Abstract: Techniques for optimizing project data storage are disclosed. An example system includes processors and memories storing a machine learning (ML) model and instructions that cause the processors to: execute the ML model to determine a predicted data category mapping for a first data category to a normalized data category, execute a set of instructions to: input the first data category into a first table, collapse the first table with a second table by adjusting identification values associated with (i) the first data category or (ii) a second data category in the second table into a path value that references both the first data category and the second data category and is stored in a third table, and store the third table in a project database. The third table has a file size that is less than a combined file size of the first table and the second table.

Abstract: Techniques for improving project data storage by generating standardized data storage templates are disclosed. An example system aggregates project data corresponding to at least two projects that is in a non-standardized format, extracts text data from the project data, and inputs the text data and an input prompt into a first machine learning (ML) model configured to extract one or more services indicated by the text data. The example system further generates a text embedding for each service and applies a second ML model to (i) the services and (ii) the text embeddings. Applying the second ML model includes: clustering the services and the text embeddings into a set of clusters, and generating, based on the clusters, at least one data storage template indicating a standardized set of services in a standardized format for projects with associated project data included in one or more clusters.

Abstract: A real estate development and construction cost and risk management system is disclosed. The real estate development and construction cost and risk management system has a real estate development and construction cost and risk management module, comprising computer-executable code stored in non-volatile memory, a processor, and a communication device. The real estate development and construction cost and risk management module, the processor, and the communication device are configured to receive an invoice data flow, receive a commitments data flow, receive a budget transactions data flow, extract data from the invoice data flow, the commitments data flow, and the budget transactions data flow, update a data set using the data extracted from the invoice data flow, the commitments data flow, and the budget transactions data flow, and generate a plurality of reports based on the updated data set.

Abstract: Techniques for optimizing project data storage are disclosed. An example system includes processors and memories communicatively coupled with the processors storing a trained machine learning (ML) model, a nesting data module, a project database, and instructions that cause the processors to: receive a first data category corresponding to a project, execute the trained ML model to determine a predicted data category mapping for the first data category, execute the nesting data module to: input the first data category into a first table having a first file size, collapse the first table with a second table that includes a second data category that is related to the first data category to generate a nested table, and store the nested table in the project database. The nested table has a file size that is less than a combined file size of the first table and the second table.