Abstract: A framework is described that improves resource utilization during operations executing within workflows of the distributed data processing system (e.g., having a plurality of interconnected nodes) in a disaster recovery (DR) environment configured to support synchronous and asynchronous (i.e., heterogeneous) DR workflows (e.g., generating snapshots and replicating data) that include synchronous replication, asynchronous replication, nearsync (i.e., short duration snapshots of metadata) replication and migration of data objects associated with the workflows for failover (e.g., replication and/or migration) to a secondary site in the event of failure of the primary site. The framework meters (regulates) execution of the operations directed to the workloads so as to efficiently use the resources in a manner that allows timely progress (completion) of certain (e.g., high-frequency) operations and reduction in blocking (stalling) of other (e.g.

Abstract: Methods, computer program products, and computer systems for the classification of unstructured data items are disclosed. Such methods, computer program products, and computer systems include ingesting an item into a classification engine, performing term processing on one or more terms of the item, and processing a relational similarity index. The classification engine is implemented in the computer system. The relational similarity index represents a similarity of the item to a reference item, and the relational similarity index is determined using the one or more terms.

Abstract: A framework is described that improves resource utilization during operations executing within workflows of the distributed data processing system (e.g., having a plurality of interconnected nodes) in a disaster recovery (DR) environment configured to support synchronous and asynchronous (i.e., heterogeneous) DR workflows (e.g., generating snapshots and replicating data) that include synchronous replication, asynchronous replication, nearsync (i.e., short duration snapshots of metadata) replication and migration of data objects associated with the workflows for failover (e.g., replication and/or migration) to a secondary site in the event of failure of the primary site. The framework meters (regulates) execution of the operations directed to the workloads so as to efficiently use the resources in a manner that allows timely progress (completion) of certain (e.g., high-frequency) operations and reduction in blocking (stalling) of other (e.g.

Abstract: Various systems and methods are provided for calculating a data criticality score upon ingesting a data object into a data storage system. This data criticality score can be used to control subsequent access requests for the data object. In one embodiment, a computer system receives a data object at a first node comprising a decision engine. The decision engine generates a data criticality score based, at least in part, on one or more inputs related to the data object. After calculating the data criticality score, the system uses the data criticality score to determine whether a given action is allowable for the data object. After determining whether the given action is allowable, the system receives a user request to perform a first action on the data object. The system then determines whether the user request should be granted with respect to the first action, and if allowable, performs the first action.

Abstract: Methods, computer program products, and computer systems for configuration management are disclosed. Such methods, computer program products, and computer systems include determining whether unstructured data or structured data is available for use as a basis to define an associative template, and in response to a determination that unstructured data is available for use as the basis to define the associative template, determining whether the unstructured data is hierarchically organized. In response to a determination that the unstructured data is hierarchically organized, an associative template can then be defined using the hierarchical organization of the unstructured data, where the unstructured data is hierarchically-organized unstructured data (HOUD).

Abstract: The disclosed computer-implemented method for evaluating and storing data items may include (i) receiving a data item to be evaluated and stored, (ii) evaluating the data item by comparing the data item with a set of rules used to determine properties of data items, (iii) storing, in connection with the data item, (a) at least one determined property of the data item and (b) contextual details about a state of at least one rule used to determine the property at a point in time at which the rule was used, and (iv) after the data item has been stored, performing an action on the data item based on the stored contextual details. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Methods, computer program products, and computer systems for configuration management are disclosed. Such methods, computer program products, and computer systems include determining whether unstructured data or structured data is available for use as a basis to define an associative template, and in response to a determination that unstructured data is available for use as the basis to define the associative template, determining whether the unstructured data is hierarchically organized. In response to a determination that the unstructured data is hierarchically organized, an associative template can then be defined using the hierarchical organization of the unstructured data, where the unstructured data is hierarchically-organized unstructured data (HOUD).

Abstract: Various systems, methods, and computer program products are disclosed for performing configuration management functionality on hierarchically-organized unstructured data using associative templates. Such methods, computer program products, and computer systems include identifying a node in the associative template and setting a configuration parameter to a parameter value, based on a template association. The node in the associative template is a node in a hierarchy of an associative template. The identifying indicates a template association between the node in the associative template and a node in a hierarchy of hierarchically-organized unstructured data (HOUD). The parameter value is maintained in the node in the associative template, and the node comprises the configuration parameter.

Abstract: Various automated data flow tracking techniques can involve obtaining metadata identifying the data items from multiple data sources and using that information to identify the relationships among the data items. This information can then be provided to users. For example, a method can involve receiving a query; accessing metadata, which identifies data items generated by multiple data sources; identifying a set of responsive data items from among the available data items; identifying one or more relationships between the data items in the set of responsive data items, and responding to the query with information identifying the set of responsive data items and the one or more relationships.

Abstract: Methods, computer program products, and computer systems for configuration management are disclosed. Such methods, computer program products, and computer systems include identifying an associative template node and setting a configuration parameter to a parameter value, based on a template association. The associative template node is a node in a hierarchy of an associative template. The identifying indicates a template association between the associative template node and a node in a hierarchy of hierarchically-organized unstructured data (HOUD). The parameter value is maintained in the associative template node, and the node comprises the configuration parameter.

Abstract: Multi-dimensional surrogation systems and methods are provided that generate at least one data surrogate using information of data and numerous data changes received from at least one data source. The multi-dimensional surrogation transforms data of each data surrogate from a format of the data source to at least one alternative format. Original metadata is extracted from each data surrogate. New metadata is generated by combining portions of the original metadata and components of each data surrogate. The multi-dimensional surrogation generates an extended data surrogate comprising the data surrogate and the new metadata.

Abstract: Multi-dimensional surrogation systems and methods are provided that generate at least one data surrogate using information of data and numerous data changes received from at least one data source. The multi-dimensional surrogation transforms data of each data surrogate from a format of the data source to at least one alternative format. Original metadata is extracted from each data surrogate. New metadata is generated by combining portions of the original metadata and components of each data surrogate. The multi-dimensional surrogation generates an extended data surrogate comprising the data surrogate and the new metadata.

Abstract: Multi-dimensional surrogation systems and methods are provided that generate at least one data surrogate using information of data and numerous data changes received from at least one data source. The multi-dimensional surrogation transforms data of each data surrogate from a format of the data source to at least one alternative format. Original metadata is extracted from each data surrogate. New metadata is generated by combining portions of the original metadata and components of each data surrogate. The multi-dimensional surrogation generates an extended data surrogate comprising the data surrogate and the new metadata.

Abstract: Multi-dimensional surrogation systems and methods are provided that generate at least one data surrogate using information of data and numerous data changes received from at least one data source. The multi-dimensional surrogation transforms data of each data surrogate from a format of the data source to at least one alternative format. Original metadata is extracted from each data surrogate. New metadata is generated by combining portions of the original metadata and components of each data surrogate. The multi-dimensional surrogation generates an extended data surrogate comprising the data surrogate and the new metadata.