Abstract: Systems and methods are disclosed to implement, in a database system, global table management operations for global tables or objects that are replicated across multiple locations. In embodiments, for a regional replica of a global table, a requested change to a metadata property of the replica (e.g., an attribute data type, a write throughput limit, or a time-to-live setting) is propagated to other replicas of the global table at other locations. In embodiments, before performing the requested management operation, the database system may first verify that the operation can be successfully performed in all locations. In embodiments, the operation may be performed optimistically without verification, and rolled back if it cannot be performed everywhere. In embodiments, to serialize management operations on a global table, the database system may check for other pending management operations via a shared control object, and proceed when any pending operations are finished.

Abstract: Data classification includes tracking classification of columns of data into data classes of a collection of classes available for classifying the columns, obtaining a target column of data, of a target dataset, to be classified into a data class of the collection of candidate classes, and classifying the target column of data into a data class of the collection of classes based on historical data classification characteristics provided by the tracking. The classifying includes selecting a group of candidate data classes of the collection of classes to compare to value(s) of the target column, the selecting excludes at least some candidate data classes of the collection from comparison to the value(s), and establishing a priority between the candidate data classes of the group of candidate classes in comparing the value(s) of the target column of data to the selected group of candidate classes.

Abstract: A determination is made, by a first device, that data in a first database is modified. The first database is associated with the first device. A notification is generated by the first device. The notification includes information associated with the data modification in the first database. The notification is transmitted by the first device to at least one second device. At least one second database is associated with the at least one second device.

Abstract: A computer-implemented method includes: acquiring question information and question area information from a questioner terminal; acquiring pieces of answer area information from an answerer database; selecting one or more answerer terminals from among terminals based on the pieces of answer area information and the question area information; transmitting the question information to the answerer terminals; acquiring at least one piece of answer information from at least one of the answerer terminals; and transmitting the at least one piece of answer information to the questioner terminal.

Abstract: Detect duplicated questions using reverse gradient adversarial domain adaptation includes applying a general network to multiple general question pairs to obtain a first set of losses. A target domain network is applied to multiple domain specific network pairs to obtain a second set of losses. Further, a domain distinguishing network is applied to a set of domain specific questions and a set of general questions to obtain a third set of losses. A set of accumulated gradients is calculated from the first set of losses, the second set of losses, and the third set of losses. Multiple features are updated according to the set of accumulated gradients to train the target domain network.

Abstract: In one embodiment, an operating system on a computer device interfaces with a graph database that has data nodes interconnected by relationship edges. The operating system generates database instructions that specify a database operation for a target node in the graph database and a node traversal list through the graph database to reach the target node. By then transmitting the database instruction to the graph database, the graph database (e.g., a database management operating system) traverses the specified node traversal list through the graph database to the target node, and performs the database operation on the target node.

Abstract: Embodiments provide a temporal sensitive cognitive interface that determines whether a temporal data value of a response to a user query is fixed or variable over time; determine a time lapse from a current time to a time of generation of a variable one of the temporal data values; generate a first presentation of the response to the user that includes a (first) text word modifier that indicates a quality of certainty of the temporal data value in response to determining that the time lapse does not exceed a confidence threshold that is specified for an attribute of the response temporal data value; and generate another, different (second) presentation of the response to the user that includes a different (second) text word modifier that indicates a quality of uncertainty of the temporal data value in response to determining that the time lapse exceeds the confidence threshold.

Abstract: The present disclosure relates to performing attribution channel modeling in real time using touchpoint data that corresponds to a user-specified set of channels and is retrieved from a database using a user-specified attribution model. For example, in one or more embodiments, a system stores raw data in an attribution database that comprises an aggregator and a plurality of nodes. In particular, each node stores touchpoint data associated with a different user. Upon receiving a query, the system can, in real time, retrieve subsets of the touchpoint data that corresponds to a user-defined set of distribution channels in accordance with a user-specified attribution model. The system then combines the subsets of touchpoint data using the aggregator and generates the digital attribution report using the combined data.

Abstract: In one embodiment, an operating system (on a computer device in a network of computer devices) interfaces with a distributed graph database that is distributed across the network of computer devices, and executes an application that has one or more parent data constructs that reference one or more child objects within the distributed graph database. Specifically, the one or more child objects each have a location-independent object identifier (OID) having at least: a) an OID-identifier (OID-ID) assigned upon creation to each particular child object by a data source of the location-independent OID; b) an OID-source structure that uniquely identifies the data source of the location-independent OID within the network of computer devices; and c) an OID-pointer indicative of a relative location of the particular child object in a memory mapped region. Accordingly, the operating system accesses the child objects (e.g., for an application) based on the location-independent OID.

Abstract: In one implementation, a data transformation tool is configured to allow a user to specify how types of data should be mapped to another type of data in a reusable fashion. In one implementation, the data transformation tool analyzes data selected for input, the desired data output, and determines a list of potential mappings that may be used between the compliant and non-compliant systems. The mappings may be generated through transformational expressions and defined as a data wrapper. Once the definition of the one or more mappings are defined, data transformation tool generates one or more files allowing the execution of the transformation of the input data to the output data during an execution phase, such as runtime.

Abstract: A database system stores both compressed and uncompressed data in a row-based database system. The database system converts a representation of data involving a set of rows of a source database table, each row comprising multiple values, into a representation involving a single row stored in a target database table, each column of the row comprising arrays of values from the set. The database system may perform type-specific compression of data when storing in the target database table. Accordingly, the database system may apply different compression schemes for different columns or sets of values obtained from the source database table and may group or order selected rows or store additional summary information to improve query performance to the target database table. The database system allows users to query data stored in compressed form. The database system combines compressed and uncompressed data at query time for efficient database analytics.

Abstract: A method implemented by a computer comprising collecting historical data for a plurality of items; categorizing each of the plurality of items; assigning at least one of the plurality of items as an index item for at least another of the plurality of items; collecting data for a plurality of context parameters related to at least one of the plurality of items; and forecasting a value for one of the plurality of items needed over a future period of time, wherein the method reduces a data storage capacity requirement for the computer and increases said computer's processing speed.

Abstract: A blockchain may include various transactions which are identified and which require processing. The order of processing such transactions may be optimized by examining content of the transactions. One example operation may include one or more of identifying blockchain transactions, designating each of the blockchain transactions as an independent transaction type or a dependent transaction type, and determining an order to process the blockchain transactions based on the independent transaction type or the dependent transaction type.

Abstract: Verified snapshots are generated by obtaining, from one of a plurality of first nodes, a difference between a common data at a first time point and the common data at a second time point that is different from the first time point, generating a first snapshot of the common data at the first time point based on the difference, obtaining a hash of the common data at the first time point from one of the plurality of first nodes, and verifying the first snapshot at the first time point with the hash of the common data at the first time point.

Abstract: Verified snapshots are generated by obtaining, from one of a plurality of first nodes, a difference between a common data at a first time point and the common data at a second time point that is different from the first time point, generating a first snapshot of the common data at the first time point based on the difference, obtaining a hash of the common data at the first time point from one of the plurality of first nodes, and verifying the first snapshot at the first time point with the hash of the common data at the first time point.

Abstract: A table storage unit 81 stores a first table including an objective variable and a second table different in granularity from the first table. A descriptor creation unit 82 creates a feature descriptor for generating a feature which is a variable that can influence the objective variable, from the first table and the second table. The descriptor creation unit 82 creates a plurality of feature descriptors, each by generating a combination of a mapping condition element indicating a mapping condition for rows in the first table and the second table and a reduction method element indicating a reduction method for reducing, for each objective variable, data of each column included in the second table.

Abstract: Example implementations relate to backups to a secondary storage system of a file system instance that hierarchically relates data objects by content-based signatures to a root object and includes subtrees that correspond to respective inodes. In an example, a list of the content-based signatures is sent to the secondary storage system. Metadata and data of a subtree is stored into an item in the secondary storage system designated for the subtree.

Abstract: A computer-implemented method includes receiving, at a datastore having a plurality of records of a transaction-monitoring system, a first record representing a first instance of a transaction. The datastore includes a first layer and a second layer. A first record hash code, based on the first record, is compared to a template hash code set that includes one or more template hash codes, where each template hash code corresponds to a respective transaction in a set of one or more known transactions. The first record is inserted into the first layer of the datastore. The first record is inserted into the second layer of the datastore, based at least in part on the first record hash code not being found in the template hash code set.

Abstract: A method may include updating database posting logic to obtain an updated database posting logic that, during a posting process to a database, adds a data field to a new line item being posted via the posting process. The method may further include receiving information from a data provider. The method may further include posting, using the updated database posting logic, the information to the database as a line item including various fields including the data field. The method may further include receiving a request to generate a transactional report. The method may further include using the data field to identify the line item. The method may further include generating the transactional report using the line item.

Abstract: Passing a result of batch processing into a data integration flow is provided. Each data record of a plurality of data records corresponding to a specified data record collection is processed within a different thread of a different execution branch in parallel. At an end of each processing of each different execution branch, a new value is computed for each processed data record based on a previously stored value and an execution branch context for each processed data record. It is determined whether all data records in the plurality of data records have been processed. In response to determining that all of the data records in the plurality of data records have been processed, a data accumulation object corresponding to the batch processing is updated within a data assembly context based on a computed new value for each processed data record to form an updated data assembly context.