Oracle International Corporation

International Business Machines Corporation

Eric Hsiao

Hoyong Park

Peter S Lee

Jennie Ching

Jeffrey Toillion

Anand Madihalli Srinivasan

Cris W Sandoval

Vikram Shukla

Santosh Kumar

Simone Geib

Dennis M Chin

Robert Carina

Andy Piper

Amy Yang

Bingqing Shang

Xiaochao Zhou

Rahul V Herwadkar

Warren R Vollinger

Rahul Gupta

Alexandre De Castro Alves

Roger Bolsius

Adriano Covello Santos

Vishal Sharma

Parul Jain

Jianrong Zhang

James Dang

Leon Zeng

Edith H Stern

Barry Edward Willner

Frank Andre Schaffa

Jose R Brunheroto

Andrew Piper

Event processing techniques for updating a database in real time based on events in a continuous event stream are disclosed. The techniques can update the database to incorporate information from thousands of received events per second. The events can include metrics measuring milestones for an organizational process defined by a user. Moreover, multiple streams can include metrics from many tenants concurrently. The techniques include receiving, from a first user device, information identifying a group identifier for a first action object. The techniques then include assigning the identifier to the first action object and to at least one other action object. The techniques then include transmitting, to a service provider, data identifying the assignment of the group identifier, receiving second information identifying events processed by the service provider, identifying events corresponding to the identifier, and generating a user interface configured to present elements corresponding to the identified events.

Techniques for configuring workflow event processing and identifier federation

Event processing techniques for updating a database in real time based on events in a continuous event stream are disclosed. The techniques can update the database to incorporate information from thousands of received events per second. The events can include metrics measuring milestones for an organizational process defined by a user. Moreover, multiple streams can include metrics from many tenants concurrently. The techniques include receiving a plurality of events corresponding to a time interval and identifying that two events share a value of a key attribute. The techniques then include causing to be generated an in-memory combined database record comprising a combination of first and second attribute information from the two events and the value of the key attribute. In response to a trigger signal, the techniques include transmitting a single message, based at least in part on the combined database record, to update the database.

Techniques for in-memory stitching

Techniques for managing real-time business event analysis and monitoring are provided. In some examples, a logical request may be received at a business intelligence server configured with a continuous query service. Additionally, the continuous query service may be configured to translate the logical request into a physical request. Further, business intelligence information may be provided based at least in part on querying against an event data stream with a continuous query configured based at least in part on the physical request translated by the continuous query service.

Real-time business event analysis and monitoring

A data analysis system is provided that enables users to perform complex data analyses based upon data that may be spread across multiple data sources. The data analysis system is configured to generate a combined query that is capable of extracting data from the multiple data sources. The user may provide analysis information describing the analysis the user desires to perform on the extracted data. In response, the data analysis system is further configured to automatically augment the combined query with program or code to implement the user-specified analysis. Execution of the augmented or modified combined query generates an analysis result set resulting from performing the user-specified analysis. The data analysis system provides a flexible and easy-to-use platform for a user, even a non-technical user, to perform complex data analyses using data stored in multiple different data sources.

Automated data analysis using combined queries

Techniques for implementing mechanisms for chaining continuous queries are provided. In some examples, results of a first continuous query may be stored in a data object. For example, the first continuous query may be configured to process business event data and store the results in the data object. Additionally, a second continuous query may then be processed based at least in part on the results stored in the data object.

Mechanism to chain continuous queries

Techniques for managing parameterized continuous query templates are provided. In some examples, a continuous query may be generated based at least in part on one or more selections of one or more business logic templates and business event parameters associated with a user. The continuous query may also be configured to retrieve business event data of the user. In some examples, a service provider may receive selection of one or more business logic templates. The service provider may also receive the business event parameters associated with the user. The business event parameters may be received via the selected template. The continuous query may then be generated using the information received via the template.

Parameterized continuous query templates

Techniques for tracking large numbers of moving objects in an event processing system are provided. An input event stream can be received, where the events in the input event stream represent the movement of a plurality of geometries or objects. The input event stream can then be partitioned among a number of processing nodes of the event processing system, thereby enabling parallel processing of one or more continuous queries for tracking the objects. The partitioning can be performed such that each processing node is configured to track objects in a predefined spatial region, and the spatial regions for at least two nodes overlap. This overlapping window enables a single node to find, e.g., all of the objects within a particular distance of a target object, even if the target object is in the process of moving from the region of that node to the overlapping region of another node.

Tracking large numbers of moving objects in an event processing system

Techniques for managing risks with continuous queries are provided. In some examples, a selection of a data object enabled to provide a real-time stream of business event data may be received. The selection may be received by a user associated with the business event data. Additionally, a continuous query language query may be generated. The query may be configured to monitor the real-time stream of business event data for a first metric. In some examples, the a risk indicator associated with a second metric that affects the first metric may be identified. Output events of a pre-existing continuous query language query may be listened to. Further, a real-time visualization of the first metric and/or the second metric may be provided.

Managing risk with continuous queries

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San Mateo, CA, United States of America

Eric Hsiao