The primary benefit of the Cogynt platform, besides making all of this complexity invisible, is creating, maintaining and understanding the “state” of complex behavioral patterns over all time periods, and at any scale.

Cogynt leverages the most advanced Open-Source Streaming Technologies

Apache Kafka

Provides messaging and persistence backbone

Apache Flink

The stateful compute engine

Apache Druid

A real-time analytics database to support “slice-and-dice”, OLAP) data analytics.

Cogynt provide user interfaces for pattern creation, analytic results, dashboards and team workflow.

 

The Cogynt Authoring Tool
A zero-code authoring environment that allows non-engineers to author hierarchical complex event patterns through a visual interface.   It also provides complete visibility to all of the Kafka topic schemas and provides the means of deploying the authored event patterns directly to Apache Flink for execution.  It also performs basic consistency checks prior to deployment, ensuring the HCEP model is logically consistent and will execute once deployed.

The Cogynt Runtime
Converts the deployed HCEP model into the Flink model representation, a Directed Acyclic Graph (DAG), from which Flink processes all events according to the HCEP pattern design. All HCEP generated events are published back into Kafka for reingestion into higher-level patterns or can be published for visualization and further analysis.

The Analyst Workstation
Is used by the analyst to visualize the analytic results with team workflow support.

COG-Technology-02

What is HCEP?

HCEP is defined by Dr. David Luckham and W. Roy Schulte1, as a meta framework of techniques that includes event filtering, event pattern matching, causal and timing analysis, hierarchical abstraction of events, creation of complex events, and specification of event hierarchies for processing flows of events in real-time and abstracting humanly understandable and actionable information from those event flows.

HCEP is both a top-down hypothesis-driven pattern definition process and a bottoms-up event matching and mapping process.

The principal benefit of HCEP over traditional CEP is that it allows the analyst to address more complex problems that were heretofore unsolvable. HCEP is able to handle the “combinatorial explosion” problem where the number of combinations that would need to be represented becomes too large for a flat CEP system.  HCEP allows aspects of the problem to be addressed separately then combined at a higher level of abstraction so the number of combinations are manageable.

Another benefit to Cogynt’s HCEP platform is that it can incorporate that events did not happen in specific patterns.  In some instances, the non-occurrence of an event can can be just as important as the occurring events.

Finally, Cogynt handles the partially satisfied event problem as well.  By using a Bayesian Belief Network, Cogynt calculates the statistical likelihood of a future event occurring, which enables continuous risk assessments and fine-grained trend analysis.

Elements of Cogynt

Computational Hierarchy

A computational hierarchy is the arrangement of patterns that form a hierarchy, where patterns at lower levels feed higher level patterns, allowing an increase in abstraction and therefore understanding as patterns are populated up the hierarchy.

Stateful CEP

Stateful CEP persists information about the state of ALL pending patterns allowing for incremental evaluation of CEP patterns and to respond to new information (events) in the shortest time, while continuing to track potential patterns over a very long period of time. The persistence of this provides the means to “understand” the entire field of endeavor continuously.

Bayesian Belief Network (BBN)

BBN network determines the statistical likelihood of a future event. This is accomplished by applying Bayesian computations within the hierarchy and propagating those computations up the hierarchical chain determining the likelihood of future events at each level within the hierarchy.

Domain Specific Language

A DSL that defines the logic of hierarchical patterns and the complex event processing. Our DSL is called Event Pattern Constraint Language (EPCL) and it is declarative that requires zero coding graphical UI.

Zero Code. Low Touch.

The Cogynt Authoring Tool is completely graphical and provides the ability to define hierarchical patterns using the EPCL, and maps the source data (Kafka topic schemas) to the patterns.

Resources

Cogynt: Flink without code — Samantha Chan
In our data-driven world, the need for speed has never been greater.
Overview of the Cogynt Event Stream Processing Platform Analytic
Cogility has been developing analytic solutions for the past 10 years.
Enhancing the Human Analytical Process through Complex Event Processing
At Cogility, we’ve been working with complex event processing (CEP) as a foundational part of our data analytics work since our founding.

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