Abstract: A computer-implemented method enables workload scheduling in a storage system for optimized deduplication. The method includes determining dynamic correlations of deduplications between workload processes in a prior time window. Workload processes include one or more tasks with defined execution timing parameters. The method further includes determining deduplication ratios based on the correlations of the deduplications between the workload processes. The method further includes scheduling multiple workload processes based on a highest determined deduplication ratio of the determined deduplication ratios.

Abstract: Systems and methods for managing input and output error of a machine learning (ML) model in a database system are presented herein. A set of test queries is executed on a first version of a database system to generate first test data, wherein the first version of the system comprises a ML model to generate an output corresponding to a function of the database system. An error model is trained based on the first test data and second test data generated based on a previous version of the system. The error model determines an error associated with the ML model between the first and previous versions of the system. The first version of the system is deployed with the error model, which corrects an output or an input of the ML model until sufficient data has been produced by the error model to retrain the ML model.

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: 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: 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: Systems and methods for generation and use of document analysis architectures are disclosed. A model builder component may be utilized to receiving user input data for labeling a set of documents as in class or out of class. That user input data may be utilized to train one or more classification models, which may then be utilized to predict classification of other documents. Trained models may be incorporated into a model taxonomy for searching and use by other users for document analysis purposes.

Abstract: A data supply chain can include functional blocks, which can automatically perform stages of data supply chain, which can include data discovery catalog, data contract negotiation, data preparation, data authentication and authorization, data usage and metering, data contract settlement, data disposal and data forensics and reporting. Information from one or more of the stages can be recorded.

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: An online system receives a search query from a user. In response to the request, the online system obtains search results matching the search query and identifies a set of attributes describing a context of the search query. The online system generates a data structure that includes a plurality of search coefficients. The search coefficients are selected based on the identified set of attributes. Some of the search coefficients have conflicting values. The online system traverses the data structure to identify the search coefficients having conflicting values. For each search coefficient having conflicting values, the online system resolves conflicts and determines a value of the search coefficient. The online system ranks search results based on the resolved values of the search coefficients.

Abstract: A computer-implemented method comprises receiving, by a processor, search requests to search a database; receiving a reindex request to reindex information in the database based on a new ontology for the database or a new number of shards for a first index server; initiating, by the processor, generation of a second index for the database while a first index managed by the first index server continues to be used live for responding to received search requests; completing the generation of the second index during a certain period when no search request is sent to the first index server; and in response to the completing, using the second index live for responding to received search requests.

Abstract: Columns of a table are stored in either row-major format or column-major format in an in-memory DBMS. For a given table, one set of columns is stored in column-major format; another set of columns for a table are stored in row-major format. This way of storing columns of a table is referred to herein as dual-major format. In addition, a row in a dual-major table is updated “in-place”, that is, updates are made directly to column-major columns without creating an interim row-major form of the column-major columns of the row. Users may submit database definition language (“DDL”) commands that declare the row-major columns and column-major columns of a table.

Abstract: A method, computer program product, and computer system for maintaining a back pointer from a physical layer block (PLB) to a virtual layer block (VLB) in a multi-level hierarchical file system. A generation number may be maintained in the VLB, wherein the generation number may indicate when data is moved from the PLB to another PLB. An object may be reconstructed in the multi-level hierarchical file system based upon, at least in part, at least one of the back pointer and the generation number.

Abstract: A response to a first query related to a geographic area, and search results responsive to a second query related to the geographic area, are received from a server system. A digital map of the geographic is provided via a user interface. A focus is provided to the first query to illustrate the response on the digital map, where the response defines a geographic context. A subset of the search results is identified based on a relationship to the geographic context. The search results in the identified subset are visually emphasized on the digital map relative to the rest of the search results.

Abstract: Systems and methods implementing a path resolver facilitating client access to a distributed file system. An example method may comprise: receiving, by a processing device, a file identifier comprising a symbolic link; producing, by a user space process being executed by the processing device, a modified file identifier, by replacing a pre-defined sequence of characters within the file identifier by a path to a mount point of a distributed file system; and identifying, in view of the modified file identifier, a data structure comprising information about a file identified by the file identifier.

Abstract: Provided is a device for writing image files into memories, which includes a memory and a processor. The memory is configured to store an instruction. The processor is coupled to the memory, a hardware database and a software database. The processor is configured to access and execute the instruction from the memory to: confirm a production condition of a product, wherein the production condition includes a hardware component characteristic and a software component characteristic; generate an image file by comparing the hardware component characteristic with a plurality of hardware characteristic data in the hardware database, and comparing the software component characteristic with a plurality of software characteristic data in the software database; and write the image file into a product flash memory.

Abstract: A system for performing continuous transaction log backups with minimal resource usage of the client computing devices that are processing the transactions is disclosed. The system detects at least one input/output (I/O) activity at a client computing device. The I/O activity can be associated with at least one database operation performed via the client computing device. The system then executes one or more native commands to backup transactions log data associated with the detected I/O activity to a virtualized location. Backing-up the transactions log data comprises dynamically identifying a mount path location corresponding to the virtualized location, and transferring the transactions log data to the dynamically identified mount path using the one or more native commands. The system can then perform data processing operations (for example, data chunking and deduplicating) on the transactions log data after it is received at the dynamically identified mount path location.

Abstract: Examples described herein relate to a data protection policy management system (DPPMS) for tuning a data protection policy in events of failures. The DPPMS may detect a failure associated with an application that is protected via the data protection policy that includes settings corresponding to parameters comprising one or more of copy tiers, a data retention interval and a recovery point objective (RPO) for the copy tiers. The DPPMS may determine a type of the failure associated with the application. Further, the DPPMS may select, based on the type of the failure, at least one parameter of the parameters to be tuned in response to detection of the failure. Moreover, the DPPMS may tune the settings corresponding to the at least one selected parameter to adapt the data protection policy.

Abstract: A container group is created using a database deployment infrastructure (DI) administrator (HA). API privileges for the container group are granted, using the HA, to a container group administrator (GA). API privileges for a container created in the container group using the GA are granted, using the GA, to a container administrator (CA). API privileges for the container are granted, using the CA, to a container developer (CD). Schema privileges for the container are granted, using the CA, to a container consumer (CC). API privileges for the container group are revoked, using the HA, from the GA. The container group is dropped using the HA.