Abstract: A computing device can use a machine learning model to generate configuration parameters for a relationship computing model. The configuration parameters can include a time period for a plurality of variables, an alert threshold parameter for the plurality of variables, a lower relationship threshold parameter for each of the plurality of variables, and an upper relationship threshold parameter for each of the plurality of variables. The computing device can configure the relationship computing model using the configuration parameters. The computing device can execute, in some cases using a machine learning language processing model, the configured relationship computing model to generate relationships for pairs of the variables. Based on the execution, the computing device can generate an alert indicating a set of pairs of variables that correspond to a relationship value exceeding an alert threshold.

Abstract: In some embodiments, techniques for self-labeling to extract a representative set of samples from a large-scale set of unlabeled documents (e.g., a set that represents a distribution of the large-scale set) are provided. The samples of the representative set may then be used to classify the documents of the large-scale set.

Abstract: Workload pool management with search time range includes calculating a search time range for a query, relating metadata including the search time range for the query, and comparing predicates in workload rules to the metadata for the query to identify a matching workload rule. Comparing the predicates includes the search time range to search time criteria in the predicates. The query is processed by the workload pools according to the matching workload rule.

Abstract: A method, a system, and computer program product for customizing settings of cloud-based software applications using an external database as a source are provided. A request to generate a new database table corresponding to a new version of a database table associated with a cloud-based system hosting a cloud-based software application is received. The request is processed to determine a content of the new database table using a data structure of the database table. A version identifier corresponding to the new database table is generated. The version identifier and the new database table are provided, to an external database for storage. A deployment, to a productive system, of the new database table, is controlled for the new database table to be used when executing the cloud-based software application.

Abstract: System and method for query augmentation for generating responses is disclosed. The method includes, receiving an input data from a user device, determining a context of the received input data, determining a domain specific graphical knowledge schema corresponding to the received input data, and identifying a plurality of missing entities by analyzing the determined context and at least one graphical instance corresponding to the determined appropriate domain specific graphical knowledge schema. The method further includes, prioritizing the identified plurality of missing entities, generating at least one sub-query for each of the identified plurality of missing entities and retrieving a relevant content corresponding to the generated at least one sub-query using a RAG-based system.

Abstract: Each event type in a business process is represented by a node in a process discovery graph. Semantic clustering merges nodes based on a commonality between them. As a result, a graphical user interface (GUI) may be presented that shows fewer nodes. This process may be repeated, using different commonalities at each level. A user may select a node to zoom in, showing the constituent nodes. The frequency of an event or its other attribute values may be compared to a predetermined threshold to determine if the event is a blocking event. The GUI may indicate that the node for an event is a blocking event. When the node for the event is merged with other nodes using semantic clustering, the blocking status of the node is transferred to the merged node. After zooming in on the blocking event, the GUI allows the user to select the blocking event to cause display of a recommendation.

Abstract: A system and method for analyzing coverage, bias and model explanations in large dimensional modeling data includes discretizing three or more variables of a dataset to generate a discretized phase space represented as a grid of a plurality of cells, the dataset comprising a plurality of records, each record of the plurality of records having a value and a unique identifier (ID). A grid transformation is applied to each record in the dataset to assign each record to a cell of the plurality of cells of the grid according to the grid transformation. A grid index is generated to reference each cell using a discretized feature vector. A grid storage for storing the records assigned to each cell of the grid is then created. The grid storage using the ID of each record as a reference to each record and the discretized feature vector as a key to each cell.

Abstract: Various examples are directed to systems and methods for managing multiple cloud-delivered services. An integration adapter may receive, from a first cloud-delivered service, event data describing an event at the first cloud-delivered service. The integration adapter may send a request for data objects for responding to the event to a determination engine executing at the data distribution computing system. The determination engine may determine a first data object and a second data object responsive to the request for data objects. The integration adapter may replicate the first data object from a database management system to generate a first replicated data object and replicate the second data object from a second cloud-delivered service to generate a second replicated data object. The integration adapter may send the first replicated data object and the second replicated data object to a target cloud-delivered service.

Abstract: Systems, methods, and non-transitory computer readable media are provided for validating data for integration. A file to be integrated into a dataset may be determined. The file may be characterized by a file name and metadata. Whether the dataset can be identified based on the file name and the metadata may be determined. Based on the dataset not being identifiable, information needed to identify the dataset may be requested. Based on the dataset being identifiable, the file may be routed for integration into the dataset.

Abstract: The present technology is directed to providing a multi-root architecture for managing devices associated with their own root namespaces. The system can include backing up multiple computing devices to a single user account such that the content items from each computing device are stored in their own respective root namespace at the user account. With each user account having multiple root namespaces, the system can identify which root namespace is receiving changes, identify which changes are necessary to synchronize the system, and then communicate those changes to the content management system. This process is facilitated by running a synchronization engine for each root namespace, thereby controlling synchronization for each root namespace. Each synchronization engine is able to retain its own parameters of operation, which provides the ability to tailor synchronization modes on each synchronization engine.

Abstract: Automated batch generation and subsequent submission and monitoring of batches processed by a system is disclosed. A plurality of records to be submitted to a system for processing is accessed. A plurality of record groups is generated, each record group corresponding to a different subset of records. A plurality of batch transaction records is generated. Each batch transaction record corresponds to one of the plurality of record groups, and includes a subset identifier that references a subset of records of a record group to which the batch transaction record corresponds, and a status field. A first batch transaction record that identifies a first subset of records is accessed. The status field of the first batch transaction record is set to a submitted value, and instructions are sent to the system to process the first subset of records.

Abstract: A multiple-tier operation evaluates a query across storage tiers in columnar format. A database server receives from a client a query for reading values from a set of columns of a database table. The multiple-tier operation comprises accessing a first subset of rows for the set of columns in columnar format in a first tier to generate a first set of results and accessing a second subset of rows for the set of columns in columnar format in a second tier to generate a second subset of results. The multiple-tier operation further comprises aggregating the first set of results and the second set of results to form a query result set and returning the query result set to the client.

Abstract: A computer-implemented method for synchronizing at least one Uninterruptible Power Supply, UPS, information collector and at least one other UPS information collector, comprising: providing at least one virtual UPS information aggregator for synchronizing the at least one UPS information collector and the at least one other UPS information collector, wherein the at least one virtual UPS information aggregator executes the following steps: identifying of new UPS information on the at least one UPS information collector, transferring the identified new UPS information to the at least one other UPS information collector.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing personalized autocomplete predictions. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform personalized autocomplete predictions using a general search corpus and/or individual curated search corpus.

Abstract: In some examples, a computing device refines feature information of query text. The device repeatedly determines attention information based at least in part on feature information of the image and the feature information of the query text, and modifies the feature information of the query text based at least in part on the attention information. The device selects at least one of a predetermined plurality of outputs based at least in part on the refined feature information of the query text. In some examples, the device operates a convolutional computational model to determine feature information of the image. The device network computational models (NCMs) to determine feature information of the query and to determine attention information based at least in part on the feature information of the image and the feature information of the query. Examples include a microphone to detect audio corresponding to the query text.

Abstract: There is provided a computerized method and system of data management using extended Structured Query Language (SQL). The method includes executing an extended SQL script created in response to a data query request and comprising at least one operator of: a first operator embedding a SQL statement, or a second operator embedding a named entity. The executing comprises identifying the at least one operator in the extended SQL script; for each of the at least one operator, evaluating the embedded content thereof in runtime to dynamically generate SQL code based on a runtime input; replacing each of the at least one operator and the embedded content thereof in the extended SQL script with the generated SQL code thereof, giving rise to a standard SQL script; and running the standard SQL script to obtain a processing result of the data query request.

Abstract: Embodiments are directed to a file system that include object stores. An object store for write requests may be provided. Write ahead log (WAL) entries that include data blocks may be generated. A WAL object may be generated based on the WAL entries and stored in the object store. An in-memory overlay may be updated to associate the data blocks with the WAL object. A checkpoint operation may be executed to: generate an index object that includes index entries that associate other data blocks with data objects stored in the object store; update the index object to include index entries that associate the data blocks with the WAL object; store the updated index object in the object store; update the in-memory overlay to remove the association of the data blocks and the WAL object and update the in memory WAL to remove records of successfully checkpointed WAL objects.

Abstract: An image file management method includes receiving a write instruction used to instruct to write a first image file; determining, based on the write instruction, at least one first edge cloud node from a plurality of edge cloud nodes included in an image file management system; and writing a plurality of data blocks included in the first image file into the at least one first edge cloud node, so that each first edge cloud node stores a part or all of the plurality of data blocks included in the first image file.

Abstract: An indexing technique provides an index data structure for efficient retrieval of a snapshot from a long-term storage service (LTSS) of an archival storage system. The snapshot is generated from typed data of a logical entity, such as a virtual disk (vdisk). The data of the snapshot is replicated to a frontend data service of the LTSS sequentially and organized as one or more data objects for storage by a backend data service of LTSS in an object store of the archival storage system. Metadata associated with the snapshot (i.e., snapshot metadata) is recorded as a log and persistently stored on storage media local to the frontend data service. The snapshot metadata includes information describing the snapshot data, e.g., a logical offset range of a snapshot of the vdisk and, thus, is used to construct the index data structure. Notably, construction of the index data structure is deferred until after the entirety of the snapshot data has been replicated and received by the frontend data service.

Abstract: Embodiments relate to improving efficiency of data analytics performed on sets of entity data in which different entity properties having very different update frequencies. Time-based analytical queries track the entity states at each moment within a given time window. Analytical queries are executed over a massive number of entity states while using a reasonable memory footprint. The technique partitions the entity properties into partial historical snapshots of data and combines the partial snapshots on demand only as needed to execute analytical queries over business entities. A complete entity state having values for all entity properties is not required to execute most queries. Only partial snapshots including values referenced by the query need to be combined to satisfy the query. Using partial snapshots minimizes data replication, and the snapshots can be efficiently combined into entity states sufficient for query execution.