International Business Machines Corporation

Nuance Communications, Inc.

Trend Micro Inc.

Tivo Corporation

John Matthew Santosuosso

Eric L Barsness

Michael J Branson

Cary Lee Bates

Alexander Cook

David Lee Darrington

James E Carey

Paul Reuben Day

Amanda Peters

Daniel Beuch

Jay Symmes Bryant

Brandon William Schulz

Bradford Lee Cobb

Manuel Orozco

Ryan Kirk Cradick

Jason Allan Nikolai

Christopher R Sabotta

Joseph W Cropper

Randy William Ruhlow

Mahdad Majd

Richard D Dettinger

Brian Robert Muras

Amanda Randles

David M Koster

Jeremy Alan Arnold

Jeffrey Michael Ryan

Jim C Chen

Robert James Crenshaw

Eric John Nelson

Paul S Nelsestuen

Bin Cao

Gilford F Martino

Jeffrey Alan Schmidt

Michael D Pfeifer

Adam D Braham

Robert J Bestgen

Steven Gene Halverson

Vadim Berestetsky

Brian Edward Olson

Robert Russell Nelson

Brian John Cragun

Zachary Adam Garbow

Jonathan Louis Kaus

Richard Alan Diedrich

Lance Christopher Amundsen

Vincent T Timon, Iii

Richard Allen Saltness

Rafal Przemyslaw Konik

William Jon Schmidt

Scott N Gerard

Patrick Joseph McCarthy

Peter John Johnson

Larry Wayne Loen

Peter Andrew Nicholls

Ryan L Rossiter

Michael Brian Brutman

Alexander J Pogue

Michael P Koranda

Raymond Keith Harney

Ray LeRoy Lucas

Kurtis W Ruby

Waheed Sujjad

Michael B Accola

John Joseph Stecher

Shantan Kethireddy

Kevin James Kathmann

Steven Paul Jones

Daniel Rodman Hicks

Chih-Hsiang Chou

Gene R Cobb

Theresa Renee Euler

Sean Eric Babineau

Joel David Eikenhorst

Douglas David Jans

John A Stroh

Erici L Barsness

Donald H Hoerle

Dale Dean Weber

Paul Rueben Day

Brandon W Schultz

Eric L Barness

Daniel R Hill

Paul R Day

Cary L Bates

Gilford Francis Martino

Vincent Thomas Timon

A stream computing application may be configured to manage the flow of tuples through a section of an operator graph. A window may be generated over one or more stream operators. The window may include breakpoint thresholds that set the maximum flow of tuples within the window. The stream operators within the window may be monitored to determine the flow of tuples occurring within the window using tuple flow counts. The tuple flow counts may be compared to the breakpoint thresholds to determine whether a breakpoint condition has occurred. If a breakpoint condition has occurred, a tuple flow change may be implemented to reduce the flow of tuples within the window.

Windowing across operators in a streaming environment

Feedback tuples are implemented in a stream computing environment to provide data flow status information for use in detecting, identifying and/or handling data flow anomalies for a stream computing application.

Feedback tuples for detecting data flow anomalies in stream computing environment

A method, computer program product, and computing device for monitoring network activity associated with streaming a data load through a stream application including a plurality of stream operators deployed on a plurality of computing devices. One or more stream operators with one or more external connections may be identified from the plurality of stream operators. The identified one or more stream operators may be deployed based upon, at least in part, the one or more external connections.

System and method for identifying external connections in a streaming application

Queries are monitored in a database which receives input from a stream computing application to identify data of interest. Parameters defining the data of interest, which are preferably expressed as a logical query, are sent to the stream computing application, which then processes the in-flight streamed data satisfying the parameters in some special manner. In some embodiments, the stream computing application increases the processing priority of in-flight data satisfying the parameters. In some embodiments, the stream computing application applies additional processing steps to the in-flight data satisfying the parameters to provide enhanced data or metadata.

Managing the processing of streamed data in a data streaming application using query information from a relational database

Certain aspects of the present disclosure provide techniques for identifying temporal data in data streams to create a temporal database for a stream(s) application to query for temporal data. An example technique includes receiving streams of data at a streams engine and processing the streams of data according to a priority order. The streams engine identifies whether the database is a temporal database and identifies temporal data in each stream of data based on frame analysis, natural language processing techniques, metadata, and optical character recognition. Further, the streams engine generates context data corresponding to the temporal data. The streams engine generates a temporal data record based on the temporal data and context data, and the streams engine generates a reliability factor. The temporal data record and reliability factor are stored in the temporal database for a stream application to query regarding temporal information at a later point in time.

Integrating relational database temporal tables with a distributed programming environment

In a streaming application receiving streaming data to be processed by a plurality of processing elements (PEs), one of a plurality of upstream PEs that reside on separate compute nodes and transmit data to a multi-input port PE is selected based on a data flow rate measured between at least two components in the plurality of PEs (e.g., an amount of incoming data per unit time or an amount of network bandwidth). At least one operator from the selected upstream PE is redeployed into the multi-input port PE. In some embodiments, the operator is selected from among a plurality of operators fused together in the selected upstream PE, un-fused from the selected upstream PE, and fused into the multi-input port PE. In some embodiments, a dynamic fusing capability is realized by selecting different ones of the plurality of upstream PEs in response to changes in the measured data flow rate.

Autonomic fusion changes based off data rates

Profiling data characterizing a data streaming application is used to predict data which will need to be retrieved by a processing element during execution of the data streaming application. Data is retrieved responsive to the prediction, in advance of actual demand by the processing element which requires it. Prediction may be based at least in part on upstream tuple contents, and could include other historical data retrieval patterns. In some embodiments, retrieval of predicted data may be delayed so that data is retrieved just in time.

Reducing flow delays in a data streaming application caused by lookup operations

A method, system and computer program product are provided for implementing anomaly detection and notification through profiled context. Anomalies are detected by effectively using multiple devices and using predefined profiles to generate a contextual comparison for subtle differences detected. When predefined subtle anomalies are detected using at least one of the predefined profiles, a selected notification is provided.

Method and system for anomaly detection and notification through profiled context

Techniques for optimizing updates to a distributed data processing application are provided. A runtime engine compares a first version of a distributed application to a second, new version of the distributed application. The runtime engine identifies change(s) to the first version. The runtime engine generates compiler hint(s) for the change(s). The second version of the distributed application and compiler hint(s) are sent to a compiler. The runtime engine receives, from the compiler, a compiled second version of the distributed application and compiler-scheduler hint(s) describing respective optimizations the compiler made to the compiled second version of the distributed application based on the change(s) made to the first version of the distributed application and the compiler hint(s). The runtime engine optimizes an update by overlaying the change(s) made to the first version of the distributed application based on the compiler-scheduler hint(s) and the compiled second version of the distributed application.

Optimizing updates to distributed data processing applications

The method can include receiving the first stream of tuples to be processed by the stream operator hosted by one or more computer processors, the stream operator having at least one input port and a condition input port. The method can also include receiving, at runtime, a customized condition at the condition input port of the stream operator. The method can also include monitoring a first group of one or more tuples from the first stream of tuples at a first window of the stream operator. The method can also include determining whether the first group of one or more tuples at the first window fulfills the customized condition. The method can also include deleting, in response to the first group of one or more tuples fulfilling the customized condition, the first group of one or more tuples.

Eviction stream for data joins

Disclosed aspects include the database management system detecting that a first data storage medium includes a first set of data of a database. The first data storage medium has a first access-factor. The database management system detects that a second data storage medium includes a second set of data of the database. The second data storage medium has a second access-factor which differs from the first access-factor. Based on a data storage relationship, the database management system determines a query plan to access both the first set of data with respect to the first data storage medium and the second set of data with respect to the second data storage medium. The database management system establishes the query plan.

Query plan based on a data storage relationship

First and second simulated processing of a stream-based computing application using respective first and second simulation conditions may be performed. The first and second simulation conditions may specify first and second operator graph configurations. Each simulated processing may include inputting a stream of test tuples to the stream-based computing application, which may operate on one or more compute nodes. Each compute node may have one or more computer processors and a memory to store one or more processing elements. Each simulated processing may be monitored to determine one or more performance metrics. The first and second simulated processings may be sorted based on a first performance metric to identify a simulated processing having a first rank. An operator graph configuration associated with the simulated processing having the first rank may be selected if the first performance metric for the simulated processing having the first rank is within a processing constraint.

Selecting an operator graph configuration for a stream-based computing application

Improvements in data security and privacy may be realized by receiving a service request including a social range and a requestor identity; mining a first social network to create a social graph of entities within the social range of the requestor identity, wherein the entities are represented by nodes in the social graph linked to a requestor via one or more edges, wherein a number of edges used to link a given entity to the requestor does not exceed the social range; determining whether the social graph includes at least one person matching personnel included in an organizational chart; and in response to determining that the social graph includes no persons matching the personnel included in the organizational chart, transmitting an acceptance response from a provider-side system to a client-side system.

Obfuscation and routing of sensitive actions or requests based on social connections

A method, system and computer program product for staggering a stream application's deployment, by: providing one or more configuration settings that define one or more delays for instantiation or initialization of at least one target processing element of the stream application based on the stream application's run-time conditions or events; and instantiating or initializing the target processing element of the stream application when the delays defined by the configuration settings have been satisfied. The configuration settings may define the delays to wait until a specified period of time has lapsed after one or more other processing elements have been instantiated or initialized, or to wait until a specified amount of data has been processed by one or more other processing elements, or to wait until one or more signals is received from one or more other processing elements, before instantiating or initializing the target processing element.

Staggering a stream application's deployment

Growing community of inventors

Rochester, MN, United States of America

John Matthew Santosuosso