Oracle International Corporation

Bea Systems, Inc.

Alexandre De Castro Alves

Anand Madihalli Srinivasan

Hoyong Park

Prabhu Thukkaram

Unmesh Anil Deshmukh

Shailendra Mishra

Andrew Piper

Ashish Mahendru

Seth John White

Eric Hsiao

Vikram Shukla

Vitaly Bychkov

Dmitrii Markovskii

Manju James

Parul Jain

Ilya Sergeyevich Shikalov

Natalia Nikiforova

Yogesh Bedekar

Sandeep Bishnoi

Yathish Gatty

Prabish Kumar

Dana Bergen

James Taylor

Anand Srinivasan

Large quantities of data can be processed and/or queried relatively quickly using a combination of continuous event processing and a Map-Reduce algorithmic tool. The continuous event processor can continuously produce real-time results by merging (a) CQL query results from events received since a currently executing Map-Reduce job was started with (b) a most recent query result produced by a most recently completed Map-Reduce job. When the currently executing Map-Reduce job completes, its query result can be stored and made accessible to the continuous event processor, and a new Map-Reduce job can be started relative to event data that has grown in size since the execution of the last Map-Reduce job. The Map-Reduce algorithmic tool provides a convenient mechanism for analyzing and processing large quantities of data.

Integrating event processing with map-reduce

Some event ordering requirements can be determined based on continuous event processing queries. Other event ordering requirements can be determined based on distribution flow types being used to distribute events from event streams to node executing the queries. Events from event streams can be ordered according to ordering semantics that are based on a combination of all of these event ordering requirements. Additionally, virtual computing nodes can be associated with constraints, and computing processors can be associated with capabilities. Virtual computing nodes for processing event streams can be assigned to execute on various computing processors based on both these constraints and capabilities. Additionally, for each of several events in an event stream, a ratio between a total latency and a communication latency can be for determined. Based on an average of these ratios, a quantity of reducing nodes that will be involved in a map-reduce operation can be selected.

Visually exploring and analyzing event streams

Scaling event processing using distributed flows and map-reduce operations

A continuous event processing (CEP) query processor determines how and when a CEP query can be processed in a concurrent manner, such that multiple threads of execution can concurrently perform at least some of the CEP query's operations concurrently with each other. According to one technique, a user can instruct a CEP query processor to attempt to execute a CEP query in a concurrent manner. The CEP query processor responsively determines whether the CEP query's execution, or parts thereof, can be made concurrent based on certain constraints that can depend on inheritance and operation type. Based on this determination, the CEP query processor can execute at least certain parts of a CEP query in parallel relative to the same event within an event stream.

Executing continuous event processing (CEP) queries in parallel

Enriching events with dynamically typed big data for event processing

Techniques for embedded event processing are provided. In some examples, instructions for executing at least a subset of complex event processing features may be received. A stream of events received by the edge computing device may be identified. Additionally, the events of the stream utilizing the subset of complex event processing features may be processed. Further, in some aspects, the processed events may be provided to a complex event platform of a server.

Embedded event processing

An event server adapted to receive events from an input stream and produce an output event stream. The event server uses a processor using code in an event processing language to process the events. The event server obtaining input events from and/or producing output events to a cache.

Event server using caching

Techniques for extending the capabilities of an event processing system to support the processing of spatial data. In one set of embodiments, this extensibility can be provided via a plug-in extension component referred to herein as a spatial data cartridge. The spatial data cartridge can enable the event processing system to support spatial data types (e.g., point, polygon, etc.) and various operations related to such data types (e.g., proximity determinations, overlap determinations, etc.). The spatial data cartridge can also define an indexing scheme that can be integrated with the capabilities of the event processing system to support the indexing of spatial data. Using the spatial data cartridge, the event processing system can operate on spatial data even if spatial data formats are not natively supported by the system.

Spatial data cartridge for event processing systems

A framework or infrastructure (extensibility framework/infrastructure) for extending the indexing capabilities of an event processing system. The capabilities of an event processing system may be extended to support indexing schemes, including related data types and operations, which are not natively supported by the event processing system. The extensibility is enabled by one or more plug-in extension components called data cartridges.

Extensible indexing framework using data cartridges

The concept of faults and fault handling are added to the execution of continuous event processing (CEP) queries. By introducing fault handling techniques to the processing of CEP queries, users are enabled to instruct a CEP query processor to respond to faults in a customized manner that does not necessarily involve the halting of the CEP query relative to which the faults occurred. For example, a fault might be due to a temporary problem. Under such circumstances, the CEP query processor can be instructed to ignore the fault and allow the execution of the CEP query to continue relative to the remainder of the event stream. Alternatively, if the fault is due to a problem with the CEP query itself, then the CEP query processor can be instructed to propagate the fault through the query processing system until the fault ultimately causes the problematic CEP query to be halted.

Handling faults in a continuous event processing (CEP) system

The present invention includes a method for providing parameterized queries in complex event processing (CEP). The method includes providing a query template which includes one or more bind variables, providing sets of parameters corresponding to the one or more bind variables, and parsing the query template to determine positions of the one or more bind variables. The method further includes scanning the provided sets of parameters to determine which of the sets of parameters are to be bound to the one or more bind variables, binding the one or more bind variables which are determined to be bound to the sets of parameters, and substituting the bound one or more bind variables with the corresponding sets of parameters. The method further includes injecting all incarnations of the parameterized queries into the system, and one template/parameterized query is configured to run them all.

Support for a parameterized query/view in complex event processing

A framework for extending the capabilities of an event processing system using one or more plug-in components referred to herein as data cartridges. In one set of embodiments, the data cartridge framework described herein can enable an event processing system to support one or more extension languages that are distinct from the native event processing language supported by the system. For example, certain 'extension language' data cartridges can be provided that enable an event processing system to support complex data types and associated methods/operations that are common in object-oriented languages, but are not common in event processing languages. In these embodiments, an event processing system can access an extension language data cartridge to compile and execute queries that are written using a combination of the system's native event processing language and the extension language.

Growing community of inventors

Doral, FL, United States of America

Alexandre De Castro Alves