Ardi Memory

The future of databases

Ardi Graph Database

Graphen's Home-grown Graph Computing Infrastructure written by C++ makes large-scale contextual artificial intelligence possible

The primary purpose of the memory part of the human brain is to provide stored information and guide action. The process of memory formation includes the initial selection and retention of the information, as well as the recollection of it.

When the human brain creates a memory, information is not stored isolated as individual pieces. Instead, the human brain makes connections between different objects and events and remembers these complex relationships. Therefore, to achieve the memory function of the human brain, it is essential to take into consideration the relationships between different objects and events. That’s where Graph Database shows its superiority.

When it comes to complex real-life problems, it is fundamental to gather, store and process information in a connected way. Traditionally, when we process data, it has always been the two-dimensional method, making it impossible for computers and machines to solve any real-life problem beyond simple calculation and automation. Ardi’s Database goes beyond that.


Graph Database vs. Relational Database

SQL requires extensive joint operations to compute neighborhoods of a vertex. Often the number of joints required is proportional with the distance from the source vertex as required by a specific algorithm. Here’s why Graph Database is more superior to relational database.

Relational Database: Finding a neighbor requires joint operations which may become untractable depending on the size of the database.

Graph Database: Finding a neighbor is logarithmic in the size of the database, thus tractable for any depth required.

Ardi Platform supports both proprietary native Graph Database (by C++) and open-source relational database


Graph Database System

Deployed in several largest enterprises in the world, Ardi’s Graph Database System has the following advantages:

  • Graphen home-grown graph computing infrastructure written by the performance-driven C++
  • Terabyte-sized native Graph DB, supports trillions of vertices and edges
  • ACID-compliant and distributed Graph database and analytics
  • Asynchronous job scheduling (both Autonomous ML and Graph DB)
  • Scalable and distributed analytics - modular and expandable through plugins
  • Cluster, replication and high availability for disaster recovery
  • Supports OpenCypher query language
  • Error and event logging, monitoring, backup and recovery
  • Supports both Graph Database and Relational Database

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    Use Case

    Eric is a market analyst working at an art auction house. He uses the Database function of Ardi platform to store relevant information about the artwork, including the number, author, auction price, auction time, etc. Ardi’s Database allows him to manage and store data with provided query commands.

    Like the brain, Graph Database includes:

  • The storage layer contains a high-performance native graph store, a process store and a storage engine.
  • The analytics layer has several computation modules utilizing Ardi’s graph analytical and machine learning algorithms.
  • The query layer includes all the services needed to synchronously or asynchronously load data, invoke algorithms, and retrieve data and results.
  • The visualization layer interactively visualizes raw graph data or computation results at the user interface.
  • The high availability proxy serversguarantee continuous system operations. Communications between layers are achieved via standardized APIs.
  • Communications between layers are achieved via standardized APIs.

    With the help of Ardi, Eric can organize the artwork information more logically. He can easily inquire about the artistic characteristics of a certain author, the changing trend of the price of a certain piece of artwork and the changes in people's preference. Therefore he’s able to make more informed judgment on the market.


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