A storage node of a database replica group may distribute different portions of data in local storage and external storage, where local storage and external storage are organized using different types of index structures. Responsive to receiving an access request for a database, a storage node may determine that an item of the database to be accessed by the request does not reside within a first portion of the database stored locally at the storage node. Responsive to this determination, the storage node may obtain from an external storage service a second portion of the database, the second portion including a plurality of items including the item, and the second portion organized according to a structure different from the first portion. The storage node may then store the plurality of obtained items in the first portion and process the request using the first portion of the database.

Embodiments are directed to managing visualizations of data. A provided data model may include a tree specification that declares parent-child relationships between objects in the data model. In response to a query associated with objects in the data model: employing the parent-child relationships to determine a tree that includes parent objects and child objects from the objects based on the parent-child relationships; determining a root object based on the query and the tree; traversing the tree from the root object to visit the child objects in the tree; determining partial results based on characteristics of the visited child objects such that the partial results are stored in an intermediate table; and providing a response to the query that includes values based on the intermediate table and the partial results.

Systems and methods for processing events are disclosed. Event data comprising passive event data, active event data, or both is received. It is determined whether the received event data is available for a pattern of passive event data and active event data. In response to determining that the received event data is available for the pattern of passive event data and active event data, one or more constraints between the passive event data and the active event data are converted into one or more query terms. The query terms are used to construct at least one query. Remaining passive event data that is related to some, but not all, of the active event data is obtained using the constructed at least one query.

A method involves receiving a first command. The first command includes a data extraction expression applied to fields of a dataset of a data source. The first command also includes a first report configuration expression applied to first dimensions of a first report. The method also involves generating, by executing the data extraction expression on the dataset, records of the dataset. The method also involves generating, by executing the first report configuration expression on the records, a first tree of subsets of the records. The method also involves populating, using the first report configuration expression and the first tree of subsets, cells of the first dimensions to obtain first populated dimensions. The method also involves generating, in response to receiving the first command and by traversing the first tree of subsets, the first report including the first populated dimensions.

Embodiments of the present disclosure relate to processing data. An example method includes acquiring data related to a first moment in streaming data of an object to be processed. The method further includes storing the data in a first entry of a data table based on an identification of the object to be processed, wherein the data table further includes a second entry before the first entry, and the second entry stores data related to a second moment before the first moment in the streaming data. The method further includes updating an index related to the object to be processed based on the first entry. Thus, a solution to the problem of performing search in data at different moments is provided, and it is unnecessary for a user to participate in the solution, thus improving the user experience and reducing the use of storage resources.

A signal relationship is defined between a granular data value and a target data value. At least a portion of the granular data value corresponds to a granular latency value that is smaller than a target data latency value corresponding to the target data value. Granular data corresponding to the granular data value is interpreted. The granular data is aggregated in response to the signal relationship. A value of the target data value for a selected time reference is estimated, and the estimated value of the target data value is provided as a nowcasting prediction of the target data value.

A request is received from a user at a client to access a file of a set of files backed up to a backup server. Upon verifying a password provided by the user, the client is issued another request for authentication. A first data structure is received responsive to the request. The first data structure is generated using identifiers corresponding to a set of files at the client of which at least some presumably have been backed up to the server. A second data structure is generated. The second data structure is generated using identifiers corresponding to the set of files backed up to the server. The first and second data structures are compared to assess a degree of similarity between the files at the client and the files backed up to the backup server. The user is denied access when the degree of similarity is below a threshold.

From a set of natural language text documents, a concept tree is constructed. For a node in the concept tree a polarity of the subset represented by the node is scored. A second set of natural language text documents is added to the subset, the adding resulting in a modified subset of natural language text documents having a polarity score within a predefined neutral polarity score range. From the modified subset, a bin of sentences is selected according to a sentence selection parameter, a sentence in the bin of sentences being extracted from a selected document in the modified subset. A sentence having a factuality score below a threshold factuality score is removed from the bin of sentences. From the filtered bin of sentences a new natural language text document corresponding to the filtered bin of sentences is generated using a transformer deep learning narration generation model.

A dynamic reconfiguration training machine learning computer architecture is disclosed. According to some aspects, a computing machine accesses a configuration file. The configuration file specifies parameters for a machine learning session. The computing machine trains a machine learning module to solve a problem, where the machine learning module operates according to the parameters specified in the configuration file. The computing machine generates an output representing the trained machine learning module.

Computing systems and technical methods that transform data structures and pierce opacity difficulties associated with complex machine learning modules are disclosed. Advances include a framework and techniques that include: i) global diagnostics; ii) locally interpretable models LIME-SUP-R and LIME-SUP-D; and iii) explainable neural networks. Advances also include integrating LIME-SUP-R and LIME-SUP-D approaches that create a transformed data structure and replicated modeling over local and global effects and that yield high interpretability along with high accuracy of the replicated complex machine learning modules that make up a machine learning application.