The technology under the hood.
Cogynt’s analytic core is HCEP (Hierarchical Complex Event Processing).
A technology that translates combinations of low level of events, or observations, into higher levels of abstraction, yielding behavioral indicators that can lead to actionable intelligence.

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 Pinot
A real-time distributed OLAP database designed for low-latency query execution

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.

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
Stateful CEP
Bayesian Belief Network
Domain Specific Language

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
- White Paper
Complex Event Processing As a Behavioral Analytic
- White Paper
Cogynt – Continuous Intelligence Explained
- Blog
Barnaby Meadows joins Cogility as Head of Marketing
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