SAP

In an example embodiment, a mechanism is provided to allow an LLM to generate multiple text blocks in which different grammars are strictly enforced with a single invocation. This mechanism defines a set of trigger tokens and end tokens. When a trigger token is encountered by the LLM, a strict grammar referenced by the trigger token is begun to be enforced and this enforcement ends when an end token is encountered. Between the time an end token is encountered, and another trigger token is encountered, no grammar is strictly enforced. By including multiple types of such trigger token/end token pairs, it becomes possible for the LLM to generate texts having different strictly enforced grammars in a single invocation.

A system and method include receipt of a query from a user, determination, from a plurality of stored text portions, of first text portions which are semantically similar to the query, determination of a first score associated with each of the first text portions, generation of a first prompt based on the first scores, the first prompt including the query and the first text portions, transmission of the first prompt to a text generation model, receipt of a response to the first prompt from the text generation model, presentation of the response and the first text portions, receipt, from the user, of a rating of one of the presented first text portions, and updating of the first score associated with the one of the first text portions based on the rating.

A transaction which targets an object store employing an open table format (OTF) may be detected by a database execution engine. Next, the database execution engine performs a determination step to determine which OTF enhancement levels to apply to the detected transaction. Then, the database execution engine applies one or more OTF enhancement levels to the transaction based on the determination step. Next, the database execution engine causes the transaction to be processed with the applied one or more OTF enhancement levels. Then, the database execution engine returns a result of the transaction processing to a computing device.

A system and method include execution of a chatbot session consisting of user queries and chatbot feedbacks, insertion of one of the chatbot feedbacks into an analytics document as analytical insight data, reception of a user selection of one or more of the chatbot feedbacks, generation of a data lineage graph from the selected one or more of the chatbot feedbacks, and storage of the data lineage graph in association with the analytical insight data.

Techniques and solutions are disclosed for managing subprocess versions and their associated components in a computing system. These techniques enable dynamic adaptation and traceability through version control. A subprocess definition is received and updated to reflect modifications to its components, configurations, or execution sequence. Changes are identified and propagated using unique version identifiers and events. Iterative refinement is supported through actions such as validating updated subprocesses, reprocessing data, or maintaining provenance chains. The disclosed solutions provide efficient and structured management of subprocesses.

A computer implemented method receives a query to perform a join operation that joins a first table with a second table based on matching values in a selected column of the first table with values in a selected column of the second table, obtains a first dictionary for the selected column of the first table and a second dictionary for the selected column of the second table, and determines an output size of the join operation based on the first dictionary and the second dictionary. A dictionary for a given column includes a plurality of unique values in the given column and corresponding range indices. A count of a given unique value in the given column is mapped to a range associated with a range index corresponding to the given unique value. Related systems and software for implementing the method are also disclosed.

A database client may establish a database session with a database server, including a session state, for a first TCP network connection through a first reverse proxy. A reattachment token is sent to the database client, and a database operation request is received from the database client through the first reverse proxy. Before or during execution of the database operation request (including any updates to the session state) an indication is received that the first reverse proxy will be terminated after a reattachment window. The server instructs the database client to perform a session reattachment and receives, from the database client, a session reattachment request, including the reattachment token, for a second TCP network connection through a second reverse proxy. The server verifies the reattachment token and resumes the database session with the database client, including the updated session state, for the second TCP network connection.

An extractor engine receives one or more metric rules defining a set of metrics to extract from a source database for insertion into a target database. Also, the extractor engine retrieves a schema of the source database. Next, the extractor engine generates, based on the one or more metric rules and the schema of the source database, one or more query language statements for extracting the set of metrics from the source database for insertion into the target database. Then, the extractor engine causes the generated one or more query language statements to be executed to extract the set of metrics from the source database and insert the set of metrics into the target database. Next, a database execution engine and/or the extractor engine generates a dashboard of the plurality of metrics in the target database and causes the dashboard to be displayed in a user interface.

Techniques and systems are disclosed for evaluating and optimizing logical plans using machine learning. In an aspect, a logical plan is received and submitted to a machine learning model, which predicts whether applying a rule to the logical plan provides a benefit, such as reduced execution time or resource use. Based on the prediction, the rule is applied or marked for application, or not applied or not marked for application. Training operations for the machine learning model include calculating and comparing execution costs of applying or not applying a rule, generating a numerical representation of the logical plan, and assigning labels based on cost differences. A machine learning model trained with the representations and labels predicts rule benefits for logical plans, including both new logical plans and those used during training.

The present disclosure relates to computer-implemented methods, software, and systems for providing data access to shared data. One example method includes managing a collection of records associated with interactions of users with one or more of a set of software products defined at a platform solution, wherein the collection of records is obtained according to relevant compliance policies for data storage defined correspondingly for the set of software products; and in response to receiving a request for data management in relation to a first record of the collection of records, determining a relevant compliance policy for the first record, the relevant compliance policy being associated with a respective software product of the set of software products; and providing access to the first record in accordance with a scope determined for the first record, the scope being based on the relevant compliance policy.