Abstract: Provided is a system and method for generating and updating a data model for use in retrieving data from an information retrieval system such as a database, a server, and the like. In one example, the method may include monitoring data that is written to database tables of a database by a software process, identifying links between the database tables where the monitored data is written, determining whether the software process has a pre-existing data model for retrieving data stored in the database, and in response to a determination that the software process does not include the pre-existing data model, creating a new data model for the software process which includes names of the database tables where the monitored data is written and links between the database tables, and storing the new data model via a database repository.

Abstract: Methods and apparatus are disclosed for extracting structured content, as graphs, from text documents. Graph vertices and edges correspond to document tokens and pairwise relationships between tokens. Undirected peer relationships and directed relationships (e.g. key-value or composition) are supported. Vertices can be identified with predefined fields, and thence mapped to database columns for automated storage of document content in a database. A trained neural network classifier determines relationship classifications for all pairwise combinations of input tokens. The relationship classification can differentiate multiple relationship types. A multi-level classifier extracts multi-level graph structure from a document. Disclosed embodiments support arbitrary graph structures with hierarchical and planar relationships. Relationships are not restricted by spatial proximity or document layout. Composite tokens can be identified interspersed with other content.

Abstract: Disclosed is a framework for providing microservices for database with embedded container service in a container cloud platform. The framework includes a container cloud manager for performing distributed management of resources in a data center. The container cloud manager includes an orchestrator with a flexible job scheduler and operator pool. The flexible job scheduler and container cloud master form a flexible job management platform for managing jobs of the data center. The flexible job management framework can isolate various types of jobs as well as mix various jobs for efficient usage of hosts or resources in the data center. The operator pool includes optional components of a database. The orchestrator facilitates quick customization and release of database products, such as a HANA database with optional services, as requested by a user in the container cloud platform.

Abstract: The present disclosure relates to computer-implemented methods, software, and systems for managing asynchronous data replication in a multiple availability zone cloud environment. Metadata for files for asynchronous replication at a second availability zone is stored at an in-memory data grid of a first instance of a storage service at a first availability zone at a multiple availability cloud platform that provides storage services. The in-memory data grid includes a queue data structure of metadata records and a map of metadata records. In response to determining that connection from the first availability zone to the second availability zone is available, asynchronous data replication for files identified at the map is executed. A file for replication is identified at the map and provided for replication at a second file storage at the second availability zone through a replication interface of a second instance of the storage service at the second availability zone.

Abstract: The present disclosure involves systems, software, and computer implemented methods for creating line item information from tabular data. One example method includes receiving event data values at a system. Column headers of columns in the event data values are identified. At least one column header is not included in standard line item terms used by the system. Column values of the columns in the event data values are identified. The identified column headers and the identified column values are processed using one or more models to map each column to a standard line item term used by the system. The processing includes using context determination and content recognition to identify standard line item terms. An event is created in the system, including the creation of line items from the identified column value. Each line item includes standard line item terms mapped to the columns.

Abstract: Methods, systems, and computer-readable storage media for receiving a set of inference results generated by a ML model, the inference results including a set of query entities and a set of target entities, each query entity having one or more target entities matched thereto by the ML model, processing the set of inference results to generate a set of matched sub-sets of target entities by executing a search over target entities in the set of target entities based on constraints, for each problem in a set of problems, providing the problem as a tuple including an index value representative of a target entity in the set of target entities and a value associated with the query entity, the value including a constraint relative to the query entity, and executing at least one task in response to one or more matched sub-sets in the set of matched sub-sets.

Abstract: The present disclosure involves systems, software, and computer implemented methods for providing an asynchronous transport channel for OData data using web sockets. One example method includes upgrading a HTTP connection to a web socket connection in response to an upgrade request. The web socket connection is maintained, after the upgrading, for receipt of subsequent web socket requests. An OData request is received, from a consuming application, over the web socket connection. The OData request is translated to a format used by an OData runtime environment and sent to the OData runtime environment. A response to the OData request is received from the OData runtime environment. The response is incorporated into a web socket message. The web socket message is sent to the consuming application, over the web socket connection, in response to the OData request, with the web socket connection remaining open after the web socket message is sent.

Abstract: Methods, systems, and computer-readable storage media for determining an initial distribution based on a set of survey boundaries and historical O-data, the historical O-data being generated through execution of a computer-implemented service, the historical distribution including a first set of parameter ranges and a second set of parameter ranges defining a set of range cells, adjusting one or more parameters ranges of at least one of the first set of parameter ranges and the second set of parameter ranges, determining an adjusted distribution defining a range space for a computer-based survey, receiving X-data from a set of users participating in the computer-based survey, the X-data including responses of users in the set of users provided to the computer-based survey, providing a set of X-data to O-data range vector correlations, and executing analytics of the computer-implemented service based on the set of X-data to O-data range vector correlations.

Abstract: Disclosed herein are system, method, and computer program product embodiments for routing communication to applications. In an embodiment, first application can transmit a connection to a second application without knowing the exact route address of the second application. An agent can intercept the connection from a first application intended for a second application. The agent can identify the route address for the second application, determine the protocol the connection was intended to be transmitted, and forward the connection based on the route address and protocol to the second application.

Abstract: In an example embodiment, a self-supervised learning task is used for training commonsense-aware representations in a minimally supervised fashion and a pair level mutual-exclusive loss is used to enforce commonsense knowledge during representation learning. This helps to exploit the mutual-exclusive nature of the training samples of commonsense reasoning corpora. Given two pieces of input where the only difference between them are trigger pieces of data, it may be postulated that the pairwise pronoun disambiguation is mutually exclusive. This idea is formulated using a contrastive loss and then this is used to update the language model.

Abstract: Methods, systems, and computer-readable storage media for receiving an index and a random number from a verifier, receiving, from a shared data storage, encrypted supply chain data (SCD) of a supply chain actor (SCA), re-encrypting the encrypted SCD to provide re-encrypted SCD using a public encryption key of a decryption party, executing homomorphic operations on the re-encrypted SCD to provide an encrypted blinded result using second random numbers, receiving an encrypted modified blinded result, generating an encrypted blinded verification result using a verification function, the encrypted blinded result, the encrypted modified blinded result, and the random number, and sending the encrypted blinded verification result for decryption to provide a blinded verification result to the verifier, the verifier determining a verification result based on the blinded verification result and the random number, and comparing the verification result to a product claim.

Abstract: The layers of a neural network model are traversed in sequence one or more times while generating a plurality of relevance scores each time based on neuron weights and neuron biases of the neuron network model. Each relevance score of the plurality of relevance scores quantifies a relevance of a neuron in a lower layer of the sequence of layers to a higher layer of the sequence of layers. One or more relevance vectors can be populated from the plurality of relevance scores generated at the one or more times. Each of the relevance scores in each relevance vector quantifies a relevance of one of the input features to a task for which the neural network model is trained to perform. An explanation of a behavior of the neural network as a whole is generated based on the one or more relevance vectors.

Abstract: A network output is generated by feeding an input vector to an input layer of a neural network model having a plurality of neurons arranged in a sequence of layers, a plurality of neuron weights, and a plurality of neuron biases. The network output is used to determine an output relevance score. Relevance scores at a last layer of the sequence of layers are generated. Relevance scores are obtained at a first layer of the sequence of layers by reverse propagating the relevance scores generated at the last layer through the sequence of layers other than the last layer using the neuron weights and neuron biases. A feature relevance vector is formed based on the input vector and the relevance scores obtained at the first layer and included in a local explainability dataset, which is then used to generate a local explanation of a prediction of the neural network model.

Abstract: Disclosed herein are system, method, and computer tangible medium embodiments for generating a relational database model from a model view that provides an estimate for generating query plan for a query on the model view, according to an embodiment. A join model of a model view set on one or more database tables is received. At least one index conversion of the join model is performed, where the index conversion converts at least one index in the join model into at least one leaf node in a relational model tree. At least one join conversion of the join model is performed where the at least one join conversion generates a node in the relational model tree that joins a first and second leaf nodes in the relational model tree. A requested parameter conversion is performed, where the requested parameter conversion includes an attribute or key figure requested by a query.

Abstract: Systems and methods are provided for receiving a query created by a user, receiving output data of at least one function to retrieve data related to the query and analyzing the output data of the at least one function to retrieve data related to the query. The systems and methods further provide for generating at least one dynamic knowledge graph associated with the output data of the at least one function, wherein the at least one dynamic knowledge graph comprises data from the output data of the at least one function and indicates relationships between the data, analyzing the at least one dynamic knowledge graph to determine data relevant to the query generated by the user, and generating a response to the query based on the data relevant in the at least one dynamic knowledge graph.

Abstract: A system includes application of respective compression types to first data associated with each of a plurality of columns to generate compressed column data, determination of a first compression ratio for each of the plurality of columns based on the compressed column data, storage of the determined first compression ratios, application, for each of the plurality of columns, of the determined compression type to second data associated with the column to generate second compressed column data, determination of a second compression ratio for each of the plurality of columns based on the second compressed column data, determination of a value for each column based on the stored first compression ratio and the second compression ratio determined for the column, determination of a representative value of the determined values, and determination, based on the representative value, whether to re-determine a compression type for each of the plurality of columns.

Abstract: Techniques and solutions are described for configuring a computer-implemented process defined by a data model. The data model includes a plurality of data objects, each data object having an object type. Displays are rendered that request selection of first and second values for respective first and second data objects of first and second object types. The first and second values are assigned to the respective first and second data objects. The computer-implemented process defined by the data model is executed, using the first and second values, to provide execution results.

Abstract: Computer-readable media, methods, and systems are disclosed for managing group-level database encryption keys under group-level encryption in a database management system. Upon startup of the database management system, persisted database entries are sequentially processed to produce an in-memory data structure comprising a set of encryption group identifier metadata tuples having an encryption group identifier and a valid-from save point cycle version. The set of encryption group identifier metadata tuples is mapped to a set of key identifier tuples including a local secure store identifier and a group-level encryption key identifier. A set of group-level encryption keys is received from a key management system, according to which a group-level encryption key is mapped to each encryption group identifier metadata tuple.

Abstract: Methods, systems, and computer-readable storage media for determining, by an automated regression detection system (ARDS), that training of a ML model is complete, the ML model being a version of a previously trained ML model, and in response, automatically, by the ARDS: retrieving the ML model, executing regression testing and detection using the ML model, generating regression results relative to the previously trained ML model, and publishing the regression results.

Abstract: A method for performing a path finding operation in graph data stored in a database is provided. The method may include receiving, from a client, a request to perform a weighted path operation on at least portion of the graph data. The portion of the graph data may correspond to a graph. The request may specify attributes associated with vertices and/or edges included in the graph. In response to the request, the weighted path operation may be performed by at least identifying a shortest path between two endpoints in the graph. The shortest path may minimize the one or more attributes of vertices and/or edges included in the shortest path. Related systems and articles of manufacture, including computer program products, are also provided.