Abstract: One or more processors receive resource type and capability information and activity information of workloads of a domain. A first model is generated and trained to map the resource information to the activity information of domain workloads. The activity information is decomposed into a set of activity core elements (ACEs). The one or more processors generate a second model, wherein the second model is trained to predict a set of resource types and resource capabilities of the respective resource types, based on an input of the first set of ACEs decomposed from the activity information of the workloads of the domain. The one or more processors receive a second set of ACEs that are decomposed from activities associated with an unprecedented workload, and the one or more processors generate a predicted set of resources to perform the second set of ACEs.

Abstract: A method and a related system for controlling user access to a target node in a knowledge graph may be provided. The method comprises defining a knowledge graph structure limitation for a user, defining a node type depending on the number of edges connecting to the node, determining a condition for an access to the target node, based on the knowledge graph structure limitation relative to the start node and the node type of the target node, upon the user attempting, coming from a start node, to access the target node in the knowledge graph, and granting access to the target node based on the determination.

Abstract: A method for event predictions is provided. The method includes receiving input data. The method further includes identifying an object in the input data with the identified object associated with a first node in a knowledge graph. The method further includes determining a second node of a first object event with the second node related to the first node in the knowledge graph. The method further includes contextualizing the identified input object with the first object event.

Abstract: A question and answer pair is received from an external knowledge base. From the question, a set of intents is extracted. Whether the set of intents exceeds a match threshold with a subset of a plurality of intents within an internal knowledge base is determined. In response to determining a match threshold success, associating the question with the subset of intents within the plurality. A virtual assistant is trained to answer the question using the subset of intents.

Abstract: Intelligent management of stub files in hierarchical storage is provided by: in response to identifying a file to migrate from a file system to offline storage, providing metadata for the file to a machine learning engine; receiving a stub profile for the file from the machine learning engine that indicates an offset from a beginning of the file and a length from the offset for previewing the file; and migrating the portion of the file from the file system to an offline storage based on the stub profile. In some embodiments this further comprises: monitoring file system operations; in response to detecting a read operation of the portion of the file: determining a file type; providing file data to the machine learning engine; and performing a supervised learning operation based on the file type and the file data to update the machine learning engine.

Abstract: A system may receive a query for a patient evaluation. The system may then receive historical patient information or hospital data, current medical information, patient information, and testing information. The system may then use the received information to generate a set of rules using a neural network and uses the rules to process an evaluation for the patient.

Abstract: The present disclosure relates to a method of coordinating personal assistants. The method comprises: sending a request to a set of personal assistants based on a received query. Assistant responses may be received in response to the request from respective personal assistants of the set of personal assistants. Each assistant response of the assistant responses comprises a confidence score of the respective assistant response. The confidence scores may be adjusted to a common scale. The adjusted confidence scores may be weighted using domain-specific weights of the personal assistants in accordance with the domain of the query. One or more assistant responses of the received assistant responses may be selected based on the weighted scores and the selected assistant responses may be provided to the user.

Abstract: One or more computer processors install a trigger on a primary database. The one or more computer processors, responsive to the trigger activating and a data modification associated with the primary database, format the data modification into a universal format for a plurality of backup databases. The one or more computer processors rotate an active backup database from the plurality of backup databases based on a determined alternating backup period. The one or more computer processors synchronize in real-time the primary database with the active backup database.

Abstract: Provided are a computer program product, system, and method for efficient management of advanced functions in software defined storage systems. A data manager is provided that allows processes performing network functions to register to receive event notifications when another process performs a function causing a network event. A process that completes a network function causing a network event notifies the data manager that the network event occurred. The data manager uses a registration database to determine the processes to receive an event notification of the event and a transmits an event notification to the determined processes.

Abstract: A disaster-recovery system contains a networked array of synchronous-to-asynchronous converter (SATC) modules that connect source databases to target backup databases. During backup, the system replicates each source-database transaction through a chain of these SATCs to a corresponding target, storing a local copy of the transaction on each SATC along the way. Each transaction's path between its source-target pair is identified by a set of tracking entries, where each entry identifies one SATC in the path. Every backup transfer between two SATCs in a chain is performed synchronously, such that a successor SATC confirms a successful transfer to a predecessor SATC only after the successor can confirm a successful transfer to the next SATC in the chain. During a subsequent recovery operation, tracking entries are regenerated to locate locally stored transactions that had not time to be fully replicated before a catastrophic failure had occurred.

Abstract: A knowledge graph is divided into a plurality of sub-graphs, each sub-graph comprising a plurality of vertices and a plurality of edges. The knowledge graph is represented as a summary graph comprising for each of the sub-graphs a summary-graph vertex. A local sub-graph is generated as a copy of one of the sub-graphs together with a copy of a surrounding graph to the one of the sub-graphs. The content of the local sub-graph is modified. The local sub-graph is reintegrated, upon a reintegration trigger event, back into the knowledge graph, wherein a structure of the surrounding graph is used as a reintegration aid, by overlaying the structure and the knowledge graph, thereby identifying identical vertices of the surrounding structure and the knowledge graph as anchor points from where changes in the local sub-graph are reintegrated into the knowledge graph.

Abstract: Methods, computer program products, devices, and/or systems are provided that perform the following operations: receiving data from an incoming data stream; separating the data into data chunks and storing the data chunks in accordance with a selection configuration matrix in a set of different matrix buffers until the matrix buffers are filled up; processing the data chunks by multiple processing units, each processing unit associated to a matrix buffer of the matrix buffers via an I/O block; providing the processed data chunks as inputs to a neural network component; and classifying the data and filtering the data based on a classification of the data by the neural network component.

Abstract: Embodiments for managing natural language user input are provided. Natural language input is received from a user utilizing a computing node. The natural language input is analyzed. The analyzing of the natural language input includes attempting to identify at least one of an intent associated with the natural language input, an entity associated with the natural language input, and context data associated with the natural language input. The natural language input is evaluated against a pre-trained model based on the analyzing of the natural language input. A quality score for the natural language input is calculated based on the evaluating of the natural language input. An action is caused to be performed utilizing the computing node based on the calculated quality score.

Abstract: The present disclosure relates to a method of coordinating personal assistants. The method comprises: sending a request to a set of personal assistants based on a received query. Assistant responses may be received in response to the request from respective personal assistants of the set of personal assistants. Each assistant response of the assistant responses comprises a confidence score of the respective assistant response. The confidence scores may be adjusted to a common scale. The adjusted confidence scores may be weighted using domain-specific weights of the personal assistants in accordance with the domain of the query. One or more assistant responses of the received assistant responses may be selected based on the weighted scores and the selected assistant responses may be provided to the user.

Abstract: A computer-implemented method according to one embodiment includes identifying a plurality of segment files within an object storage system, determining all data blocks associated with the plurality of segment files within the object storage system, and mapping all the data blocks associated with the plurality of segment files to a single new file within the object storage system.

Abstract: User bioelectrical authentication is provided. A bioelectrical configuration signature of a user in physical contact with a hardware token is received, via a token reader, during a low range radio frequency user authentication process to permit a secure transaction by the user. The bioelectrical configuration signature of the user is utilized as part of the low range radio frequency user authentication process to authenticate the user to perform the secure transaction.

Abstract: A method provides a set of predicted responses to a user. The method includes receiving a message from a user, the message having natural language information. The method includes processing, using cognitive switching logic (CSL), the natural language information and information from previous messages from the user. The method includes identifying, using CSL, a context of the natural language information based on the information from the previous messages. The method includes identifying, using CSL, at least one knowledge domain which contains a response to the message, based on the identified context and on user persona information. The method includes retrieving a response from each identified knowledge domain. The method further includes, in response to retrieving more than one response, transmitting feedback to CSL to refine identifying the at least one knowledge domain until only one response is retrieved. The method further includes presenting the one response to the user.

Abstract: A method for distributed code execution involving a first serverless computing infrastructure, the first serverless computing infrastructure comprising one or more first infrastructure nodes, the one or more first infrastructure nodes comprising a first invocation controller node and one or more first executing nodes, the one or more first infrastructure nodes being communicatively coupled to one or more client nodes, the one or more client nodes being external to the one or more first infrastructure nodes, the method comprising receiving event information, identifying application logic associated with the event information, selecting, from an invoker group, an invoker node for executing the application logic, causing the invoker node to execute the application logic, causing the invoker node to provide a result of the executed application logic, and receiving the result.

Abstract: A method to securely collect diagnostic information from an IT product is disclosed. In one embodiment, such a method includes contacting, by an IT product, an external service provider. Upon contacting the external service provider, the IT product checks a database of the external service provider to determine if diagnostic information associated with the IT product needs to be collected. If diagnostic information needs to be collected, an authorization request is sent to an administrator of the IT product. If the authorization request is approved, the IT product collects the diagnostic information and transmits the diagnostic information to the external service provider. A corresponding system and computer program product are also disclosed.

Abstract: Static parameters of a software container are identified that relate to metadata of the software container itself. The software container is assigned to a selected runtime environment based on the static parameters using a first machine learning model. Runtime parameters for the software container are identified by analyzing the software container at runtime. The runtime parameters relate to operations that the software container requires during runtime. Using a second machine learning model, it is determined whether the selected runtime environment matches the runtime parameters. Where the runtime environment matches, the software container continues to run in this environment. Where the runtime environment does not match, the software container is run in a different runtime environment that matches both the static and runtime parameters.