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Reference: https://iwringer.wordpress.com/2015/10/15/thinking-deeply-about-iot-analytics/

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Experian delivers a digital marketing platform, where CEP plays a key role to analyze in real-time customers behavior and offer targeted promotions. CEP was chosen after careful analysis, primarily for its openness, its open source nature, the fact support is driven by engineers and the availability of a complete middleware, integrated with CEP, for additional use cases.

Eurecat is the Catalunya innovation center (in Spain) - Using CEP to analyze data from iBeacons deployed within department stores to offer instant rebates to user or send them help if it detected that they seem “stuck” in the shop area. They chose WSO2 due to real time processing, the variety of IoT connectors available as well as the extensible framework and the rich configuration language. They also use WSO2 ESB in conjunction with WSO2 CEP.

Pacific Controls is an innovative company delivering an IoT platform of platforms: Galaxy 2021. The platform allows to manage all kinds of devices within a building and take automated decisions such as moving an elevator or starting the air conditioning based on certain conditions. Within Galaxy2021, CEP is used for monitoring alarms and specific conditions.Pacific Controls also uses other products from the WSO2 platform, such as WSO2 ESB and Identity Server.

A leading Airlines uses CEP to enhance customer experience by calculating the average time to reach their boarding gate (going through security, walking, etc.). They also want to track the time it takes to clean a plane, in order to better streamline the boarding process and notify both the air line and customers about potential delays. They evaluated WSO2 CEP first as they were already using our platform and decided to use it as it addressed all their requirements.

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•Core Event Processing Engine

Event Processors

•Publishes events to external systems

Event Publisher

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Transformation FilterComposition/Aggregation/Ana

lytics

Join Multiple Streams Detect Patterns Blacklisting

Build Profiles

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Category Operators

Event Sequencing

e handle out of order events by using a variant of the K-Slack algorithm, which is a well-known solution to handling disorder in event streams, by buffering data until order can be guaranteed.Compensation for missed events is not supported in the current version, but is on the roadmap. Additionally, we can use filtering to reduce noisy events in a stream (based on Kalman filter)

EnrichmentEnrichment is done via two ways: event tables to access historical data from any JDBC data source, and custom extensions to connect to custom source of data, such as files.

Business LogicScripting can be used to add any business logic to any execution plan. JavaScript, Scala and R are supported out of the box. Additional, customers can easily invoke custom logic through their own operators.

Transformation

The filter operator can be used to filter streams on a certain set of conditions, which can be combined via and/or - Conditions can be expressed using mathematical operators, regular expressions, string manipulation and logical operators. Additional , queries allow to select information from input stream, project them to output stream or new stream, and replace certain elements

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Category Operators

Time Windows

Siddhi provides very strong support for time windows, a domain where an SQL-like query language bring much simplicity compared to a programing language. Several types of windows are supported, including sliding and tumbling (batch) windows, time windows starting from a point in time, or CRON-based time windows. Additionally, we support applying streaming processing to events based on the number of events ( length window), the unicity of events or the frequency of events.

Aggregation/CorrelationUsing Join and Pattern operators, we can aggregate and correlate two or more streams of data. Join allows to join events based on condition, while pattern allows to correlate multiple events based on time, logical relationship or event counting.

Pattern Matching

We detect patterns based on temporal order (based on arrival order), logical relationship (based or the logical relationship of 2 events, or counting (to limit the number of events matching the pattern). The pattern may or may not allow events in between the events the condition. If no foreign event is allowed, the sequence operator must be used.

CustomDevelopers can create their own function, operators , time windows and processing operators. The extensions are written in Java. Once implemented the operators can be used as any other out of the box operator or function.

Libraries to support custom operators

Developers use the current operators as reference to develop their own, this is one of the key advantages with open source distribution. We deliver dozens of extensions on GitHub which can be adapted by 3rd parties. At the implementation level, implementing an extension just involves extending a well-defined interface.

Other operatorsWe support more than 100 custom operators on top of the list above, including geographical operators, for location-based applications, time series, math, natural language processing, integration with machine learning models created in PMML or our own Machine Learning product.

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define stream StockStream (symbol string, volume int, price double);

@name(Filter Query’)@dist(parallel= ‘3')from StockStream[price > 75] select * insert into HighPriceStockStream ;

@name(‘Window Query’)@dist(parallel= ‘2')partition with (symbol of HighPriceStockStream)begin from HighPriceStockStream#window.time(10 min) select symbol, sum(volume) as sumVolume insert into ResultStockStream ;end;

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Using Enron Email Data Set

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Protocol Throughput (events/sec)

Standalone 6555

distributed 1 node 3699

distributed: 2 nodes 6627

distributed: 3 nodes 9500

distributed: 4 nodes 11050

•Data Set: • 517,417 emails• mean body size of 1.8KB• largest size 1.92MB

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Using Enron Email Data Set

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Protocol Throughput (events/sec)

Thrift 82137

Binary 245000

Protocol Throughput (events/sec)

Standalone 6555

distributed 1 node 3699

distributed: 2 nodes 6627

distributed: 3 nodes 9500

distributed: 4 nodes 11050

Simple Use Case

•Data Set: • 517,417 emails• mean body size of 1.8KB• largest size 1.92MB

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Guaranteed SLAs Pattern 1 (Hot/warm)

Pattern 2(Persistence)

Pattern 3 (Distributed)

Continuity in the availability of the system even in case of failure

Guarantee that no event is lost.

Stateful queries (for example involving a time window or a sequence), state is not lost despite failures.

Not supported in current version (on roadmap)

Event is treated only once Not fully supported Not supported by Storm

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http://wso2.com/library/articles/2015/01/article-geo-spatial-data-analysis-using-wso2-complex-event-processor-0/

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