Abstract: Disclosed herein are ultrasound probe assemblies that include the probe, a sheath, and an accessory implement that may be selectively attached to and detached form the probe. The implement includes an attachment surface for attaching accessories thereto. The attachment surface includes a defined angle with respect to the head of the probe so that accessories attached thereto are disposed in a defined alignment with the probe. The implement includes a circumferential wall defining a cavity configure to receive the probe head therein. The cavity includes a bottom opening to expose a patient coupling surface of the probe. The assembly may include an ultrasound gel disposed within the opening. The sheath may include a stretchable band to secure the sheath to the probe. The sheath defines a sterile barrier and may be attached to the implement.

Abstract: Processing a hybrid flow containing a continuous flow can include connecting a continuous flow to a finite flow of a hybrid flow using a continuous connector operator, and processing the data flow graph of the hybrid flow.

Abstract: Example embodiments relate to providing real-time monitoring and analysis of query execution. In example embodiments, a query plan is obtained for a database query that is scheduled for execution in a database. A query tree is then generated based on the query plan, where the query tree includes operator nodes that are associated corresponding operators in the query plan. At this stage, performance metrics are collected from the database during the execution of the database query. Next, the query tree is displayed with the performance metrics, where a related portion of the performance metrics are displayed in each of the operator nodes.

Abstract: Examples for mapping a relational database to a graph database include a mapping engine to execute an arbitrary query on a relational database, identify a result column tag based on a tag of an underlying base table, process the result column into a typed, directed property graph based on the result column tag, and output the typed, directed property graph to a graph database. Examples for mapping a graph database to a relational database include processing a graph transaction by updating a mapping layer with a surrogate describing a change to a database object, determining, for an object in the mapping layer, if a database constraint defined on the object is satisfied, collecting database changes defined by the surrogate into a database change request, submitting the change request to a relational database as a transaction, and deleting the surrogate for the object in the mapping layer.

Abstract: Examples for graph database query classification include receiving a graph query and determining if the graph query matches benchmark data. In the event that the graph query does not match benchmark data, the query may be parsed, a canonical internal representation of the query may be determined, the representation may be mapped to a rule, and the query may be classified based on the rule. In the event that the confidence score for the query classification does not exceed a threshold, the query may be sent to a synthetic graph or synopsis for simulation. In some examples, the simulation may include selecting computationally expensive graph operators in the query for simulation.

Abstract: Described herein are techniques for identifying a path in a workload that may be associated with a deviation. A workload may be associated with multiple measurements of a plurality of metrics generated during execution of the workload. The multiple measurements may be aggregated at multiple levels of execution. One or more measurements may be compared to one or more other measurements or estimates to determine whether there is a deviation from an expected correlation. If determined that there is a deviation, a path can be identified in the workload that may be associated with the deviation.

Abstract: Example implementations relate to automatic generation of sub-queries. For example, a computing device may include a processor. The processor may receive a query defining a particular set of data stored in at least one database. The processor may access metadata associated with the particular set of data and may select a data attribute associated with the particular set of data based on the metadata. The processor may automatically generate sub-queries of the query using the data attribute selected based on the metadata. The processor may process the sub-queries and provide a query result of the query that is a combination of sub-query results of the sub-queries.

Abstract: Examples for mapping a relational database to a graph database include a mapping engine to execute an arbitrary query on a relational database, identify a result column tag based on a tag of an underlying base table, process the result column into a typed, directed property graph based on the result column tag, and output the typed, directed property graph to a graph database. Examples for mapping a graph database to a relational database include processing a graph transaction by updating a mapping layer with a surrogate describing a change to a database object, determining, for an object in the mapping layer, if a database constraint defined on the object is satisfied, collecting database changes defined by the surrogate into a database change request, submitting the change request to a relational database as a transaction, and deleting the surrogate for the object in the mapping layer.

Abstract: Described herein are techniques for generating a visualization relating to execution of a workload. Multiple measurements of a plurality of metrics relating to execution of the workload may be aggregated at multiple levels of execution. A visualization may be generated that comprises a representation of the measurements of a metric at one of the levels of execution.

Abstract: Disclosed herein are techniques for optimizing and managing the execution of hybrid flows. An execution plan is generated for each hybrid flow based at least partially on attributes associated therewith. The execution of each hybrid flow is managed in accordance with the execution plan.

Abstract: Converting a hybrid flow can include combining each of a plurality of task nodes with a plurality of corresponding operators of the hybrid flow and converting the combined plurality of task nodes and the plurality of corresponding operators of the hybrid flow to a data flow graph using a code template.

Abstract: Examples for graph database management comprise a graph database system including a graph processor engine to receive a graph database update from an application, a graph navigation query engine to access a real-time graph and process the graph database update on the real-time graph, and a synchronization engine to extract changes from the real-time graph and process the changes to a derived graph view and to a historical graph. Examples for managing a graph database also include receiving a graph query, determining a graph query type, and in the event that the graph query type is a navigational short query type, accessing a real-time graph on a graph navigation query engine and processing the navigation short query, and in the event that the graph query type is an analytical long query type, accessing a historical graph on a graph analytic query engine and processing the analytical long query.

Abstract: A flow of operations is to be executed in a plurality of execution environments according to a distribution. In response to determining that the distribution is unable to achieve at least one criterion, the distribution is modified according to at least one policy that specifies at least one action to apply to the flow of operations in response to a corresponding at least one condition relating to a characteristic of the flow of operations.

Abstract: Example implementations relate to read-optimized database changes. For example, a computing device may include at least one processor. The at least one processor may receive a change associated with a read-optimized database and may identify at least one hot attribute associated with the change. The at least one processor may generate an insert operation based on the change using the at least one hot attribute and may send the insert operation to a hot table associated with the read-optimized database.

Abstract: Example implementations relate to a database transfer of changes. For example, a computing device may include at least one processor. The at least one processor may receive a stream of changes to an online transaction processing (OLTP) database and may cache the stream of changes in a buffer. The at least one processor may identify specified criteria indicating a manner of sending the stream of changes to an online analytical processing (OLAP) database and may transfer the stream of changes from the buffer to the OLAP database based on the specified criteria.

Abstract: A method and apparatus apply a transition to an initial information integration flow graph to form a modified information integration flow graph which is visually depicted in a modified design canvas. The initial information integration flow graph has nodes, each node having initial location coordinates for visual depiction in an initial design canvas, wherein nodes of the modified information integration flow graph having location coordinates based upon the initial location coordinates.

Abstract: Example implementations relate to speculative execution of a stream of changes. For example, a computing device may include at least one processor. The at least one processor may receive a stream of changes concurrently received by an online transaction processing (OLTP) database engine in communication with the computing device. The at least one processor may process the stream of changes based on speculative execution and verify that an order of the stream of changes processed based on speculative execution matches an OLTP order of the stream of changes committed by the OLTP database engine. The at least one processor may send the stream of changes processed based on speculative execution to an online analytical processing (OLAP) database engine to be stored in an OLAP database.

Abstract: Example implementations relate to automatic generation of sub-queries. For example, a computing device may include a processor. The processor may receive a query defining a particular set of data stored in at least one database. The processor may access metadata associated with the particular set of data and may select a data attribute associated with the particular set of data based on the metadata. The processor may automatically generate sub-queries of the query using the data attribute selected based on the metadata. The processor may process the sub-queries and provide a query result of the query that is a combination of sub-query results of the sub-queries.

Abstract: A system, method, and non-transitory computer readable medium are provided to access a graph comprising a plurality of nodes and at least one edge. Each node is associated with at least one database operation. Computer code is constructed that corresponds to the graph in accordance with a nesting level. The nesting level represents a degree of temporary storage to be allocated for intermediate output produced by the at least one database operation.

Abstract: A technique of optimizing analytic flows includes sampling source data using a sampling method, executing a flow over the sampled data, obtaining runtime statistics from the executed flow, and combining runtime statistics with historical statistics.