View source for 18046603. CYBER RECOVERY FORENSICS KIT - EFFICIENT EVENT SEARCHING OVER PITS simplified abstract (Dell Products L.P.)

=CYBER RECOVERY FORENSICS KIT - EFFICIENT EVENT SEARCHING OVER PITS=


==Organization Name==

[[:Category:Dell Products L.P.|Dell Products L.P.]]


[[Category:Dell Products L.P.]]

==Inventor(s)==

[[:Category:Ofir Ezrielev of Beer Sheva (IL)|Ofir Ezrielev of Beer Sheva (IL)]][[Category:Ofir Ezrielev of Beer Sheva (IL)]]

[[:Category:Jehuda Shemer of Kfar Saba (IL)|Jehuda Shemer of Kfar Saba (IL)]][[Category:Jehuda Shemer of Kfar Saba (IL)]]

[[:Category:Amihai Savir of Newton MA (US)|Amihai Savir of Newton MA (US)]][[Category:Amihai Savir of Newton MA (US)]]

==CYBER RECOVERY FORENSICS KIT - EFFICIENT EVENT SEARCHING OVER PITS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18046603 titled 'CYBER RECOVERY FORENSICS KIT - EFFICIENT EVENT SEARCHING OVER PITS

==Simplified Explanation==

The method described in the abstract involves searching for a specific event in data associated with a particular PIT, restoring the data, running a production system copy using the data, and taking backups of the data until the event is located.

* Searching a group of PITs for an event indicator
* Identifying the PIT with the event indicator
* Restoring the data associated with the identified PIT
* Running a production system copy using the restored data
* Taking increasingly granular backups of the data while the production system copy is running

== Potential Applications ==
This technology could be applied in data recovery and troubleshooting scenarios where locating specific events or issues within a dataset is crucial.

== Problems Solved ==
This method helps in efficiently identifying and resolving data corruption or other problems within a dataset by systematically searching and analyzing the data.

== Benefits ==
The method allows for targeted data restoration and backup processes, reducing the time and resources needed to locate and address specific events or issues within a dataset.

== Potential Commercial Applications ==
This technology could be valuable for companies that deal with large volumes of data and need effective methods for data recovery and problem-solving.

== Possible Prior Art ==
One possible prior art for this method could be traditional data backup and recovery processes that involve systematic data restoration and analysis to identify and address issues within a dataset.

=== What are the specific indicators used to identify the occurrence of an event in the data? ===

The abstract does not provide specific details on the indicators used to identify the occurrence of an event in the data. Further information on the types of indicators or criteria used in this method would be helpful in understanding the process better.

=== How does the method determine the granularity of the backups taken during the event search process? ===

The abstract mentions taking increasingly granular backups of the data while the production system copy is running, but it does not elaborate on how the method determines the level of granularity for these backups. Understanding the criteria or factors that influence the granularity of the backups would provide insights into the efficiency and effectiveness of the process.


==Original Abstract Submitted==

A method includes searching a group of PITs, identifying one of the PITs as having an indicator of an occurrence of an event involving data associated with the identified PIT, restoring the data, running a production system copy using the data, and while the production system copy is running, taking increasingly granular backups of the data until the event is located. The event may be a corruption of the data, or other problem.



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