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: 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: Cloud-based systems and methods for visualizing dynamically updated data flows are disclosed, which includes inputting expected data flow defined over a set time period and generating therefrom a default graphical display depicting a default graphical curve having a graphical curve type defined by each of a predefined graphical shape as selected. A unit time period may be selected and overridden, causing the creation or generation of an augmented graphical curve defined by an overridden expected data flow. The augmented graphical curve having an updated portion visually depicting the overridden expected data flow over a time period may be generated as part of a dynamically updated graphical display for visualization on a GUI of the display screen of the client device.

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 data inbox, a project database associated with a project, and instructions that cause the processors to: receive, at the data inbox, an input including data corresponding to the project, wherein the data is formatted in accordance with a non-standardized format; execute the trained ML model to: extract the data from the input, and analyze the data to output (i) a predicted classification and (ii) a predicted impact associated with the project; convert the data to a standardized format based on the predicted classification; store (i) the data and (ii) the predicted impact in the project database; and generate an indication of the data and the predicted impact for display to a user as part of the data inbox.

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: 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.

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.