Certus Knowledge Systems (CKS) is a method to engineer organizational goals as well as a measure of value into the data structures and query methodologies of the supporting data system. CKS represents over a decade of thought by our founders on how to apply advanced data engineering directly to customer solutions.
The CKS solution has been deployed to support financial analytics and money laundering workloads, autonomous airborne and submersible systems, open source analysis, and other data analysis end user workloads.
The development of these types of solutions displayed the applicability across multi-domain and discipline requirements. CKS is not a one size fits all solution to all data problems but can be applied generally or specifically as your data challenges grow in specificity.
Certus Group has two standards, ciuTshi and cerDO, as well as licensed software, sKG with sETL under the hood, that implements those standards. In all large scale data projects we apply our data operations framework, ciuTshi, in order to better understand and apply value to the data we integrate.
For those projects in which relationships between data sets are of value we implement ontology driven property graphs utilizing the Certus Data Ontology standard (cerDO) as a guide for those implementations enabling better communication and integration between already created and future knowledge graphs.
In order to implement ontology driven property graphs from multiple datasets at the billion entity scale we implement Certus Group’s Semantic Knowledge Graph (sKG) with our Semantic ETL engine (sETL) under the hood which enables faster integration of data pipelines into a given ontology.
ciuTshi is a simplified collection of data practice modules for establishing and building up your metadata best practices. Understanding that each data challenge is unique, the modular framework’s application can be tailored to develop repeatable data processes for valid models and critical data-driven decisions. Your organization can selectively apply ciuTshi to data management, data governance, content management, and task management challenges using data catalogs and other data operations tools of your choice. The use of ciuTshi will result in a higher quality of data asset and, as a result, improve the value of data-driven decisions derived from solid metrics and advanced data analysis models.
cerDO is a framework for consistently representing and rigorously managing data-derived ontological entities.
This ISO 21838-compliant framework guides engineers, leadership, and all data users toward a shared understanding of their data's connection to real-world entities; facilitating collaboration and longitudinal analysis of organizational data.
Using cerDO results in high-quality institutional knowledge, improving the contextual understanding of data assets, organizational decisions, and their measurable impact. cerDO is one of sKG’s core elements, used to infer ETL steps for disparate data sources, enabling schema representation in scalable datastores, and automatic discovery of relationships among entities.
The Semantic Knowledge Graph (sKG) sKG combines advanced semantic technologies with a powerful mapping framework, allowing for seamless integration and alignment between diverse knowledge graphs.
sKG enables users to effortlessly navigate and query data across multiple knowledge graphs, breaking down information silos and fostering a more interconnected digital ecosystem.
sKG enhances data discovery, integration, and enrichment, empowering users with a holistic view of interconnected knowledge. Researchers, developers, and knowledge enthusiasts can leverage sKG's capabilities to uncover new insights, create innovative applications, and contribute to the collective knowledge of humankind. With sKG, the potential for collaborative exploration and discovery across knowledge graphs is limitless, propelling us towards a more interconnected and intelligent information landscape.
sKG is a scalable (both horizontally and vertically) datastore that supports SQL, graph, and keyword search queries.
sKG also uses a scalable ETL pipeline that supports instrumentation and on-the-fly modifications.