Predicting resource-related failures using multi-dimensional-based machine learning techniques

A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:

• to obtain data pertaining to at least one resource-related activity involving at least one resource and one or more users, wherein obtaining data pertaining to at least one resource-related activity comprises obtaining one or more of user-related data attributed to the one or more users, resource-related data attributed to the at least one resource, data related to one or more actions already performed as part of the at least one resource-related activity, and temporal data associated with the at least one resource;
• to predict one or more failures associated with the at least one resource-related activity by processing at least a portion of the obtained data using one or more machine learning techniques;
• to predict one or more reasons attributed to at least one of the one or more predicted failures by processing the at least a portion of the obtained data using the one or more machine learning techniques;
• and
• to perform one or more automated actions based at least in part on at least a portion of the one or more predicted failures and at least a portion of the one or more predicted reasons.

An apparatus comprising:

• at least one processing device comprising a processor coupled to a memory;
• the at least one processing device being configured:
• to obtain data pertaining to at least one resource-related activity involving at least one resource and one or more users, wherein obtaining data pertaining to at least one resource-related activity comprises obtaining one or more of user-related data attributed to the one or more users, resource-related data attributed to the at least one resource, data related to one or more actions already performed as part of the at least one resource-related activity, and temporal data associated with the at least one resource;
• to predict one or more failures associated with the at least one resource-related activity by processing at least a portion of the obtained data using one or more machine learning techniques;
• to predict one or more reasons attributed to at least one of the one or more predicted failures by processing the at least a portion of the obtained data using the one or more machine learning techniques;
• and
• to perform one or more automated actions based at least in part on at least a portion of the one or more predicted failures and at least a portion of the one or more predicted reasons.