Abstract: Methods that provide accelerated data operations by splitting data records into sub-records and by using in-memory storage. In these methods, a computing device obtains a complex data record that includes at least one unique identifier, primary information about the complex data record, and a plurality of data values that change over time. The computing device generates a plurality of data sub-records by splitting, based on a set of rules, the complex data record into a plurality of parts in which at least a portion of the primary information is separated from the plurality of data values and added to a respective data sub-record of the plurality of data sub-records, generating a unique binding identifier, and adding the unique binding identifier to each of the plurality of data sub-records to link the plurality of data sub-records to each other. The data sub-records are stored, using an in-memory database, into a blockchain.

Abstract: In response to receiving a tagging request to map a first non-fungible token (NFT) to a first user device, a processor transmits an authorization request to a second user device to map the first NFT to the first user device. In response to receiving an approval of the request, the processor transmits a request to a minting server of an NFT blockchain network to generate the first NFT for the first user device. The processor stores a token ID of the generated first NFT in a memory. In response to receiving a second authorization from the first user device to perform a data interaction, the processor requests the NFT blockchain network based on the token ID, verification of the first user device. In response to receiving an indication that the first user device is verified, the processor processes the data interaction.

Abstract: The present embodiments relate to implementing change data on no-master NoSQL data stores. An optimized node can be identified from a plurality of NoSQL data storage nodes and a specialized node can be connected (e.g., collocated) to the optimized node. The specialized node can maintain change data capture (CDC) data provided by client nodes in a hash map that can be used as a point of truth for coordinating CDC data across the plurality of NoSQL data storage nodes. The plurality of NoSQL data storage nodes can identify and coordinate all read/write data obtained from multiple client devices in a geographically separated large-scale (e.g., planet scale) system to identify change data in a distributed data store. The specialized data can provide read data to devices in the large-scale system to reconcile inconsistencies in change data across nodes in the large-scale system.

Abstract: A method, computer program product, and computing system for deploying an agent configured to communicate with a centralized database and a plurality of remote databases. The plurality of remote databases may be polled, via the agent, for data for storage in the centralized database. The data may be consolidated from the plurality of remote databases to the centralized database.

Abstract: Example implementations as described herein are directed to generate a fault tree with a design document and minimized rules or database. The fault tree generation system may utilize assembly data and ladder programs as input and generates a fault tree by connecting an upper part of the fault tree generated from an assembly tree structure in the assembly data and a lower part of a fault tree from control logic in the ladder program. The fault tree is generated by connecting the lower part of the tree of the fault tree from the logic.

Abstract: A computing device displays, in a graphical user interface corresponding to a data visualization application, a dashboard having one or more data visualizations related to a data source. The device receives a user interaction with a first data visualization of the dashboard. The device compares the user interaction to a set of stored trigger actions and determines, based on the comparing, that the user interaction corresponds to a predefined trigger to initiate a workflow action to be executed by an external service, distinct from the data visualization application. In accordance with the determination, the device identifies parameters of a predefined action template corresponding to the workflow action. The device extracts a subset of data from the data source, corresponding to the parameters. The device maps the subset of data to the parameters of the action template and initiates execution of the external service.

Abstract: An exemplary system for constructing data structures that can perform inferential reasoning to answer input queries may receive input data, extract and cluster entities in the input data into topic clusters, and for a first topic cluster construct a data structure comprising a plurality of nodes, wherein nodes of the data structure respectively represent a topic entity extracted from the input data and grouped into the first topic cluster, and wherein a first node of the data structure is associated with a second node of the data structure based on the first node and the second node respectively representing a first topic entity and a second topic entity associated in the input data with a common one of the one or more identified linguistic modalities. An exemplary system comprising the data structure may receive an input query and generate a response to the input query using the data structure.

Abstract: Heterogeneous sets of cloud applications and storage can be bridged. Cloud services can be configured to include a Web Application Open Platform Interface (WOPI) abstractor, multiple WOPI servers corresponding to respective multiple WOPI clients of cloud applications that may be used to access files, and a cloud storage abstractor that functions as an intermediary between the multiple WOPI servers and multiple cloud storages in which the files may be stored. The cloud services allow a user to access a single webpage or other interface to obtain access to his or her files stored in the multiple cloud storages via any of the multiple cloud applications.

Abstract: A quick application startup method and a related apparatus are provided. The method includes: An electronic device requests an acceleration script of one or more quick applications from an application server. A first operation for a target quick application is detected. In response to the first operation, the electronic device requests an application package of the target quick application from the application server. An acceleration script of the target quick application is included in the acceleration script of the one or more quick applications. In response to the first operation, the electronic device runs the acceleration script of the target quick application to obtain a first URL, and obtains first data based on the first URL. The electronic device may generate and display a first screen of the target quick application based on the first data.

Abstract: A method of operations of a display-server computing system includes delivering input by a display device to a server computer over a communications network, processing an application unit of the server computer with the input, responsive to the step of inputting, outputting a result of the processing, together with tags indicative of the input, delivering the result and the tags by the server computer to the display device over the communications network, receiving the result and the tags by the display device, receiving new input to the display device prior to receiving the result and the tags by the display device, and processing a transform of the display device by comparing the tags to the new input to obtain a transformed result.

Abstract: The disclosure presents processes to improve the ability to analyze geological information for an area or region of interest. A user can specify one or more input files, such as from public, private, or proprietary sources. The user can specify a geological or geographic framework to utilize. The process can then perform a matching between the data in the input files and the data in the framework. The matching process can utilize a geological matching using a specified range of depths or a geographical matching followed by the geological matching. Other parameters can be utilized such as a radius to define an area of interest around a central location of interest. Matched data elements can have geological attributes from the geological framework data linked to data elements in the input files. The input files can be downloaded, displayed, printed, or communicated to another computing system or program for further analysis.

Abstract: Example methods and systems for accessing data in a log-structured file system having a plurality of snapshots of storage objects backed by a first-level copy-on-write (COW) B+ tree data structure and a plurality of second-level B+ tree data structures have been disclosed. One example method includes obtaining a first first-level mapping associated with a first snapshot from the plurality of snapshots based on a first logical block address, wherein each of the plurality of snapshots corresponds to each of the plurality of second-level B+ tree data structures, identifying a first second-level B+ tree data structure corresponding to one of the plurality of snapshots based on the first first-level mapping, obtaining a first second-level mapping based on the first logical block address in the first second-level B+ tree data structure, obtaining a first physical block address based on the first second-level mapping, and accessing data at the first physical block address.

Abstract: Methods, apparatus, systems, and articles of manufacture to estimate cardinality through ordered statistics are disclosed. In an example, an apparatus includes processor circuitry to selects a sample dataset from a first reference dataset of media assets and partitions the sample dataset into m mutually exclusive subsets of approximately equal size. The processor circuitry then estimates a ratio of a sample weighted average and empirical cumulative distribution of an approximately largest order statistic from at least one of the m subsets and generates an estimate of a total cardinality of the first reference dataset by multiplying the ratio by approximately m.

Abstract: A secret hash table construction apparatus constructs a secret hash table capable of storing up to Z items of data in B kinds of address values by secret computation from a real data stream including items of data each having a key and a flag indicating whether or not the data is dummy data. The secret hash table construction apparatus executes generating a first array in which a storage destination data array is connected with another storage destination data array as dummy data; generating a second array in which the real data stream is connected with a dummy data stream; sorting each of the first and second arrays based on a ranking operations; extracting address values of lower ranks to generate a third array; and sorting the second array using a fourth array, and outputting BZ elements in the sorted second array as the secret hash table.

Abstract: Generally discussed herein are devices, systems, and methods for machine learning (ML) by flowing a dataset towards a target dataset. A method can include receiving a request to operate on a first dataset including first feature, label pairs, identifying a second dataset from multiple datasets, the second dataset including second feature, label pairs, determining a distance between the first feature, label and the second feature, label pairs, and flowing the first dataset using a dataset objective that operates based on the determined distance to generate an optimized dataset.

Abstract: Systems and methods are disclosed for processing a search request received from a user. A method includes providing access to content items associated with metadata. The method includes providing information about at least one search previously performed for a user, and providing access to information describing relationships between at least one of the content items and the metadata. The method includes receiving a present input from the user intended to identify a desired content item. Upon determining that results from the previous search did not satisfy the search request from the user, the method includes determining an intent shared by the previous search and the present input based on comparing (i) the relationship information, (ii) the previous search, and (iii) the present input. The method also includes selecting and presenting a subset of content items based on comparing the shared intent and the metadata.

Abstract: A digital method for tracing data from a plurality of data sources includes the steps of: a. obtaining initial data originating from a plurality of data sources, each initial datum comprising initial elements and initial connections, each initial element having a type and a value, b. determining a final datum on the basis of at least two initial data following the merging with one another of initial elements of the initial data according to a merging strategy, the merged initial elements making it possible to obtain a merged element having a type and a value, and c. determining a merging history for each merged element, the merging history comprising the type and the value of each initial element from which the merged element originates, the type and the value of the merged element, and data relating to the merging strategy.

Abstract: Initially, a database schema is parsed and a table tree structure is created delineating the relationships between data that are identified in the schema. In addition to accomodating relationships between main tables of data, the table tree structure also accomodates possible side tables of data, and possible circular references between tables, should such be encountered when parsing the schema. Subsequently, a migration mechanism consumes the generated table tree structure and iteratively migrates data in accordance therewith. Individual layers of the table tree structure are migrated consecutively with referenced layers being migrated prior to referencing layers. Circular links are accommodated through temporary null values, and side tables are accommodated during migration of the referencing main table. The iterative process provides completeness and fault tolerance/failure recovery.

Abstract: A method includes generating a task using a plurality of logical statements embedded in a database, the plurality of logical statements corresponding to a data modification. Database data is ingested into a staging table that is configured within the database. The task is executed based on applying the data modification to a first set of partitions storing the database data and generating a second set of partitions. The second set of partitions store modified data corresponding to the database data. A stream of committed transactions is advanced at least in part by adding an entry into the stream. The entry corresponds to committed transactions performed on the first set of partitions during the data modification. A data processing task is scheduled for execution on the modified data based on the advancing of the stream offset.

Abstract: This application discloses a database transaction processing method performed by a computer device. The method includes: in a case of starting a target transaction, obtaining, by a working node in a distributed database system, a transaction timestamp of the target transaction and current global latest version information, the target transaction including at least one operation statement for a target data object, and the global latest version information being version information of latest generated metadata of metadata stored in the distributed database system; determining latest-version metadata of the target data object according to the current global latest version information, and determining to-be-accessed user data of the target transaction according to the transaction timestamp; and executing an operation statement of the target transaction on the to-be-accessed user data based on the latest-version metadata of the target data object.