Abstract: A method includes receiving a plurality of files where each file represents an object. The method further combines selected files of the plurality of files into an aggregation file and stores the aggregation file on low-latency storage media, such as disk storage media. The method represents the aggregation file with a single inode that is stored on the low-latency storage media. The method migrates the aggregate file to high-latency storage media, such as tape storage media, while leaving the single inode representing the aggregation file on the low-latency storage media. By collecting files into larger aggregation files, the number of files archived is significantly reduced and the files archived are of larger size. The reduced number of files and increased size of the files improves archival and retrieval performance due to reduced metadata operation overhead. A corresponding system and computer program product are also disclosed.

Abstract: An artificially intelligent system for generating compliance-related content, the system includes monitoring circuitry that receives regulatory compliance data from published regulatory texts; a taxonomy engine that generates taxonomy-based classifications of the regulatory compliance data, the taxonomy-based classifications having a plurality of modules and compliance requirements within each module.

Abstract: Automated query retry includes selecting a first node of a plurality of nodes of an execution platform in response to a query. The first node is configured with a first version of a database platform. A first execution of the query is performed using the first version of the database platform at the first node. The method further includes detecting the first execution results in a failed execution. A second execution of the query is scheduled. The second execution uses at least one of the first version of the database platform or a second version of the database platform configured at a second node of the plurality of nodes. The second version is released after the first version. A cause of the failed execution of the query at the first node is determined based at least in part on a result of the second execution of the query.

Abstract: There is provided a data processing apparatus including a processor, the processor is configured to: receive one or more sets of data output from at least one data output apparatus via a communication network, each set of the data including sensor data obtained by at least one sensor and first specifying information that specifies the data processing apparatus; determine whether or not the first specifying information included in the received one or more sets of data satisfies a predetermined condition; classify the one or more sets of data determined to satisfy the condition into a same group; and generate and output an output file based on the one or more sets of data classified into the same group.

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: 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: 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: 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.