Oracle International Corporation

Samsung Electronics Co., Ltd.

Hoyong Park

Prabhu Thukkaram

Sandeep Bishnoi

Anand Madihalli Srinivasan

Unmesh Anil Deshmukh

Shailendra Mishra

Eric Hsiao

Vitaly Bychkov

Gyorgy Geiszter

Namit Jain

Iuliia Shmeleva

Péter Gordos

Vikram Shukla

Santosh Kumar

Jeffrey Toillion

Alexandre De Castro Alves

Roger Bolsius

Dmitrii Markovskii

Andy Piper

Dmitrii Andreevich Strizhikozin

Junghyun Cho

Seungbo Shim

Dougyong Sung

Pavan L Advani

Myungsun Choi

Kunal Mulay

Namjun Kang

Yathish Gatty

Ilya Sergeyevich Shikalov

Ashok Kumar Dhakar

Devendra Jain

Muthukumar Velusamy

Subhrajyoti Choudhury

Alan Messer

Jinyoung Park

Je-Woo Han

Herbert Stiel

Andrew Piper

Rakesh Komuravelli

Parul Jain

Jin Young Bang

Praveen Kumar

JungHwan Um

Victor Zhu

Sangjean Jeon

Chanhoon Park

Il Sup Choi

Arnab Nandi

Alexander Kotopoulis

Jagdev Dhillon

Iullia Shmeleva

A distributed event processing system is disclosed that receives a batch of events via a continuous data stream and performs the serialization of data in the batch of events. In certain embodiments, the system identifies a first data type of a first attribute for each event in a batch of events and determines a first type of data compression to be performed on data values represented by the first attribute. The system determines a first type of data compression to be performed on data values represented by the first attribute based on the first data type of the first attribute. The system then generates a first set of serialized data values for the first attribute. The system processes the first set of serialized data values against a set of one or more continuous queries to generate a first set of output events.

Data serialization in a distributed event processing system

An event processing system for processing events in an event stream is disclosed. The system can launch a first CQL engine in a cluster of CQL engines using a CQL engine tracking engine. The system can schedule, using the CQL engine tracking engine, the first CQL engine to process a batch of a continuous stream of input events related to an application. The system can track, using the CQL engine tracking engine, the first CQL engine to be scheduled for execution. The system can then execute, using the CQL engine tracking engine, the first CQL engine to process the batch of the continuous stream of input events to generate a set of output events related to the application.

Clustering event processing engines

Techniques for implementing an orchestration service for data replication are provided. In one technique, a recipe is stored that comprises (1) a set of configuration parameters and (2) executable logic, for a data replication operation, that comprises multiple sub-steps. Each sub-step corresponds to one or more configuration parameters in the set of configuration parameters, which includes a first parameter that is associated with a default value and a second parameter that is not so associated. User input that specifies a value for the second parameter is received. The set of configuration parameters is updated to associate the value with the second parameter. The data replication operation is then initiated by processing the executable logic, which processing comprises, for each sub-step of one or more sub-steps, making an API call to a data replication service. In response to each API call, a response is received from the data replication service.

Orchestration service for database replication

Systems and methods for query decomposition for scalability of continuous query processing are disclosed. In some examples, it is determined whether a query is fully stateful. If it is, the query may be divided into two functions, an aggregate function and a global function. The aggregate functions may be distributed across multiple distributed nodes and executed in parallel. The global function may then receive the results from the distributed nodes and join the results to achieve the fully stateful result.

Query decomposition for scalability of continuous query processing

Techniques for processing event streams are provided that use streaming analytics to estimate a target user for dynamic assets. An exemplary technique includes receiving a batch of events that have position data for a potential target, determining one or more polygons that comprise the potential target based on the position data for the potential target, determining an approximate target viewership for the one or more polygons based on a profile or taxonomy of the potential target, associating an advertisement with the one or more polygons based on the approximate target viewership, receiving a continuous stream of events that have position data for a dynamic asset, determining that the one or more polygons include the dynamic asset based on the position data for the dynamic asset, obtaining the advertisement associated with the one or more polygons, and providing the advertisement to a sink for display on the dynamic asset.

Target user estimation for dynamic assets

An event processing system for processing events in an event stream is disclosed. The system can execute instructions to receive a continuous query, apply a transformation to the continuous query to generate a query plan for the continuous query, transform the query plan using a transformation algorithm to generate a transformed query plan, receive a micro-batch stream of input events related to an application, process the input events of the micro-batch stream based at least in part on the transformed query plan to generate a set of output events related to the application, and store the set of output events related to the application in an output queue.

Complex event processing for micro-batch streaming

Systems and methods described herein are directed towards a flexible event ingestion framework. In some examples, an input source comprising information that identifies a plurality of events may be identified. Additionally, in some examples, an adapter for ingesting the information of the input source may be implemented. At least one additional component for modifying the adapter may be received. The adapter may be modified by implementing the at least one additional component with a transport component and a mapper component as part of ingesting the information. Further, a tuple for at least a first event may be generated based at least in part on the modified adapter, and the tuple may be provided to an event server.

Flexible event ingestion framework in an event processing system

An event processing system for processing events in an event stream is disclosed. The system is configured for determining a stage for a continuous query language (CQL) query being processed by an event processing system and/or determining a stage type associated with the stage. The system is also configured for determining a transformation to be computed for the stage based at least in part on the stage type and/or determining a classification for the CQL query based at least in part on a plurality of rules. The system can also be configured for generating a transformation in a Directly Acyclic Graph (DAG) of a data transformation pipeline for the stage based at least in part on the partitioning criteria for the stage. In some examples, the system can also be configured for determining a partitioning of the stage based at least in part on the transformation.

Data partitioning and parallelism in a distributed event processing system

An event processing system for distributing a continuous query, and processing events in an event stream, is disclosed. The system can synchronize timestamps across all partitions by adding a heartbeat propagator component propagating a heartbeat signal for silent partitions. The system can further include a timestamp tracker component that tracks the maximum observed timestamp across all partitions for each batch, and sends this to each partition. The system can distribute partitions of the events to the worker nodes, and instruct a heartbeat propagator component to generate a heartbeat for a first worker node corresponding to an event in a different partition. The system receives outputs from the worker nodes and combines these into a single result corresponding to a continuous query. The system can further implement a timestamp tracker to select a highest timestamp value of a previous batch, and send the value to the worker nodes.

Heartbeat propagation in a distributed stream processing system

Systems and methods for extracting tuples using dynamically generated extractor classes are disclosed. In some examples, an optimized tuple extraction class can be dynamically generated to enable more efficient tuple extraction.

Tuple extraction using dynamically generated extractor classes

A distributed event processing system is disclosed that receives continuous data streams, registers a continuous query against the data streams, and continuously executes the query as new data appears in the streams. In certain embodiments, the distributed event processing system deploys and executes applications (e.g., event processing applications) by distributing the execution of the application on a cluster of machines within the system. In certain embodiments, the system provides users with the ability to specify logical rules in an application. The system processes the logical rules by generating a DAG of transformations representing the logical rules and converts the DAG of transformations into a logical rules Resilient Distributed Dataset (RDD) DAG of transformations. The system processes events in an event batch against the RDD DAG of transformations to generate a set of output results for the user.

Integrating logic in micro batch based event processing systems

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 distributed event processing system is disclosed that receives continuous data streams, registers a continuous query against the data streams, and continuously executes the query as new data appears in the streams. In certain embodiments, the distributed event processing system deploys and executes applications (e.g., event processing applications) by distributing the execution of the application on a cluster of machines within the system. In certain embodiments, the system receive updates to an application, processes the updated application against a continuous stream of data, and continuously generates updated results to a user. In certain embodiments, the system reduces delays associated with application deployment and execution when updates to an application are received. The system enables a user to view results pertaining to an updated application quickly without experiencing the typical deployment and execution delays that occur when updates to an application are made.

Framework for the deployment of event-based applications

Growing community of inventors

San Jose, CA, United States of America

Hoyong Park