Abstract: Systems and methods for concurrently performing multiple searches of a file system based on a plurality of search requests. Each search request belonging to the plurality of search requests has a search root. A common set of search roots is identified from the plurality of search roots. A first window function is generated based upon a first search root belonging to the common set of search roots. Candidates located in a plurality of directory trees are enumerated. Each of the directory trees belonging to the plurality of directory trees begins in a search root in the common set of search roots. The candidates are evaluated based upon a first search criteria and the first window function. And, the candidates that satisfy both the first search criteria and the first window function are reported as a result.

Abstract: One or more techniques and/or systems are provided for implementing a pipeline used to generate, train, test, and implement a document scoring model for assigning document scores to documents. Features from various sources are combined to create a joined page level feature set, a joined domain level feature set, and a host level feature set. Numerical features and content features are extracted from ground truth documents and random documents. The numerical features are joined with the joined feature sets to create a set of joined features. The document scoring model is trained using the set of joined features and a training technique. A document is scored with a document score using the document scoring model based upon the content features and the set of joined features with document scores obtained during training.

Abstract: Techniques are described relating to unstructured document processing. An associated computer-implemented method includes identifying a plurality of deduplicated data blocks associated with a collection of unstructured documents. The method further includes sorting the plurality of deduplicated data blocks in descending order based upon at least one block frequency metric, selecting a highest sorted unprocessed deduplicated data block, applying text analytics to the selected deduplicated data block, and applying at least one result of the text analytics to any document among the collection of unstructured documents including the selected deduplicated data block. The method is terminated responsive to satisfaction of at least one stopping condition.

Abstract: A search method using an artificial intelligence based information retrieval model and a method for training the artificial intelligence based information retrieval model used for the method are provided. In the method, even if there is no labeled data and only a corpus exists, the artificial intelligence based information retrieval model can be trained using the weak-supervision methodology. Search can be performed by dividing documents into passages having short lengths. Compared to an information retrieval model based on unsupervised learning, improved search results are provided.

Abstract: An illustrative pseudo-file-system driver uses deduplication functionality and resources in a storage management system to provide an application and/or a virtual machine with access to a locally-stored file system. From the perspective of the application/virtual machine, the file system appears to be of virtually unlimited capacity. The pseudo-file-system driver instantiates the file system in primary storage, e.g., configured on a local disk. The application/virtual machine requires no configured settings or limits for the file system's storage capacity and may thus treat the file system as “infinite.” The pseudo-file-system driver intercepts write requests and may use the deduplication infrastructure in the storage management system to offload excess data from local primary storage to deduplicated secondary storage, based on a deduplication database.

Abstract: According to principles described herein, a system applies Active Learning methodology to multiple models simultaneously. The system includes a means to distribute the sample selection algorithm across large pools of unlabeled data and a automatic model training deployed on hardware matched to the model type that scales to large volumes of data without consuming all resources.

Abstract: In an embodiment, a cloud server determines a point in time in a current task, and identifies a corresponding point in time in each of a number of completed tasks retrieved from a first database. User activity data from a second database is used to calculate time spent on the current task up to the identified point in time, a ratio between time spent on each completed task after the corresponding point in time and time spent on the completed task before the corresponding point in time. The cloud server also calculates a similarity score between each completed task and the current task based on a number of parameters and using a predetermined algorithm. A weighted arithmetic mean of the time ratios can be calculated using the similarity scores as weights, and can be used in conjunction with the time spent on the current task up to the identified point in time, to calculate time required to complete the current task.

Abstract: A system for identifying similarities between tables and databases stored in a storage server comprises the storage server and a management server. The storage server comprises a first database configured to store a plurality of first database tables, and a second database configured to store a plurality of second database tables. The management server is configured to store a storage index indicating metadata describing a plurality of tables stored at the storage server, wherein the metadata indicates a column name of a column in each of the tables, determine a table similarity score, a database similarity score, and access data, and transmit an instruction to the storage server to delete data stored in at least one of the first database or the source table to eliminate redundancies at the storage server.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: receiving a request for a lock on a page from a virtual database amongst two or more virtual databases, the virtual database including a number of containers respectively corresponding to the same number of database components of the virtual database. A copy of the page is refreshed with a latest copy of the page in an overall cache prior to granting the lock based on ascertaining that the page is not locked by any other virtual database. The virtual database is granted with the lock and have an exclusive access to the page.

Abstract: The present disclosure relates to generating a master group node graph from a plurality of systems of record. Record objects of a system of record corresponding to a data source provider may be accessed. Each record object can have a record object type and identify a group entity. Each record object can be linked to one or more second record objects of a second record object type and identifying a respective member entity. A record object corresponding to a group entity can be identified. A connection profile between the record objects of the group entity and another group entity can be generated. An association between the group node profiles and the connection profile can be stored.

Abstract: Various embodiments of the disclosure disclose a method and an apparatus, which includes: a display, a memory, and a processor operatively connected to the display and/or the memory, wherein the processor is configured to: add a frame to an appended file based on a request to update application data, allocate a reserved space to the appended file, update a database file based on an update condition, and allocate the reserved space to the database file.

Abstract: A method and system for detecting and queuing data stewardship tasks, the method may comprise receiving, at a private database, incoming identity information comprising one or more received data record attributes; comparing the one or more received data record attributes to a plurality of saved data records in the private database. Each saved private data record may include one or more saved attributes. A data stewardship task record is created that contains the received data record attributes and saved data record attributes that are near matches to the received data record. The data stewardship task is displayed to indicate a match quality for each of the received data record attributes and the saved data record attributes. The match quality may be indicated using a color coding scheme that uses a unique color or lack of color to indicate one of a match, a partial match, and no match.

Abstract: A methods, systems, and devices for secure linking and matching of data elements across independent data systems of a first and a second enterprise are disclosed. The system includes a first private data system having a first private database that may include all attributes known to the first enterprise A reference data system having a reference database and is also part of the first enterprise. The reference database may include all known attributes regarding data records contained any data system that is in communication with and/or has access to the reference database. A first set of query attributes may be used determine whether a subject represented by the first set of query attributes associates uniquely with a subject of any private data record stored in the first private database. A similar comparison may be made in the reference database.

Abstract: A method, computer program product, and computer system for controlling, by a computing device, access to a non-volatile memory using a non-volatile lock as a reader of the non-volatile memory. Metadata (MD) non-volatile memory commits may be throttled until capacity of the non-volatile memory is at a threshold capacity.

Abstract: Systems for application configuration backup and restore. Techniques establish high frequency restore points of application configuration metadata. Processing commences upon receiving instructions to periodically take snapshots of application configuration metadata. To avoid incurring unnecessary costs to process snapshots of application configuration metadata when the application configuration metadata is slow-changing, the method performs a low-cost check to get the latest update time of the application configuration metadata. That time is compared to the time of the most recent snapshot of the application configuration metadata. When the time of the latest update time of the application configuration metadata is equal to the time of the most recent snapshot of the application configuration metadata, then rather than incurring the cost of generating a duplicate snapshot, the method waits for a duration without generating a new snapshot.

Abstract: Provided is an edge compute platform (“ECP”) for serving optimized content from local cache or from output of a shared customizable function executed by a compute device at the network edge on behalf of different customer content such that the function is not redundantly deployed for different customer content, and is not be executed each time the same variant of the optimized content is requested. The ECP may canonicalize first transformation parameters of a received original request according to a transformation parameter definition of a particular function that is implicated by the original request, may generate second transformation parameters with a different ordering than the first transformation parameters as a result of the canonicalization, may generate a variant of the original file by inputting the second transformation parameters to the particular function, and may provide the variant in response to the original request.

Abstract: An Artificial Intelligence (AI) based document digitization, transformation and validation system extracts fields from digital documents via different document digitization processes. A document packet with a plurality of documents is initially accessed and any non-digital documents in the document packet are digitized. The errors in the digitized documents are corrected and non-English documents are translated into English. Each of the documents is provided to a plurality of digitization services for the extraction of fields by a plurality of field extraction models. If a field has multiple field instances extracted by more than one digitization service, then a field instance with the highest confidence score is selected for inclusion into the consolidated results. The consolidated results produced in different JavaScript Object Notation (JSON) formats are converted into a common JSON format which may be further validated and provided to downstream processes.

Abstract: With multiple software applications, multiple electronic devices for a user to access and work upon, and multiple potential storage locations for the files it is difficult for a user to remember the identity of a file worked upon, its storage location, or which application they employed. Whilst some applications provide a user with a listing of recently opened files these are only those accessed by that specific application upon a specific device. Embodiments of the invention provide users with a synthesized centralized view of all files accessed irrespective of where the user has stored the electronic content, with which electronic device the electronic content was accessed with, or with which software application the electronic content was accessed with.

Abstract: Techniques are provided for managing objects within an object store. An object is maintained within an object store. In an embodiment, a rule is enforced for the object that in-use slots of the object are non-modifiable and unused slots of the object are modifiable. Metadata of additional information for a slot within the object is attached to the object header. A first application allowed to access user data within the slot is provided access to the user data without being provided access to the metadata. A second application allowed access to the user data and the additional information is provided with access to the user data and the metadata for identifying a location of additional information within the object.

Abstract: An extraction apparatus (10) is the extraction apparatus that extracts a common plan subtree that is a common part among a plurality of plan trees from the plurality of plan trees, and includes a first assignment unit (11) that calculates, based on information of nodes of a plan tree, a hash value for each node to assign the calculated hash value to the corresponding each node, a second assignment unit (12) that assigns a unique number associated with a set of pointers to each node based on the hash value of each node, and a common plan subtree extraction unit (13) that lists subtrees below a node having the same unique number to extract the common plan subtree from the plurality of plan trees.