Abstract: A harvester is disclosed for harvesting metadata of managed objects (files and directories) across file systems which are generally not interoperable in an enterprise environment. Harvested metadata may include 1) file system attributes such as size, owner, recency; 2) content-specific attributes such as the presence or absence of various keywords (or combinations of keywords) within documents as well as concepts comprised of natural language entities; 3) synthetic attributes such as mathematical checksums or hashes of file contents; and 4) high-level semantic attributes that serve to classify and categorize files and documents. The classification itself can trigger an action in compliance with a policy rule. Harvested metadata are stored in a metadata repository to facilitate the automated or semi-automated application of policies.

Abstract: Embodiments of the present invention can comprise systems and methods for managing filesystems and can provide a programmable filesystem with active rules and policies, an n-tier network filesystem, stack mounting, a union filesystem with write-through semantics, a filesystem middleware with selective delegation, a filesystem with a statistical warehouse and/or other management functionality.

Abstract: Embodiments disclosed herein provide a system and method useful for pre-processing non-sentence text extracted from business documents (e.g., malformed bulleted lists, runaway sentence identification, spatially separated data, etc.). One embodiment includes two heuristic algorithms: one searches for sentences in a document and another looks for non-sentences (e.g., lists, tables, tabs, names of people, addresses, etc.) in the same document. In one embodiment, when malformed text is encountered, a particular character (e.g., “?”) is inserted to signify to a natural language processing layer that this set of “words” represent a logical construct and should be evaluated independent of other sentences. Embodiments disclosed herein allow non-sentence text, which is linguistically dry but contextually rich, be included in the natural language processing. Embodiments disclosed herein also facilitate to reduce false positive concept extraction assertions by the natural language processing layer.

Abstract: Embodiments of the present invention can comprise systems and methods for managing filesystems and can provide a programmable filesystem with active rules and policies, an n-tier network filesystem, stack mounting, a union filesystem with write-through semantics, a filesystem middleware with selective delegation, a filesystem with a statistical warehouse and/or other management functionality.

Abstract: Embodiments of the present invention can comprise systems and methods for managing filesystems and can provide a programmable filesystem with active rules and policies, an n-tier network filesystem, stack mounting, a union filesystem with write-through semantics, a filesystem middleware with selective delegation, a filesystem with a statistical warehouse and/or other management functionality.

Abstract: Embodiments of the invention provide methods and systems for defining classes of objects which entails defining and manipulating expressions. A two-tiered classification editor operates to enable a user to define and manipulate expression in real time through a browser-based user interface. The first tier comprises a group editor for enabling a user to define groups. The second tier comprises an expression editor for enabling a user to define and manipulate an expression comprising a set of groups and one or more relationships between the groups. Each group may comprise a set of conditions, each of which may be based on a possible attribute of an object. Via the expression editor, a user can drag and drop to create and modify these expressions on-the-fly. When a user is done manipulating an expression on the second tier, the user is returned to the first tier, which may present the expression as modified.

Abstract: Systems and methods for identifying objects in a managed storage environment with a user and involving the user with policy implementations or decisions associated with these objects are disclosed. These systems and methods may allow a single user identity for the managed storage environment to be assigned to a user and associated with a set of user identities, each of the set of user identities corresponding to the user's identity with respect to a particular domain. Previous or subsequent to establishing a user's enterprise wide identity, data and metadata may be obtained about objects residing in one or more of the domains in the enterprise as described in detail above. Objects within these domains can then be associated with a user using the set of user identities and a report generated for the user based upon these objects, including the policies associated with these objects.

Abstract: Disclosed are systems, methods and apparatuses for managing objects in an enterprise environment according to policies. According to the invention, each policy rule ties a condition that conceptually lives in a repository to an appropriate action(s). Since each condition can be expressed in terms of metadata, policies can be applied and enforced over managed objects (files and directories) by allowing users to program appropriate corresponding action(s) via manipulating metadata stored in a metadata repository, e.g., a policy could prohibit storing MP3 files in corporate storage. In one embodiment, a user can specify a policy rule that ties the condition “no MP3 files in volumes A-Z” to an action “delete MP3 files from volumes A-Z” via a Web-based user interface. A harvester is disclosed for harvesting metadata across file systems.

Abstract: A harvester is disclosed for harvesting metadata of managed objects (files and directories) across file systems which are generally not interoperable in an enterprise environment. Harvested metadata may include 1) file system attributes such as size, owner, recency; 2) content-specific attributes such as the presence or absence of various keywords (or combinations of keywords) within documents as well as concepts comprised of natural language entities; 3) synthetic attributes such as mathematical checksums or hashes of file contents; and 4) high-level semantic attributes that serve to classify and categorize files and documents. The classification itself can trigger an action in compliance with a policy rule. Harvested metadata are stored in a metadata repository to facilitate the automated or semi-automated application of policies.

Abstract: Disclosed are systems, methods and apparatuses for managing objects (files and directories) in network file systems according to policies. Each policy may have one or more rules, each of which ties a condition to an action. Each condition can be expressed in terms of metadata harvested across file systems and stored in a metadata repository. The actions are user-programmable. Users can apply and/or enforce a policy by manipulating the metadata stored in the metadata repository. For example, suppose a policy prohibits storing MP3 files in corporate storage, a user can specify a rule that ties the condition “no MP3 files in volumes A-Z” to an action “delete MP3 files from volumes A-Z.” A file management application may apply a filter to the metadata repository to produce metadata records having values that meet the specified condition and take the corresponding action on managed objects associated with those metadata records.

Abstract: A harvester is disclosed for harvesting metadata of managed objects (files and directories) across file systems which are generally not interoperable in an enterprise environment. Harvested metadata may include 1) file system attributes such as size, owner, recency; 2) content-specific attributes such as the presence or absence of various keywords (or combinations of keywords) within documents as well as concepts comprised of natural language entities; 3) synthetic attributes such as mathematical checksums or hashes of file contents; and 4) high-level semantic attributes that serve to classify and categorize files and documents. The classification itself can trigger an action in compliance with a policy rule. Harvested metadata are stored in a metadata repository to facilitate the automated or semi-automated application of policies.

Abstract: Embodiments of a classification pipeline disclosed herein have the ability to both collect data as it occurs and dynamically redact it, allowing ongoing statistics to be gathered and maintained while simultaneously constraining the total amount of storage capacity that must be dedicated to such a purpose. Various types of information can be extracted from or obtained on the object through the classification pipeline. In one embodiment, the classification pipeline comprises a plurality of layers implemented as a set of services available to network clients through a Web interface or an Applications Programming Interface (API). Each client can subscribe to one or more layers of the classification pipeline at their leisure and tailor their classification pipeline configuration through the interface. The classification pipeline can be configured to collaborate with other software to provide a consistent snapshot of the state of a network environment based on data collected at the time.

Abstract: Embodiments of a classification pipeline disclosed herein have the ability to both collect data as it occurs and dynamically redact it, allowing ongoing statistics to be gathered and maintained while simultaneously constraining the total amount of storage capacity that must be dedicated to such a purpose. Various types of information can be extracted from or obtained on the object through the classification pipeline. In one embodiment, the classification pipeline comprises a plurality of layers implemented as a set of services available to network clients through a Web interface or an Applications Programming Interface (API). Each client can subscribe to one or more layers of the classification pipeline at their leisure and tailor their classification pipeline configuration through the interface. The classification pipeline can be configured to collaborate with other software to provide a consistent snapshot of the state of a network environment based on data collected at the time.