In various example embodiments, a system and method for consistent schema-less and scalable database storage are described herein. A data object is generated. The data object corresponds to a column of a table from a database. The data object includes information regarding an order that is placed over a network publication system. The data object is stored in the column of the table in the database. A request to access the data object is received from a device of a first user. The data object is transmitted to the device of the first user. The data is kept coherent during concurrent updates by using optimistic locks. The data is kept backward and forward compatible utilizing intermediate data structures common to both versions of the software. The data is kept searchable by using lookup indexes. The storage system is kept scalable by sharding data across many databases.

Embodiments of the present disclosure provide to a method and a device for acquiring a data model in a knowledge graph, an apparatus and a storage medium. The method includes: receiving a knowledge entry describing a relationship between an entity and an object; determining a plurality of candidate object types of the object according to at least one of the entity, the relationship and the object; determining an object type for generating a data model that matches the knowledge entry from the plurality of candidate object types based on a preset rule; and generating the data model based at least on the object type.

A method for mitigating errors in the transportation of configuration data may include identifying, at a development system, dependent configuration data associated with a first transport request. The dependent configuration data may implement a customization to a software application hosted at a production system. A reference table identifying the dependent configuration data may be sent to the production system. A missing object list identifying dependent configuration data absent from the production system may be generated at the production system based on the reference table. The missing object list may be sent to the development system where a corrective action may be performed such that the dependent configuration data identified by the missing object list as being absent from the production system is sent to the production system in the first transport request and/or a second transport request. Related systems and articles of manufacture, including computer program products, are also provided.

Methods, systems, and computer-readable storage media for receiving, by a current database system, a query plan file representative of a captured query plan from a source database system, receiving, by the current database system, a set of definitions including one or more definitions, each definition in the set of definitions corresponding to an object that is implicated by the query plan, the object being included in a set of objects, and determining, by the current database system, that each definition in the set of definitions is identical to a respective definition of a corresponding object within the current database system, and in response: executing the captured query plan in the current database system to provide a query result.

A computer-implemented method of mapping attribute names of a source data model to a destination data model includes obtaining multiple source attribute names from the source data model, and obtaining multiple destination attribute names from the destination data model. The destination data model includes multiple attributes that correspond to attributes in the source data model having different attribute names. The method includes processing the obtained source attribute names and the obtained destination attribute names to standardize the attribute names according to specified character formatting, supplying the standardized attribute names to a machine learning network model to predict a mapping of each source attribute name to a corresponding one of the destination attribute names, and outputting, according to mapping results of the machine learning network model, an attribute mapping table indicating the predicted destination attribute name corresponding to each source attribute name.

Various implementations of the present application set forth a method comprising generating three-dimensional data and two-dimensional data representing a physical space that includes a real-world asset, generating an extended-reality (XR) stream representing a remote collaboration session between a host device and a set of remote devices, where the XR stream includes a combination of the three-dimensional data and the two-dimensional data, a set of augmented-reality (AR) elements associated with the real-world asset, and a set of performed actions associated with a portion of the digital representation or at least one AR element, serializing the XR stream into a set of serialized chunks, transmitting the serialized chunks to the remote devices, where the remote devices recreate the XR stream in a set of remote XR environments, and transmitting the serialized chunks to a remote storage device, where a device subsequently retrieves the serialized chunks to replay the remote collaboration session.

The present disclosure describes a system, method, and computer program for converting a natural language update instruction to a structured update database statement. In response to receiving a natural language query for a database, an NLU model is applied to the query to identify an intent and entities associated with the query. If the intent is to update a data object, the system evaluates the entities to identify update fields and update values. Update fields are matched to update values based on update parameters, operand type of the update value, and location of the update fields and values. For each update field and value pair, an update context is calculated to determine whether the update value is absolute or relative to an existing field value. An update plan is created with the update field and value pairs and corresponding update contexts, and a database update statement is generated from the update plan.

This disclosure describes systems, methods, and devices related to coordinating the exchange of information relating to the delivery of assets (e.g., packages, freight, supplies, parts, materials, raw goods, inventory, tools, and equipment) in a supply chain or inventor management context. A status token may be provided to an unauthorized user, wherein the status token is initialized in an invalid state. Attempts to query status information while the status token may be denied and/or convey no information regarding the status of the asset. If an adverse condition occurred during a designated time period, status information for the asset may be provided.

Technologies are provided for determining a suitability of data payloads for training a machine learning model. A schema can be generated based on sample data payloads that have different data formats. The sample data payloads (and/or additional data payloads) can be converted to a format that conforms to the schema. Feature vectors can then be generated based on the converted data payloads, and used to determine a suitability of the data payloads for training a machine learning model. If the data payloads are sufficiently suitable, the converted data payloads can be used to train the machine learning mode. Otherwise, the schema may be annotated and new converted payloads may be generated based on the annotated schema. The feature vector generation and suitability analysis can then be repeated.

A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to “slices”, and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.