Abstract: The present disclosure relates to computer-implemented methods, software, and systems for automatic database configuration. One example method includes receiving, at a database associated with a data warehouse application, a request to generate a configuration to acquire data objects based on executing calls to corresponding programming interfaces provided by a source application, wherein the programming interfaces are identified in the received instructions and are associated with acquiring the data objects associated with metadata entities defined at the database; in response to the received instructions, configuring the database to automatically execute one or more calls to acquire relevant data for a metadata entity defined at the database, wherein the metadata entity is associated with a section of a first programming interface; and invoking the first programming interface at the source application, wherein invoking the first programming interface results in acquiring the relevant data.

Abstract: Systems and methods for neural machine translation are provided. In one example, a neural machine translation system translates text and comprises processors and a memory storing instructions that, when executed by at least one processor among the processors, cause the system to perform operations comprising, at least, obtaining a text as an input to a neural network system, supplementing the input text with meta information as an extra input to the neural network system, and delivering an output of the neural network system to a user as a translation of the input text, leveraging the meta information for translation.

Abstract: An asset monitoring system receives a first multi-dimensional representation of an asset associated with an industrial process. The asset monitoring system generates a second multi-dimensional representation of the asset from the first multi-dimensional representation. The second multi-dimensional representation may be a two-dimensional vector graphic representation of the asset and may be configurable via a graphical user interface. In generating the second multi-dimensional representation, the asset monitoring system maps at least one operational parameter associated with the asset to the second multi-dimensional representation. The asset may include one or more devices of the asset monitoring system. The asset monitoring system may display, via a dashboard at the graphical user interface, the second multi-dimensional representation and status information associated with the asset.

Abstract: Described herein is a system and method for providing an integrated function editor, for use with a data analytics environment. The function editor can be utilized to create and register functions available within a cloud infrastructure or cloud environment, for use within a data analytics environment. Such functions available for use within the cloud infrastructure or cloud environment can be displayed for the user, and used, for example, in data analytics workbooks, to create an interface or API that allows connection of the data analytics environment to a cloud infrastructure database.

Abstract: A database system operates by: determining a first query that indicates a first request to generate a K nearest neighbors (KNN) model; executing the first query to generate KNN model data for the KNN model, wherein the KNN model data is set as a reduced dataset for one of a plurality of training iterations; determining a second query that indicates a second request to apply the KNN model to input data; and executing the second query to generate model output of the KNN model for the input data based on, for each row in the input data, identifying a classification label for the each row from the discrete set of labels based on performing the KNN classification algorithm to classify the each row in the input data by applying the reduced data set.

Abstract: The present disclosure provides techniques and solutions for sorting data. In a particular implementation, a sorting technique is provided that places values in a sorted order by adding an offset value to values that are not in a sorted order. The resulting sorted set of values is not truly sorted, in that the set of modified values is sorted, but the underlying data itself is not sorted. In another implementation, a sorting technique can use multiple streams or sets. When an out of order element is encountered, it can be added to a new stream, if such a stream is available. The sorting techniques can be used for a variety of purposes, including provided sorted data for use in generating summary data, or for providing sorted data to be used in determining an intersection between two datasets.

Abstract: Methods and systems for managing distribution of data are disclosed. The data may be distributed by edge systems to processing systems. The data may be distributed using a data pipeline. The data pipeline may include functionality to process data using different modalities depending on the type of data and operable connectivity between the edge systems and processing systems. By modifying the manner in which the data is processed, limitations in operable connectivity may be managed and a broader array of different types of data may be more efficiently distributed.

Abstract: An apparatus for classifying an entity to an executable data structure, the apparatus including a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to receive entity input from an entity using a graphical user interface, wherein receiving the entity data includes receiving a low-level entity data at a first time and receiving a high-level entity data at a subsequent time determine a degree of interaction pertaining to the entity by comparing the low-level entity data to the high-level entity data, identify at least a protocol metric as a function of the degree of interaction, determine a protocol object as a function of the high-level entity data and the at least a protocol metric, establish an executable data structure for the entity as a function of the protocol object and display the executable data structure using the graphical user interface.

Abstract: A system for determining relevant information based on user interactions may include a processor configured to receive data and associated relevance information from a data source and a set of signals describing a current environment of a user or historical user behavior information in which the data source being local to a computing device. The processor may be further configured to provide, using a machine learning model, a relevance score for each of multiple data items based at least in part on the received relevance information and the set of signals. The processor may be further configured to sort the data items based on a ranking of each relevance score for each data item. The processor may be further configured to provide, as output, the multiple data items based at least in part on the ranking.

Abstract: A user device can be used to generate medical term expressions, which represent medical terms of a health record. The user device can identify a medical concept present in the health record based on a medical term expression. The user device can generate a node in a personalized relational graph that corresponds to the medical concept. One or more sub-nodes can be added to the node. Responsive to a request, a user interface is presented that identifies the medical concept and some of the additional information.

Abstract: A method includes generating a new user query embedding for a new user query received from a user, obtaining an indexed user query matching the new user query from a search engine index, a vector index corresponding to the indexed user query, and a relevancy score corresponding to the indexed user query. The method further includes selecting a vector structure corresponding to the vector index from a plurality of vector structures in a vector store, obtaining, from the vector structure, a result embedding matching the new user query embedding, transmitting, by a user query answer service to an answer generation model, the result embedding and receiving, by the user query answer service, an answer to the new user query from the answer generation model.

Abstract: A method is provided that includes establishing, by an application server, a remote access session with a client device, and creating, by a file system agent running on the application server, a metadata-only virtual file system associated with the remote access session, wherein the virtual file system only comprises file metadata associated with a plurality of files residing in a local file system of the client device. The method further includes responsive to receiving, by the virtual file system, a request to access content of a file referenced by the virtual file system, redirecting the request to a file system driver implementing at least a sub-tree of the local file system of the client device.

Abstract: In one example method, a first image including M objects is obtained. For N objects in the M objects, when N is greater than or equal to 2, arrangement orders of the N objects is determined, where an arrangement order of any one of the N objects is determined based on at least one of a scene intent weight, a confidence score, or an object relationship score. The scene intent weight is used to indicate a probability that the any object is searched in a scene corresponding to the first image, the confidence score is a similarity between the any object and an image in an image library, and the object relationship score is used to indicate importance of the any object in the first image. Search results of some or all of the N objects are fed back according to the arrangement orders of the N objects.

Abstract: The disclosure discloses a point-of-interest recommendation method based on a temporal knowledge graph. Based on the historical behavior trajectory of the user and multimodal information, the disclosure constructs a dynamic temporal knowledge graph and a static group knowledge graph, which are used to learn interest preferences of the user that change over time and stable features that do not change over time, respectively. At the same time, the disclosure uses deep learning methods to build a point-of-interest recommendation model, which can extract a point-of-interest fusion feature representation and a user fusion feature representation from the two knowledge graphs in combination with a user review sentiment embedding sequence to accurately predict a point of interest most likely to be visited by the target user at a next moment. The disclosure has the characteristics of high precision and strong scalability, and can provide support for personalized user behavior trajectory prediction.

Abstract: Techniques are disclosed for storing and accessing data in an information model. In some embodiments, data is stored in the information model using assets, components, and relationships, or using any other suitable transformation of the data. Each asset is a container for components. Each component stores data according to a schema that defines the organization of data in the component. Assets and components can be related to other assets and/or other components. A file can be imported into the information model by extracting data from the file, creating assets and components in the information model based on the extracted data, and wiring together the created assets and components. In addition or alternatively, an application can invoke an API to write data to the information model. An application can also invoke the API to query the information model, such as to read data from the information model.

Abstract: Various examples are directed to systems and methods for installing a plugin to a cloud-implemented database management application. A shared container file system may be initiated at a cloud environment. A plugin container image may be accessed, where the plugin container image comprises plugin payload data describing a first plugin to the database management application and a copy executable. A plugin container may be started at the cloud environment, where the plugin container is based at least in part on the plugin container image. The plugin container may be mounted to the shared container file system. The copy executable may be executed to copy the plugin payload data to the shared container file system. The first plugin may be installed to a database management application instance executing at the cloud environment.

Abstract: A data platform for managing an application as a first-class database object. The data object can include User Interface (UI) components. The data application can be shared by a provider account to a plurality of consumer accounts using a share object and based on grant commands. The consumer accounts can deploy and operate the UI component based on the share object.

Abstract: A system is provided for intelligent and integrated preservation of multiple electronic data records within a distributed electronic data register. In particular, the system may use a stacked data structure within the distributed register that allows data records or blocks within the distributed register to include multiple links or pointers (e.g., hash values) to previous blocks, where such previous blocks may be linked to a single parent block. By using such a multi-link data structure, the system may prevent the waste of energy and/or computing resources associated with the computational work of the nodes for submitting blocks to be appended to the distributed register. The system may further use a two-pointer algorithm for traversing the multi-link distributed register, thereby providing for an efficient way to search for specific data records. In this way, the system provides a more secure and efficient way to store data within a distributed register.

Abstract: A system and method is described for database split generation in a massively parallel or other distributed database environment including a plurality of databases and a data warehouse layer providing data summarization and querying functionality. A database table accessor of the system obtains, from an associated client application, a query for data in a table of the data warehouse layer, wherein the query includes a user preference. The system obtains table data representative of properties of the table, and determines a splits generator in accordance with one or more of the user preference or the properties of the table. The system generates, by the selected splits generator, table splits dividing the user query into a plurality of query splits, and outputs the plurality of query splits to an associated plurality of mappers for execution by the associated plurality of mappers of each of the plurality of query splits against the table.

Abstract: Provided are techniques for a search using an overlay graph mapping to source knowledge graphs. A plurality of overlay graphs are generated, where each overlay graph comprises entities represented by nodes and relations represented by edges, and where the entities and the relations map to a subset of entities and relations in a plurality of source knowledge graphs. A search request comprising an entity and a relation is received. An overlay graph is selected from the plurality of overlay graphs based on the entity and the relation. The search request is issued against the overlay graph, where the search request is translated to knowledge graph specific queries, and where the knowledge graph specific queries are issued against the plurality of source knowledge graphs. Search results are received from the plurality of source knowledge graphs. The search results are used to respond to the search request.