Abstract: A system for generating task schedules using an electronic device includes: a processor, the processor comprising neural networks; a memory coupled to the processor; a scheduler coupled to the processor, the scheduler is configured to: receive: a total work database configured to contain items representing work packages; a resources database configured to contain items representing resources required to fulfill items in the work packages; a constraints database configured to contain items representing constraints to fulfilling items in the work packages; and a scheduling objective database configured to designate a prime objective that is to be achieved by the optimum task schedule; provide a trained reinforcement learning engine for optimizing the task schedule based on inputs from the databases; and generate an optimum work package schedule to sequence the work packages using the trained reinforcement learning engine, wherein the optimum work package schedule maximizes the one or more prime objectives.

Abstract: A method of associating data from a plurality of databases is disclosed. The method comprises accessing a first database comprising a first dataset and a second database comprising a second dataset. The method further comprises identifying a first set of expressions and a second set of expressions corresponding to the first dataset and the second dataset, respectively. The method further comprises determining a first set of clusters and a second set of clusters corresponding to the first database and the second database, respectively. Furthermore, the method comprises creating a relational database based on a set of relationships and a mapping function determined based on the first set of clusters and the second set of clusters.

Abstract: A system for generating task schedules using an electronic device includes: a processor, the processor comprising neural networks; a memory coupled to the processor; a scheduler coupled to the processor, the scheduler is configured to: receive: a total work database configured to contain items representing work packages; a resources database configured to contain items representing resources required to fulfill items in the work packages; a constraints database configured to contain items representing constraints to fulfilling items in the work packages; and a scheduling objective database configured to designate a prime objective that is to be achieved by the optimum task schedule; provide a trained reinforcement learning engine for optimizing the task schedule based on inputs from the databases; and generate an optimum work package schedule to sequence the work packages using the trained reinforcement learning engine, wherein the optimum work package schedule maximizes the one or more prime objectives.

Abstract: A system for generating task schedules using an electronic device includes: a processor, the processor comprising neural networks; a memory coupled to the processor; a scheduler coupled to the processor, the scheduler is configured to: receive: a total work database configured to contain items representing work packages; a resources database configured to contain items representing resources required to fulfill items in the work packages; a constraints database configured to contain items representing constraints to fulfilling items in the work packages; and a scheduling objective database configured to designate a prime objective that is to be achieved by the optimum task schedule; provide a trained reinforcement learning engine for optimizing the task schedule based on inputs from the databases; and generate an optimum work package schedule to sequence the work packages using the trained reinforcement learning engine, wherein the optimum work package schedule maximizes the one or more prime objectives.

Abstract: A method of associating data from a plurality of databases is disclosed. The method comprises accessing a first database comprising a first dataset and a second database comprising a second dataset. The method further comprises identifying a first set of expressions and a second set of expressions corresponding to the first dataset and the second dataset, respectively. The method further comprises determining a first set of clusters and a second set of clusters corresponding to the first database and the second database, respectively. Furthermore, the method comprises creating a relational database based on a set of relationships and a mapping function determined based on the first set of clusters and the second set of clusters.