Hewlett Packard Enterprise Development LP

Micro Focus Lcc

Hewlett-packard Development Company, L.p.

Entit Software LLC

Unravel Data Systems, Inc.

Alkiviadis Simitsis

William Kevin Wilkinson

Umeshwar Dayal

Martin Arlitt

Petar Jovanovic

Craig Peter Sayers

Stavros Harizopoulos

Olga Poppe

Maria G Castellanos

Georgia Koutrika

David Alan Tamez Cantu

Meichun Hsu

Harumi Kuno

Mahashweta Das

Shivnath Babu

Themistoklis Palpanas

Adrian Daniel Popescu

Kevin Wilkinson

Erik Lik Han Chu

Alejandro Guerrero Gonzalez

Enrico Iori

Jorge Augusto Saldivar Galli

Abdul Wasay

A network traffic flow is directed to a computing services system is determined as being unrelated to the service that the system provides. In response, the network traffic flow is deflected away from the computing services system. Demand of the sender of the network traffic flow is managed by shaping the deflected network traffic flow in responding to the sender and/or by misinforming the sender in responding to the sender.

Demand management of sender of network traffic flow

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.

Hybrid flows containing a continous flow

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.

Real-time monitoring and analysis of query execution

Example implementations relate to a database and a data stream query. For example, a computing device may include a processor. The processor may receive a query associated with at least one of a database and a buffer storing streamed data from a data stream, where the database stores database data previously stored in the buffer. The processor may identify at least one postponed command relevant to the query, the at least one postponed command being associated with at least one of the database data and the streamed data. The processor may generate a modified query based on the query and the at least one postponed command, the modified query being a modification of the query to account for the at least one postponed command. The processor may process the modified query and provide a query result of the query based on the modified query being processed.

Database and data stream query

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.

Graph database and relational database mapping

A system and method for data application performance management is disclosed. According to one embodiment, a computer-implemented method, comprises receiving a selection of a goal for an application on a cluster of compute nodes. The goal includes one or more of a speedup goal, an efficiency goal, a reliability goal, and a service level agreement goal. The application on the cluster is executed. Data associated with the goal is collected. A recommendation to adjust one or more parameters that would allow the goal to be achieved.

System and method for data application performance management

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.

Multiple measurements aggregated at multiple levels of execution of a workload

A technique includes dynamically assigning, by a server, network addresses selected from a plurality of network addresses to network devices of a network based on a schedule. The schedule represents a time during which a given network address is to remain unassigned. The technique includes, based on the schedule, detecting anomalous behavior associated with the network.

Detecting anomolous network activity based on scheduled dark network addresses

Example implementations relate to processing a query of a database and a data stream. For example, a computing device may include a processor. The processor may receive a query associated with at least one of a database and a buffer storing streamed data from a data stream. The database may store database data previously stored in the buffer. The processor may identify a range associated with the query and generate a set of sub-queries including at least one of a buffer sub-query if the range is associated with the streamed data in the buffer and a database sub-query if the range is associated with the database data in the database. The processor may process the set of sub-queries and provide a query result of the query, where the query result is a combination of sub-query results of the set of sub-queries.

Processing query of database and data stream

According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions executable by the processor to access network activity data collected over a time period associated with a plurality of network entities, in which each of the network entities is assigned a distinct internet protocol (IP) address including a network prefix set of bits and a network entity identifier set of bits. The instructions may also cause the processor to generate representations of the network activity data corresponding to the respective network entities and display the generated representations of the network activity data corresponding to the respective network entities on an IP address block map according to the network entity identifier set of bits of the respective network entities.

Display of network activity data

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.

Automatic generation of sub-queries

Processing a data flow graph of a hybrid flow can include a data structure to store metadata for each of a plurality of nodes of a data flow graph of a hybrid flow, processing the data flow graph of the hybrid flow, and defining metadata in the data structure for a particular node affected during processing of the data flow graph of the hybrid flow.

Processing a data flow graph of a hybrid flow

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.

Generating a visualization of a metric at one or multiple levels of execution of a database workload

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.

Optimizing and managing execution of hybrid flows

According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions executable by the processor to: access network traffic data pertaining to data flows among nodes in a network; partition the network traffic data into a plurality of windows; for each of the plurality of windows, aggregate data flows between pairs of nodes; compute a data distribution of each of the aggregated data flows; select a summary structure for each of the aggregated data flows based on the computed data distributions of the aggregated data flows; generate a summary of each of the aggregated data flows using the selected summary structures for the aggregated data flows; and store the generated summaries.

Growing community of inventors

Santa Clara, CA, United States of America

Alkiviadis Simitsis