value for the predefined attribute of the object.

Abstract: Custom alerts may be generated based on sound type indicators determined using a machine learning classification model trained on user-provided sound recordings and user-defined sound type indicators. A device may provide a sound recording and a type indicator identifying an entity that made a sound in the recording for storage in a database that includes a plurality sound recordings associated with a plurality of type indicators. A machine learning classification model may be trained based on the stored recordings, including the user-defined recordings. The model may be used to classify sounds recorded by other devices and generate alerts identifying the type of sound. Thus, multiple users may contribute data to customize machine learning models that recognize sounds and generate alerts based on user-defined identifiers.

Abstract: Methods and systems may be associated with a cloud computing environment. A serverless function orchestrator may execute a socket activation for a VM to pre-provision a TCP socket (e.g., setting up virtual interfaces and creating socket structures) before the VM hosts any serverless function associated with the pre-provisioned TCP socket. After this socket activation, the orchestrator may receive a request for a first serverless function and, responsive to the received request, start the first serverless function on the VM using the pre-provisioned TCP socket. After the activation and prior to starting the first serverless function, the system may queue packets received in connection with the pre-provisioned TCP socket. In some embodiments, multiple TCP sockets, each associated with a VM, may activated before any serverless functions are hosted and the first serverless function is started on a VM selected based on information in a serverless function experience data store.