Abstract: The present application describes a system and method for converting a natural language query to a standard query using a sequence-to-sequence neural network. As described herein, when a natural language query is receive, the natural language query is converted to a standard query using a sequence-to-sequence model. In some cases, the sequence-to-sequence model is associated with an attention layer. A search using the standard query is performed and various documents may be returned. The documents that result from the search are scored based, at least in part, on a determined conditional entropy of the document. The conditional entropy is determined using the natural language query and the document.

Abstract: In an approach for optimizing resources in a disaster recovery cleanup process, processors are configured for receiving transaction entries represented by transaction identifiers at a source database in communication with target databases via Synchronous-to-Asynchronous Traffic Converters (SATCs).

Abstract: A system for simulating a dataset based on sample statistics, and generating pipeline instructions for a database using the simulated dataset, is disclosed. The system may be a cloud-based platform. The system can provide improved performance and security while designing and deploying Extract-Transform-Load (ETL) database pipelines to manage on-premises data. The system receives, from a remote client, sample statistics characterizing a distribution of data corresponding to a column of a database table. The system can generate, based on the received sample statistics, a simulated dataset representing the column of the database table. The simulated dataset may be generated using pseudo-random data values from a distribution with statistics matching the sample statistics. The system can build a simulated database relation based at least in part on the simulated dataset. The system can generate and deploy pipeline instructions using the simulated database relation.

Abstract: A method and apparatus of converting a schema in a deep learning framework, an electronic device, and a computer storage medium are provided. The method of converting the schema in the deep learning framework includes: updating a first schema, based on first syntax elements in the first schema and a context relationship between the first syntax elements in the first schema, so as to obtain an updated first schema; generating second syntax elements corresponding to updated first syntax elements in the updated first schema, based on a mapping relationship between the updated first syntax elements in the updated first schema and second syntax elements in a second schema system; and combining the second syntax elements according to a context relationship between the updated first syntax elements, so as to generate a second schema.

Abstract: Described herein is a system and method for performing an updating of child records associated with a parent record within the specified time period, wherein the parent record has a value including a sum of values present in the associated child records, wherein a row lock on the child records is obtained, and the update is performed, and wherein such an update does not require a row lock on the associated parent record, wherein the parent record is periodically updated within the specified time period.

Abstract: A method for managing file based backups (FBBs) includes obtaining, by a backup agent, a backup request for a FBB, in response to the backup request, generating a FBB, generating a FBB metadata file corresponding to the FBB, wherein the FBB metadata file comprises a set of attribute regions, performing, using the set of attribute regions, a deduplication on the FBB metadata file to obtain a deduplicated FBB metadata file, and storing the deduplicated FBB metadata file in a backup storage system.

Abstract: Techniques for processing data may include: receiving a data block stored in a data set, wherein a hash value is derived from the data block; determining, in accordance with selection criteria, whether the hash value is included in a subset; responsive to determining the hash value is included in the subset, performing processing that updates a table in accordance with the hash value and the data set, and determining, in accordance with the information in the table, whether to perform deduplication processing for the data block to determine whether the data block is a duplicate of another stored data block. The table may include an entry for the hash value. The entry may include information identifying data sets referencing the data block and, for each of the data sets, may specify a reference count denoting a number of times the data set references the data block.

Abstract: Systems and methods of predicting documentation associated with an encounter between attendees are provided. For instance, attendee data indicative of one or more previous visit notes associated with a first attendee can be obtained. The attendee data can be inputted into a machine-learned note prediction model that includes a neural network. The neural network can generate one or more context vectors descriptive of the attendee data. Data indicative of a predicted visit note can be received as output of the machine-learned note prediction model based at least in part on the context vectors. The predicted visit note can include a set of predicted information expected to be included in a subsequently generated visit note associated with the first attendee.

Abstract: Data is processed iteratively by a database system with a first cache storing key-value data which resulted from previous iterations of processing input data and a second cache storing aggregated data which resulted from previous iterations of processing key-value data stored in the first cache. In a current iteration, the database system receives further input data related to the input data of the previous iterations, transforms the further input data into further key-value data and stores the further key-value data in the first cache in addition to the stored key-value data which resulted from previous iterations. The database system further processes the further key-value data and the aggregated data stored in the second cache to form updated aggregated data, and stores the updated aggregated data in the second cache for usage in further iterations. The database system also provides the updated aggregated data to at least one client.

Abstract: Time-based partitioning of a data set is applied to capture updates to the data set in a copy of the data set. Items that have been updated in a data set with in a time period are identified. Partitions of the data set that include the updated items are created according to a partitioning scheme. The created partitions are grouped in a storage location for the time period in a file structure that stores a copy of the database. A latest version of the copy of the data set may be accessed according to latest partitions of the data set stored in the different locations of the file structure.

Abstract: Embodiments of the present invention are directed to facilitating distributed data processing for machine learning. In accordance with aspects of the present disclosure, a set of commands in a query to process at an external computing service is identified. For each command in the set of commands, at least one compute unit including at least one operation to perform at the external computing service is identified. Each of the at least one compute unit associated with each command is analyzed to identify an optimized manner in which to execute the set of commands at the external computing service. An indication of the optimized manner in which to execute the set of commands and a corresponding set of data is provided to the external computing service to utilize for executing the set of commands at the external computing service.

Abstract: A descriptive media content search solution is provided to allow a user to search for media content that better matches a user's descriptive search request. The descriptive media content search solution utilizes an extensive catalog of playlists each having a playlist description, such as a playlist title or other descriptive text, and identifies additional descriptive information for media content items to be searched. The descriptive media content search solution can set up a descriptive search database and utilize the descriptive search database to conduct a descriptive search responsive to the user's descriptive search request.

Abstract: A dynamic schema service (DSS) stores and vends data schema for use by one or more client services. Each schema includes one or more data fields that describe related entities. The DSS also stores meta-schema, which defines limits for field definitions in the schema and other constraints. Using the DSS, a service owner defines fields and constraints unique to its business application and can revise those fields and constraints over time. When the client service requires a schema, the client service sends a request to the DSS, and the DSS responds with the current version of the schema for use by the client service. Service owners are able to edit their schemas in DSS, and the changes are immediately implemented in the corresponding services by virtue of the schema request to DSS. In some embodiments, the DSS employs a hierarchical graph structure to determine the requested schema.

Abstract: There is provided a method for compressing a first tree data structure. The method includes determining, by a processor, to compress a first tree data structure associated with a dictionary of a database management system. The method further includes compressing the first tree data structure to generate a compressed tree data structure. The compressing includes traversing, by the processor and in response to the determining, the first tree data structure on a lowest level. The compressing further includes identifying, by the processor and in response to traversing, empty nodes on the lowest level. The compressing further includes removing the identified empty nodes to compress the lowest level. The compressing further includes constructing, in response to the removing, a second level of the compressed tree data structure based on the compressed lowest level, the second level higher in the compressed tree data structure than the compressed lowest level.

Abstract: Computer-readable media, methods, and systems are disclosed for storing and analyzing dynamic data within a semantic data store. The dynamic data comprises one or more types of data having a normalized data schema. A dynamic data manager interfaces with the semantic data store to instruct storage of the data. The data may be received through an event service from either of an external data source or an internal data source.

Abstract: A transposable identity enchainment system processing digital entities of each of users, data and processes equivalently and comprising a recombinant access mediation system, a associational process management system, a topical capability mediation system, and a multi-dimensional enchainment system that enchains aspects of entity identities via mediated association certificates and including at least one root certificate for at least one of the entities.

Abstract: A system and method relating to multi-level data de-duplication operations relating to data records associated with multiple user systems. The system includes a first set of computing instances execute a first set of de-duplication operations to generate a set of locally de-duplicated files associated with a data stream comprising the data records associated with the user systems. The system includes a storage system to store the set of locally de-duplicated files. The system includes a second set of computing instances to receive, in accordance with a frequency type of multiple frequency types, the set of locally de-duplicated files from the storage system and execute a second set of de-duplication operations to generate a set of globally de-duplicated files associated with the data records.

Abstract: Techniques are provided for storage system backup and de-duplication. In an example, a file system stores a plurality of files. These files can be grouped into smaller sets through a series of iterations, where a set of files is similar to each other. Then, files of a set of files can be directly compared for duplicate information relative to each other, one copy of this duplicate information can be stored, and other copies can be discarded.

Abstract: There is a need for more effective and efficient detection of cross-data-column relationships. This need can be addressed by, for example, techniques for detecting cross-data-column data relationships that utilize at least one of feature-based similarity models and deep-learning-based similarity models. The cross-data-column data relationships may be displayed to an end-user using a cross-column relationship detection user interface.

Abstract: Batch building for artificial intelligence workflows, including: issuing, responsive to a request for a batch of data objects, requests to a data repository for multiple data objects stored among one or more directories; selecting, in accordance with a batch building policy, a subset of data objects based on one or more responses to the requests; and providing, to the artificial intelligence workflow, a batch of data objects that includes the subset of data objects.