Definitions
Rivalz storage concepts and terminologies
Peer to Peer Network
Rivalz utilizes a highly efficient P2P network architecture based on the Chord algorithm. Chord is a protocol for a peer-to-peer distributed hash table (DHT). A distributed hash table stores key-value pairs by assigning keys to different computers (known as “nodes”); a node will store the values for all the keys for which it is responsible. Chord specifies how keys are assigned to nodes, and how a node can discover the value for a given key by first locating the node responsible for that key. The Chord protocol enables the creation of a structured overlay network, allowing nodes to locate and retrieve vector data within the network efficiently.
Rivalz node types
Storage worker Nodes: rNode
Rivalz’s rNodes (storage nodes) are a fundamental component, responsible for storing and managing raw data samples in their original formats (JPEG, MP4, MP3, PDF, etc) and vectorized-encrypted data sets
Data processors and validator Nodes: zNodes
In Rivalz, zNodes play a crucial role in handling data processing, querying, and retrieval requests from clients. They act as intermediaries between the client applications and the rNode network, facilitating efficient and secure data access.
Semi-Fungible Token
Semi-fungible tokens (SFTs), or ERC-3525, is a relatively new token standard, introduced on the Ethereum, combining uniqueness like NFTs (i.e. ERC-721 non-fungible tokens) divisibility like fungible tokens (i.e. ERC-20 tokens). The key features of ERC-3525:
AI Query Capabilities
Normally, SQL queries are for programmers or analysts with professional skills. Recently, thanks to generative AI ( genAI) advancement, everyone can prompt a genAI client to query data samples, and visualize them as charts or tables. Rivalz offers AI query capabilities that allow users to perform simple to complex queries directly on the stored vector data. Rivalz enables efficient similarity search, pattern matching, and anomaly detection on vector data such as voice recordings and DNA sequences by integrating machine learning algorithms and indexing techniques.