A processor performing machine learning including spectral clustering can receive data from the sensor. Graph Laplacian of the data can be created and stored in a memory device. Spectral characteristic can be created by applying density of states and spectral gaps can be detected in an unsupervised manner in the spectral characteristic to determine r as number of clusters to cluster the data. A range space of a rational matrix of the graph Laplacian can be determined. K-means clustering can be performed on the range space of rational matrix of the graph Laplacian using r as the number of clusters, the K-means clustering returning r clusters of the received data.

A computer-implemented method, medium, and system for global deadlock detection in a hybrid database for transactional and analytical workloads are disclosed. In one computer-implemented method, a daemon is launched on a coordinator segment in a massively parallel processing (MPP) database, where the MPP database is a hybrid database for both transactional workloads and analytical workloads. A respective local wait-for graph for each of a plurality of segments in the MPP database is collected periodically, where each of the plurality of segments includes the coordinator segment or a worker segment of a plurality of worker segments in the MPP database. A global wait-for graph that includes all collected local wait-for graphs is built. The global wait-for graph is used to determine that a global deadlock exists in the MPP database. The global deadlock is broken using one or more predefined policies in response to determining that the global deadlock exists.

A graph representation of cloud resources and their relationships is generated and maintained to provide insights into impact of incidents affecting cloud resources on others in the cloud environment. Cloud resource data for the cloud resources are obtained and relationships among the cloud resources are determined. Relationships among the cloud resources are determined based on analysis of configuration data associated with the cloud resources from which relationships among cloud resources of different types can be inferred, and external sources may also be utilized to facilitate identification of relationships. A graph representation of the cloud resources and their determined relationships is built where the cloud resource data are stored in vertices with directed edges between the vertices representing the identified relationships. The graph can be analyzed based on various graph algorithms to analyze impact of misconfigured or compromised resources to identify related cloud resources that are or would be affected.

In some embodiments of the present disclosure, techniques are utilized that allow answers to be provided to end users such as health care consumers, based on benefit book documents. The benefit book documents, which do not initially contain machine-readable structural or semantic information, are processed in order to detect structure and create semantic content based on the structure. This semantic content may then be added to a graph that represents the information contained in the benefit book document. A computing device may then use the nodes of this graph to answer questions received from consumers, where templates that provide answers to the questions reference the nodes of the graph.

An example method of managing memory in a computer system implementing non-uniform memory access (NUMA) by a plurality of sockets each having a processor component and a memory component is described. The method includes replicating page tables for an application executing on a first socket of the plurality of sockets across each of the plurality of sockets; associating metadata for pages of the memory storing the replicated page tables in each of the plurality of sockets; and updating the replicated page tables using the metadata to locate the pages of the memory that store the replicated page tables.

Respective initial feature sets are obtained for the nodes of a graph in which the nodes represent instances of entity types and edges represent relationships. Using the initial feature sets and the graph, a graph convolutional model is trained to generate one or more types of predictions. In the model, a representation of a particular node at a particular hidden layer is based on aggregated representations of neighbor nodes, and an embedding produced at a final hidden layer is used as input to a prediction layer. The trained model is stored.

Systems and methods are disclosed for identifying a set of internal edges on a representation of a network that satisfy a set of demands on the network. The disclosed systems and methods perform a multi-step process of selecting the internal edges. In a first step, an initial set of internal edges can be selected using a clique graph (or in another suitable manner). In a second step, a second set of internal edges can be selected using stream graph(s) (or in another suitable manner). The second set of internal edges can be used when determining network paths that satisfy the demands. When the representation of the network has a cut of two, the disclosed systems and methods can identify a set of internal edges providing a degree of protection against link failure.

The present disclosure describes methods and systems to predict predicate metadata parameters in knowledge graphs via neural networks. The method includes receiving a knowledge graph based on a knowledge base including a graph-based dataset. The knowledge graph includes a predicate between two nodes and a set of predicate metadata. The method also includes determining a positive structural score, adjusting each positive structural score based on each corresponding significance parameter, generating a synthetic negative graph-based dataset, determining a negative structural score for each synthetic negative triple of the synthetic negative graph-based dataset, adjusting each negative structural score based on each corresponding significance parameter, determining a significance loss value based on the adjusted positive structural scores and the adjusted negative structural scores, and determining a likelihood score of a link between a third node and a fourth node in the knowledge graph based on the significance loss value.

Techniques are provided for serializing replication operations. A plurality of operations are implemented upon a first storage object and are replicated as a plurality of replication operations. An order with which the plurality of replication operation are to be executed upon a second storage object is determined. Execution of the plurality of replication operations upon the second storage object is serialized according to the order.

Techniques for generating a dataflow graph include generating a first dataflow graph with a plurality of first nodes representing first computer operations in processing data, with at least one of the first computer operations being a declarative operation that specifies one or more characteristics of one or more results of processing of data, and transforming the first dataflow graph into a second dataflow graph for processing data in accordance with the first computer operations, the second dataflow graph including a plurality of second nodes representing second computer operations, with at least one of the second nodes representing one or more imperative operations that implement the logic specified by the declarative operation, where the one or more imperative operations are unrepresented by the first nodes in the first dataflow graph.