Abstract: Systems and methods for supporting both batch processing and streaming data applications, to load and process data incrementally, while providing a near-constantly materialized dataset based on raw blockchain data, are described. For example, the system may receive first on-chain data in a first format via a first input stream, wherein the first on-chain data originates from a blockchain node of a blockchain network. The system may transform the first on-chain data to a second format for storage in a second dataset, wherein the second format comprises an unbounded table.

Abstract: Systems and methods for a unified approach that is compatible with all blockchains, protocols, etc. The systems and methods use a bifurcated indexing system with a dynamically selected application service. The systems and methods bifurcate the indexing process into a storage layer and a compute layer.

Abstract: Systems and methods for creating a reorganization-immune blockchain index using mono-increasing sequence records are described. For example, the system may receive on-chain data for a plurality of blocks, wherein the plurality of blocks comprises a first block comprising a first event of a plurality of blockchain events within the on-chain data. The system may determine a first sequence number for the first event, wherein the first sequence number is based on a mono-increasing sequence record.

Abstract: Methods and systems use a second dataset comprising independently validated data based on labeled blockchain operations previously processed through the blockchain network. The use of the second dataset in conjunction with the dataset comprising labeled blockchain characteristics data received from a plurality of sources provides a comprehensive input for a machine learning model to identify and label suspicious blockchain operations. By doing so, the system mitigates fraudulent, criminal, or suspicious transactions. For example, the system may send an alert to a user if a behavior type is deemed a high risk by the machine learning model; the system may furthermore hold the funds associated with the blockchain operation in a separate account while the transaction is manually reviewed.

Abstract: Systems and methods for supporting both batch processing and streaming data applications, to load and process data incrementally, while providing a near-constantly materialized dataset based on raw blockchain data, are described. For example, the system may receive first on-chain data in a first format via a first input stream, wherein the first on-chain data originates from a blockchain node of a blockchain network. The system may transform the first on-chain data to a second format for storage in a second dataset, wherein the second format comprises an unbounded table.

Abstract: Systems and methods for a unified approach that is compatible with all blockchains, protocols, etc. The systems and methods use a bifurcated indexing system with a dynamically selected application service. The systems and methods bifurcate the indexing process into a storage layer and a compute layer.

Abstract: Systems and methods for creating a reorganization-immune blockchain index using mono-increasing sequence records are described. For example, the system may receive on-chain data for a plurality of blocks, wherein the plurality of blocks comprises a first block comprising a first event of a plurality of blockchain events within the on-chain data. The system may determine a first sequence number for the first event, wherein the first sequence number is based on a mono-increasing sequence record.

Abstract: Systems and methods for a unified approach that is compatible with all blockchains, protocols, etc. The systems and methods use a bifurcated indexing system with a dynamically selected application service. The systems and methods bifurcate the indexing process into a storage layer and a compute layer.

Abstract: Methods and systems that use of a multi-tiered machine learning architecture that aggregates traits of blockchain and off-chain operations. The multi-tiered machine learning architecture then uses this data to generate recommendations related to account identities for communications (e.g., blockchain operations) that occur across decentralized applications.