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This would perform poorly on a typical database, but\nRelational AI has implemented new technology and algorithms that makes this\npossible."}]},{"type":"p","children":[{"type":"text","text":"ML/AI and legacy imperatively coded applications are Black Boxes. The models and\ncode are created by specialists, but are opaque to business people. Our approach\ndramatically reduces lines of codes and declares the requirements in a more\nconcise human readable format."}]},{"type":"p","children":[{"type":"text","text":"The days of people telling the computer step by step how to perform a task are\nbehind us -- we no longer have to describe how we want to win a game, we just\ndeclare that we want to."}]},{"type":"p","children":[{"type":"text","text":"RAI is a cloud-based relational knowledge graph management system, with state of\nthe art probabilistic processing, and declarative reasoning at scale to make\ndeveloping Data Applications a superpower for your business."}]},{"type":"p","children":[{"type":"text","text":"Our language lets developers declaratively model their business problem, define\nand enrich an enterprise scale Knowledge Graph from multiple data sources and\nwith computed knowledge, then query and reason over the graph. Think of our\nlanguage as “executable specifications”, enabling high-bandwidth communication\nbetween domain experts and application developers."}]},{"type":"p","children":[{"type":"text","text":"It is the inevitable next step in the evolution and expansion of the relational\nparadigm and how data applications will be built."}]},{"type":"p","children":[{"type":"text","text":"We believe in the relational paradigm for data storage."}]},{"type":"p","children":[{"type":"text","text":"But most application-centric data models use some variation of 3NF, with wide\ntables designed to answer an application’s specific typical queries. If a\ndifferent application’s workload needs a different schema, the data is copied\nand transformed to suit that second application. Data duplication and silos are\nthe result. And business-level constraints, the rules that define the integrity\nand meaning of the data, are implemented in application-specific code."}]},{"type":"p","children":[{"type":"text","text":"RAI data storage is built around “Graph Normal Form”, GNF, which is Sixth Normal\nForm, 6NF, with the knowledge embodied by the relationships between the tables.\nThis approach allows for clean data to be stored with maximum flexibility for\ncurrent and future data apps."}]},{"type":"p","children":[{"type":"text","text":"If your schema is not organized for a specific application workload, it can\nbetter adapt to unanticipated workloads."}]},{"type":"p","children":[{"type":"text","text":"A business can answer new questions without having to copy and reorganize the\ndata. 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RelationalAI Overview · RelationalAI
Check out highlights of RelationalAI at Snowflake's Data Cloud Summit 2024!
At Relational AI, we have built the first Relational Knowledge Graph Management
System (RKGMS).
We believe relational knowledge graphs are the foundation for data-centric
applications - these are systems that learn, reason, and predict over richly
interconnected data.
With a Relational Knowledge Graph Management System as the foundation diverse
data application workloads can either be learned or declared all grounded on the
same data.
A Relational Knowledge Graph Management System as the foundation for these data
applications minimizes accidental complexity and automatically provides
performance, scalability, incrementality, provenance, and audit trail.
Data Applications built on the RelationalAI platform operate directly against
the relational structure. This would perform poorly on a typical database, but
Relational AI has implemented new technology and algorithms that makes this
possible.
ML/AI and legacy imperatively coded applications are Black Boxes. The models and
code are created by specialists, but are opaque to business people. Our approach
dramatically reduces lines of codes and declares the requirements in a more
concise human readable format.
The days of people telling the computer step by step how to perform a task are
behind us -- we no longer have to describe how we want to win a game, we just
declare that we want to.
RAI is a cloud-based relational knowledge graph management system, with state of
the art probabilistic processing, and declarative reasoning at scale to make
developing Data Applications a superpower for your business.
Our language lets developers declaratively model their business problem, define
and enrich an enterprise scale Knowledge Graph from multiple data sources and
with computed knowledge, then query and reason over the graph. Think of our
language as “executable specifications”, enabling high-bandwidth communication
between domain experts and application developers.
It is the inevitable next step in the evolution and expansion of the relational
paradigm and how data applications will be built.
We believe in the relational paradigm for data storage.
But most application-centric data models use some variation of 3NF, with wide
tables designed to answer an application’s specific typical queries. If a
different application’s workload needs a different schema, the data is copied
and transformed to suit that second application. Data duplication and silos are
the result. And business-level constraints, the rules that define the integrity
and meaning of the data, are implemented in application-specific code.
RAI data storage is built around “Graph Normal Form”, GNF, which is Sixth Normal
Form, 6NF, with the knowledge embodied by the relationships between the tables.
This approach allows for clean data to be stored with maximum flexibility for
current and future data apps.
If your schema is not organized for a specific application workload, it can
better adapt to unanticipated workloads.
A business can answer new questions without having to copy and reorganize the
data. Whereas the application-centric approach leads to duplication and silos,
the data-centric approach leads to treating a dataset as an organization-wide
asset.
Existing machine learning algorithms operate against wide data frames, or
tensors, or matrices, depending on your terminology. These data frames are
extracted and labeled in the feature engineering process. This consumes both
compute and human processing time.
RAI Secret Sauce includes fundamental innovations in this field. We are tapping
into almost 2 decades of profound and under-utilized research by the database
theory and algorithms community. . . . . more on this in future RAI shots!