Abstract:
The disclosed technology can receive a voice query or text query in a natural language and translate it from natural language to a native database management language to respond to the query. For example, a human can ask his or her computer to “show large emails from December 2016”, and a data agent on computer can receive the voice request, convert audio associated with the voice to words in natural language, convert natural language into a SQL query, and convert the SQL query into a database management query. The data agent is trained with a corpus of technical documents and rules to determine the intent or keywords for answering the query. In some implementations, the disclosed technology can also include a chatbot and/or administrative assistant to enable a human to interface with a database management software using voice or text. In some implementations, the disclosed technology allows the user to automatically connect to a help desk technician to assist in completing the query.
Abstract:
This application relates to targeted search of backup data. A data storage system can provide a targeted search of backup data based on events associated with the backup data. Upon receiving a search query that identifies an event stored in an event database, the data storage system can determine an event location and an event time associated with the identified event by accessing the event database and output a search result including a portion of the backup data that is associated with the event location and the event time.
Abstract:
Certain embodiments described herein relate to an improved disk usage growth prediction system. In some embodiments, one or more components in an information management system can determine usage status data of a given storage device, perform a validation check on the usage status data using multiple prediction models, compare validation results of the multiple prediction models to identify the best performing prediction model, generate a disk usage growth prediction using the identified prediction model, and adjust the available space of the storage device according to the disk usage growth prediction.
Abstract:
Described herein are techniques for better understanding problems arising in an illustrative information management system, such as a data storage management system, and for issuing appropriate alerts and reporting to data management professionals. The illustrative embodiments include a number of features that detect and raise awareness of anomalies in system operations, such as in deduplication pruning operations. Such anomalies can include delays in the processing of archive files to be deleted and/or delays in the generation of the list of archive files to delete. Anomalies are characterized by frequency anomalies and/or by occurrence counts. Utilization is also of interest for certain key system resources, such as deduplication databases, CPU and memory at the storage manager, etc., without limitation. Predicting low utilization periods for these and other key resources is useful for scheduling maintenance activities without interfering with ordinary deduplication pruning operations and/or other data protection jobs.
Abstract:
According to certain aspects, a system can include a client computing device configured to: in response to user interaction, store an identifier associated with a first tag in association with a first file; and in response to instructions to perform a secondary copy operation, forward the first file, a second file, and the identifier associated with the first tag. The system may also include a secondary storage controller computer(s) configured to: based on a review of the identifier associated with the first tag, identify the first file as having been tagged with the first tag; electronically obtain rules associated with the first tag; perform on the first file at least a first secondary storage operation specified by the rules associated with the first tag; and perform on the second file at least a second secondary storage operation, wherein the first and second secondary storage operations are different.
Abstract:
The data storage system according to certain aspects can filter secondary copies of data (e.g., backups, snapshots, archives, etc.) generated by multiple client computing devices into a single, filtered, global reference copy. A reference copy may be a filtered view or representation of secondary storage data in a data storage system. A reference copy may include a data structure that includes references to a subset of secondary storage data that meets certain filtering criteria. The filtering criteria may be specified by users according to user preference. Data included in a reference copy may be stored in native format (e.g., format of the application that generated the data) and be accessible through the application associated with the data.
Abstract:
Systems and methods for storage pruning can enable users to delete, edit, or copy backed up data that matches a pattern. Storage pruning can enable fine-grain deletion or copying of these files from backups stored in secondary storage devices. Systems and methods can also enable editing of metadata associated with backups so that when the backups are restored or browsed, the logical edits to the metadata can then be performed physically on the data to create a custom restore or a custom view. A user may perform operations such as renaming, deleting, modifying flags, and modifying retention policies on backed up items. Although the underlying data in the backup may not change, the view of the backup data when the user browses the backup data can appear to include the user's changes. A restore of the data can cause those changes to be performed on the backup data.
Abstract:
Certain embodiments described herein relate to an improved disk usage growth prediction system. In some embodiments, one or more components in an information management system can determine usage status data of a given storage device, perform a validation check on the usage status data using multiple prediction models, compare validation results of the multiple prediction models to identify the best performing prediction model, generate a disk usage growth prediction using the identified prediction model, and adjust the available space of the storage device according to the disk usage growth prediction.
Abstract:
This application relates to targeted search of backup data. A data storage system can provide a targeted search of backup data using facial recognition. Upon receiving a search query that identifies a specific user whose profile is stored in a user database, the data storage system can retrieve a profile photograph of the specific user, perform facial recognition on photographs included in the backup data, and output a search result including a set of photographs in the backup data that match the facial features of the specific user.
Abstract:
An information management system is provided herein that uses machine learning (ML) to predict what data to store in a secondary storage device and/or when to perform the storage. For example, a client computing device can be initially configured to store data in a secondary storage device according to one or more storage policies. A media agent in the information management system can monitor data usage on the client computing device, using the data usage data to train a data storage ML model. The data storage ML model may be trained such that the model predicts what data to store in a secondary storage device and/or when to perform the storage. The client computing device can then be configured to use the trained data storage ML model in place of the storage polic(ies) to determine which data to store in a secondary storage device and/or when to perform the storage.