Abstract: Historical data pertaining to past project is classified into phases and corresponding sub-phases. A plurality of parameters of a software code associated with a given sub-phase and a total time spent in finishing the given sub-phase is determined. A correlation between the plurality of parameters determined for the given sub-phase and the total time spent in finishing the given sub-phase is then found. A learning model is trained based on said correlation. An ongoing project is divided into ongoing phases and corresponding ongoing sub-phases and next phases and corresponding next sub-phases. The learning model is employed to determine a time that is estimated to be required to finish each ongoing phase and the corresponding ongoing sub-phases. The time that is estimated to be required is then compared with an actual time spent on each ongoing phase and the corresponding ongoing sub-phases, to track a progress of the ongoing project.

Abstract: A method for capturing data comprising the steps of (A) handling a call for a first operating system at a storage library, (B) routing the call from the storage library to a controller firmware, (C) sending a response to the call from the controller firmware to the storage library, and (D) storing the response in a data store box for later use by the storage library.

Abstract: A method for capturing data comprising the steps of (A) handling a call for a first operating system at a storage library, (B) routing the call from the storage library to a controller firmware, (C) sending a response to the call from the controller firmware to the storage library, and (D) storing the response in a data store box for later use by the storage library.