Data Streaming Consensus

Unlike static data, streaming data presents unique challenges due to its inherent variability and the rapid pace at which it changes, driven by external factors like market dynamics. Traditional consensus mechanisms, designed for more stable datasets, often fall short in this context, necessitating a more adaptable and responsive solution to maintain data integrity and reliability across the network.

To address these challenges, external developers can create their own data finalization library to create consensus. These libraries are equipped with sophisticated algorithms capable of handling complexities of streaming data, ensuring that despite the fluctuations and timing differences among data Transmitters, consensus can be achieved efficiently.

External Developers have the authority to deploy these libraries for each Stream Data Spotter, essentially tailoring the consensus mechanism to the specific requirements of each data type being transmitted.

Furthermore, these libraries facilitate a voting mechanism, allowing external developers to reward Transmitters who consistently provide accurate and timely data, thereby incentivizing high-quality data transmission within the network.

The finalization library contains a function whose algorithm seeks a result closest to reality based on Transmitter votes, classifying them into those deserving rewards and those not meeting the required level of accuracy.

After data finalization, the "Transmitters Bets" mechanism is activated, processing bets and rewards. Similar to Photon Messaging, if an Agent fails to receive rewards consecutively over an extended period, they are slashed. This ensures participation and integrity within the protocol. Finalized data is then recorded in the "MasterStreamDataSpotter" contract.

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