Lookup and relationship caches for dynamic fetching

A system for decreasing report result times, the system comprising:

• at least one memory storing instructions;
• and
• at least one processor executing the instructions to perform operations, the operations comprising:
• receiving a trained machine learning model trained using training data including, item attributes, attributes having a frequency of change for a respective attribute value below a threshold frequency, and attributes having the frequency of change for the respective attribute value not below the threshold frequency, in which the attributes having the frequency of change for the respective attribute value below the threshold frequency are identified;
• applying the trained machine learning model to automatically identify, among a plurality of item attributes associated with an organization, an attribute having the frequency of change for a respective attribute value below the threshold frequency;
• receiving, at a first time, a first smart cube comprising a value for the attribute having the frequency of change for the respective attribute value below the threshold frequency;
• storing the first smart cube at a local cache;
• initiating a report associated with the organization;
• generating a database query for the report based on initiating the report;
• determining that the report requires the attribute having the frequency of change for the respective attribute value below the threshold frequency and requires another attribute among the plurality of item attributes other than the attribute having the frequency of change for the respective attribute value below the threshold frequency;
• modifying the database query to remove the attribute having the frequency of change for the respective attribute value below the threshold frequency from the database query;
• retrieving a value for the attribute having the frequency of change for the respective attribute value below the threshold frequency from the first smart cube at the local cache;
• executing the modified database query to retrieve a value for the other attribute from a remote database;
• and
• providing a report result.

A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations, the operations comprising:

• receiving a trained machine learning model trained using training data including, item attributes, attributes having a frequency of change for a respective attribute value below a threshold frequency, and attributes having the frequency of change for the respective attribute value not below the threshold frequency, in which the attributes having the frequency of change for the respective attribute value below the threshold frequency are identified;
• applying the trained machine learning model to automatically identify, among a plurality of item attributes associated with an organization, an attribute having the frequency of change for a respective attribute value below the threshold frequency;
• receiving, at a first time, a first smart cube comprising a value for the attribute having the frequency of change for the respective attribute value below the threshold frequency;
• storing the first smart cube at a local cache;
• initiating a report associated with the organization;
• generating a database query for the report based on initiating the report;
• determining that the report requires the attribute having the frequency of change for the respective attribute value below the threshold frequency and requires another attribute among the plurality of item attributes other than the attribute having the frequency of change for the respective attribute value below the threshold frequency;
• modifying the database query to remove the attribute having the frequency of change for the respective attribute value below the threshold frequency from the database query;
• retrieving a value for the attribute having the frequency of change for the respective attribute value below the threshold frequency from the first smart cube at the local cache;
• executing the modified database query to retrieve a value for the other attribute from a remote database;
• and
• providing a report result.