Abstract: A system for executing a multi-tenant application includes at least one processor and at least one memory storing program instructions. The multi-tenant application generates one or more page size recommendations and one or more sequential request count recommendations for one or more calls to an external database. The multi-tenant application performs a first call to the external database using a page size which is based on a first page size recommendation, where the page size specifies a number of records to retrieve from the external database. The multi-tenant application also performs, in a sequential manner by the multi-tenant application, the first call and a number of subsequent calls to the external database, where the number of subsequent calls is based on a first sequential request count recommendation. Related methods and computer program products are also provided.

Abstract: Techniques are presented for the detection of whether an EV is using a household or other user location for charging. A machine learning model is trained on a training population of user locations using historical usage data and a label for a some of the user locations, typically a small proportion, indicating that an EV charges there, where the labels can, for example, be provided by a utility or derived from the EVs' telematics. The trained model can then be applied to un-labeled user locations' electricity usage data to detect EV charging, both assigning a label and a confidence value to the label. If telematics are available, for user locations at which EV charging is detected, the EV charging can be disaggregated from other electricity usage of the user location.

Abstract: Provided is a system and method directed to a process of extending a software application using a semantic model as the logic of the extension. A parallel architecture is created by the extension which allows the software application to process logic from a semantic model (e.g., a graph) and process logic from source programming code. In one example, the method may include generating an extension comprising logic for a software application hosted on a host platform. The logic may include an entity-based semantic model, The method may further include deploying the extension within the software application on the host platform, wherein the deploying includes modifying programming logic of the software application to execute the entity-based semantic model, and activating the extension within the software application on the host platform.

Abstract: Techniques are presented for scheduling the charging of electric vehicles (EVs) that protect the resources of local low voltage distribution networks. From utilities, data on local low voltage distribution networks, such as the rating of a distribution transformer through which a group of EVs are supplied, is provided to a load manager application. Telematics information on vehicle usage is provided from the EVs, such as by way of the original equipment manufacturer. From these data, the load manager application determines schedules for charging the group of EVs through a shared low voltage distribution network so that the capabilities of the local low voltage distribution network are not exceeded while meeting the needs of the EV user. Charging schedules are then transmitted to the on-board control systems of the EVs for implementation.

Abstract: Provided is a system and method directed to a process of extending a software application using a semantic model as the logic of the extension. A parallel architecture is created by the extension which allows the software application to process logic from a semantic model (e.g., a graph) and process logic from source programming code. In one example, the method may include generating an extension comprising logic for a software application hosted on a host platform. The logic may include an entity-based semantic model, The method may further include deploying the extension within the software application on the host platform, wherein the deploying includes modifying programming logic of the software application to execute the entity-based semantic model, and activating the extension within the software application on the host platform.

Abstract: Techniques are presented for scheduling the charging of electric vehicles (EVs) that protect the resources of local low voltage distribution networks. From utilities, data on local low voltage distribution networks, such as the rating of a distribution transformer through which a group of EVs are supplied, is provided to a load manager application. Telematics information on vehicle usage is provided from the EVs, such as by way of the original equipment manufacturer. From these data, the load manager application determines schedules for charging the group of EVs through a shared low voltage distribution network so that the capabilities of the local low voltage distribution network are not exceeded while meeting the needs of the EV user. Charging schedules are then transmitted to the on-board control systems of the EVs for implementation.

Abstract: Techniques are presented for scheduling the charging of electric vehicles (EVs) that protect the resources of local low voltage distribution networks. From utilities, data on local low voltage distribution networks, such as the rating of a distribution transformer through which a group of EVs are supplied, is provided to a load manager application. Telematics information on vehicle usage is provided from the EVs, such as by way of the original equipment manufacturer. From these data, the load manager application determines schedules for charging the group of EVs through a shared low voltage distribution network so that the capabilities of the local low voltage distribution network are not exceeded while meeting the needs of the EV user. Charging schedules are then transmitted to the on-board control systems of the EVs for implementation.

Abstract: In some implementations, there may be provided a method that includes: in response to receiving a request from a client device, initiate, by a cloud service, execution of a process; detecting, by the cloud service, that the process includes a call to a database that is external to the cloud platform; in response to the detecting, requesting, by the cloud service, a time-out value from a time-out value recommendation engine, the time-out value determined based on a state of a cloud platform hosting the cloud service; in response to the request, receiving, by the cloud service, the time-out value determined based on the state of the cloud platform; and performing, based on the time-out value determined based on the state of the cloud platform, the call to the database that is external to the cloud platform. Related systems, methods, and articles of manufacture are also disclosed.

Abstract: Technologies are described for determining between synchronous and asynchronous modes for computing service requests. Computing service requests are received by a computing service from clients. The computing service dynamically determines whether to use synchronous mode or asynchronous mode for processing the computing service requests. The computing service makes the dynamic determination of which mode to use (synchronous or asynchronous) based on various criteria, which can include synchronous/asynchronous mode recommendations generated by machine learning models and/or synchronous/asynchronous mode recommendations generated by static rules.

Abstract: Methods and systems are disclosed for data retrieval, from databases to clients, in an environment requiring runtime authorization. In response to a request for T data records, a learning module provides a prediction R of a suitable number of data records to retrieve from a database. Following retrieval of R records or record identifiers, authorization is sought from an authorization service, resulting in A of the records being authorized. The A authorized records are returned to the requesting client, and, if more records are needed, T is decremented and the cycle is repeated. A performance notification is provided to the learning module for training, with respect to providing values of prediction R. The performance notification can be based on a measure of authorization service performance, the number A of authorized records, latency, communication or resource costs, a measure of resource congestion, or other parameters. Variants are disclosed.

Abstract: Technologies are described for determining between synchronous and asynchronous modes for computing service requests. Computing service requests are received by a computing service from clients. The computing service dynamically determines whether to use synchronous mode or asynchronous mode for processing the computing service requests. The computing service makes the dynamic determination of which mode to use (synchronous or asynchronous) based on various criteria, which can include synchronous/asynchronous mode recommendations generated by machine learning models and/or synchronous/asynchronous mode recommendations generated by static rules.

Abstract: Technologies are described for performing automated data integration, reconciliation, and/or self-healing using machine learning. For example, data integration can be checked using a reconciliation procedure. The number of times that the reconciliation is performed can be determined dynamically by a machine learning model. For each iteration, reconciliation can be performed to check integrated data against source data. If any reconciliation errors are found, then self-healing operations can be performed. Results of the reconciliation can be output. The reconciliation results can be used to update the machine learning model so that the machine learning model can dynamically adjust the number of iterations to perform based at least in part on reconciliation results.

Abstract: Techniques are presented for scheduling the charging of electric vehicles (EVs) that protect the resources of local low voltage distribution networks. From utilities, data on local low voltage distribution networks, such as the rating of a distribution transformer through which a group of EVs are supplied, is provided to a load manager application. Telematics information on vehicle usage is provided from the EVs, such as by way of the original equipment manufacturer. From these data, the load manager application determines schedules for charging the group of EVs through a shared low voltage distribution network so that the capabilities of the local low voltage distribution network are not exceeded while meeting the needs of the EV user. Charging schedules are then transmitted to the on-board control systems of the EVs for implementation.

Abstract: Technologies are described for performing automated data integration, reconciliation, and/or self-healing using machine learning. For example, data integration can be checked using a reconciliation procedure. The number of times that the reconciliation is performed can be determined dynamically by a machine learning model. For each iteration, reconciliation can be performed to check integrated data against source data. If any reconciliation errors are found, then self-healing operations can be performed. Results of the reconciliation can be output. The reconciliation results can be used to update the machine learning model so that the machine learning model can dynamically adjust the number of iterations to perform based at least in part on reconciliation results.

Abstract: Methods, systems, and computer program products for a database processing system are described. A core database comprises common procedures for processing information, at least one of the common procedures configured to provide a common functionality to an analysis application. A content import database comprises content-specific procedures, at least one of the content-specific procedures configured to provide access for at least one of the common procedures to content of the content import database that conforms to a content-specific data model.

Abstract: Methods and systems are disclosed for flexibly managing lock modes in a data-sharing environment, including optimistic and pessimistic lock modes. A lock mode determination service receives a request from an application instance for a lock mode with which to access specified data objects. The service can determine and provide a suitable lock mode based on a context of the application, and optionally other parameters, by evaluating a checklist of criteria sorted in precedential order. Criteria can include geo-boundaries, platform, role, and others. The requesting application instance uses the determined lock mode to access the data, with optional assistance of a lock service. The lock mode determination service can support one or more applications having one or more instances each, and can also support multiple disjoint data-sharing environments. Suitable environments include cloud, datacenter, mobile, client-server, and SAAS.

Abstract: To probe linear and non-linear relationships of entities in a network, in a graphical user interface, in a login session, a selection of an action on a first node is received. In response to the selection of the action on the first node, the action is automatically performed. A navigation breadcrumb is dynamically generated in the graphical user interface with a first navigation identifier corresponding to the action performed on the first node and a corresponding first state information of the nodes. In database tables in a database, the first state information of the nodes corresponding to the first navigation identifier is stored. Selection of the first navigation identifier on the navigation breadcrumb is received, and in response to the selection, the first state information of the nodes is retrieved from the database tables and displayed in a canvas in an area of focus.

Abstract: Provided are devices and methods for generating and capturing context related to a situation using a network visualization. In one example, the method includes identifying a plurality of entities that are associated with each other based on a common situational event and generating a plurality of nodes representing the plurality of entities, determining relationships between the plurality of entities based on respective attributes of each of the plurality of entities with respect to the common situational event, generating context between the plurality of entities by generating a network visualization including the plurality of nodes linked together based on the determined relationships between the plurality of entities, and outputting the network visualization including the plurality of nodes linked together based on the determined relationships to a user interface.

Abstract: To probe linear and non-linear relationships of entities in a network, in a graphical user interface, in a login session, a selection of an action on a first node is received. In response to the selection of the action on the first node, the action is automatically performed. A navigation breadcrumb is dynamically generated in the graphical user interface with a first navigation identifier corresponding to the action performed on the first node and a corresponding first state information of the nodes. In database tables in a database, the first state information of the nodes corresponding to the first navigation identifier is stored. Selection of the first navigation identifier on the navigation breadcrumb is received, and in response to the selection, the first state information of the nodes is retrieved from the database tables and displayed in a canvas in an area of focus.

Abstract: Methods, systems, and computer program products for a database processing system are described. A core database comprises common procedures for processing information, at least one of the common procedures configured to provide a common functionality to an analysis application. A content import database comprises content-specific procedures, at least one of the content-specific procedures configured to provide access for at least one of the common procedures to content of the content import database that conforms to a content-specific data model.