Abstract: A data model is derived from transaction data. The model is represented in a combination data structure for a tree and a hash table. The hash table provides direct access to leaves of the tree, each leaf comprises a frequency count for a particular unique basket of items detected in the transaction data. Mining the combination data structure does not require recursive traversal of the tree. Moreover, derivation is performed with just two passes on the transaction data, during each pass multiple concurrent reducer tasks handle a unique portion of the transaction data providing parallel processing during creation and derivation which improves the processor elapsed time to complete the combination data structure. Furthermore, updates to the data structure are incremental without requiring any additional passes on the original transaction data and without requiring full traversal of the tree. Output from the mining is provided as input to predictor services.

Abstract: A data model is derived from transaction data. The model is represented in a combination data structure for a tree and a hash table. The hash table provides direct access to leaves of the tree, each leaf comprises a frequency count for a particular unique basket of items detected in the transaction data. Mining the combination data structure does not require recursive traversal of the tree. Moreover, derivation is performed with just two passes on the transaction data, during each pass multiple concurrent reducer tasks handle a unique portion of the transaction data providing parallel processing during creation and derivation which improves the processor elapsed time to complete the combination data structure. Furthermore, updates to the data structure are incremental without requiring any additional passes on the original transaction data and without requiring full traversal of the tree. Output from the mining is provided as input to predictor services.

Abstract: Embodiments of the present disclosure provide methods, systems, and computer program products that facilitate the creation, sale, transfer, and exchange of tokens with a backing component on a platform. The method includes obtaining a digital asset for conversion to a token. The digital asset is a non-fungible token (NFT). The method also includes collecting a backing component for the digital asset and creating a smart contract associated with the digital asset. The smart contract links the backing component for the digital asset. The method further includes generating the token via tokenization of the digital asset and the smart contract. Embodiments of the disclosure include token that include backing components that are physical assets and banking mechanisms that are retrievable when predetermined criteria are achieved.

Abstract: Systems and methods for analyzing student retention rates are disclosed. The systems and methods disclosed construct networks of students based on data associated with financial transactions conducted by those students. The systems and methods analyze the networks of students to calculate network features associated with the networks and utilize those network features to forecast student retention. The network features analyzed include node appearance metrics, degree metrics, and edge metrics. The systems and methods may also utilize campus integration metrics calculated from data associated with financial transactions conducted by students to forecast student retention.