Context-driven data backup and recovery
Disclosed herein are systems, methods, and processes to perform context-driven (or context-based) data backup and recovery operations. A request to perform a backup operation on a dataset is received. Current external context datasets related to the dataset and generated based on prioritization techniques are collected from computing devices. a saved context dataset is generated based on the current external context datasets. The backup operation is performed by storing a backup image that includes at least a portion of the dataset and the saved context dataset.
1. A method of comprising:
receiving a request to restore data, wherein
the request comprises a query;
comparing the query to a current external context dataset, wherein
the comparing generates query results, and
the query results are based on the current external context dataset;
performing a task on the query results, wherein
the task comprises a natural language processing (NLP) problem statement,
the performing generates task results, and
the task results are based on a saved context dataset; and
restoring the data, wherein
the restoring is based, at least in part, on the task results.
2. The method of claim 1 , wherein
the query is associated with a topic.
3. The method of claim 2 , wherein
the current external context dataset was generated by applying at least a first prioritization technique to a dataset,
the current external context dataset comprises a subset of the dataset.
4. The method of claim 3 , wherein
the first prioritization technique is a topic modeling technique,
the topic modeling technique is based, at least in part, on a natural language processing (NLP) methodology, and
the NLP methodology is configured to determine whether the subset is responsive to the topic, at least in part, by applying a lexical analysis technique to the dataset.
5. The method of claim 3 , wherein
the current external context dataset was generated by applying at least a second prioritization technique,
the second prioritization technique is a social network data analysis technique, and
the social network data analysis technique comprises analyzing data associated with one or more users who share a social network with a given user.
6. The method of claim 3 , wherein
the saved context dataset was generated by determining one or more differences between a previous saved context dataset and the current external context dataset.
7. The method of claim 1 , further comprising:
generating the task, at least in part, by parsing the query.
8. A non-transitory computer readable storage medium comprising program instructions executable to perform a method comprising:
receiving a request to restore data, wherein
the request comprises a query;
comparing the query to a current external context dataset, wherein
the comparing generates query results, and
the query results are based on the current external context dataset;
performing a task on the query results, wherein
the task comprises a natural language processing (NLP) problem statement,
the performing generates task results, and
the task results are based on a saved context dataset; and
restoring the data, wherein
the restoring is based, at least in part, on the task results.
9. The non-transitory computer readable storage medium of claim 8 , wherein
the query is associated with a topic.
10. The non-transitory computer readable storage medium of claim 9 , wherein
the current external context dataset was generated by applying at least a first prioritization technique to a dataset,
the current external context dataset comprises a subset of the dataset.
11. The non-transitory computer readable storage medium of claim 10 , wherein
the first prioritization technique is a topic modeling technique,
the topic modeling technique is based, at least in part, on a natural language processing (NLP) methodology, and
the NLP methodology is configured to determine whether the subset is responsive to the topic, at least in part, by applying a lexical analysis technique to the dataset.
12. The non-transitory computer readable storage medium of claim 10 , wherein
the first prioritization technique is a topic modeling technique,
the current external context dataset was generated by applying at least a second prioritization technique,
the second prioritization technique is a social network data analysis technique, and
the social network data analysis technique comprises analyzing data associated with one or more users who share a social network with a given user.
13. The non-transitory computer readable storage medium of claim 10 , wherein
the saved context dataset was generated by determining one or more differences between a previous saved context dataset and the current external context dataset.
14. The non-transitory computer readable storage medium of claim 8 , further comprising:
generating the task, at least in part, by parsing the query.
15. A system comprising:
one or more processors; and
a memory coupled to the one or more processors, wherein the memory stores program instructions executable by the one or more processors to perform a method comprising
receiving a request to restore data, wherein
the request comprises a query,
comparing the query to a current external context dataset, wherein
the comparing generates query results, and
the query results are based on the current external context dataset,
performing a task on the query results, wherein
the task comprises a natural language processing (NLP) problem statement,
the performing generates task results, and
the task results are based on a saved context dataset, and
restoring the data, wherein
the restoring is based, at least in part, on the task results.
16. The system of claim 15 , wherein
the query is associated with a topic.
17. The system of claim 16 , wherein
the current external context dataset was generated by applying at least a first prioritization technique to a dataset,
the current external context dataset comprises a subset of the dataset.
18. The system of claim 17 , wherein
the first prioritization technique is a topic modeling technique,
the topic modeling technique is based, at least in part, on a natural language processing (NLP) methodology, and
the NLP methodology is configured to determine whether the subset is responsive to the topic, at least in part, by applying a lexical analysis technique to the dataset.
19. The system of claim 17 , wherein
the current external context dataset was generated by applying at least a second prioritization technique,
the second prioritization technique is a social network data analysis technique, and
the social network data analysis technique comprises analyzing data associated with one or more users who share a social network with a given user.
20. The system of claim 17 , wherein
the saved context dataset was generated by determining one or more differences between a previous saved context dataset and the current external context dataset.