Abstract: In some embodiments, there is provided generating a logical query plan for a query that includes a single fact table and multiple reducer tables; optimizing the logical query plan for query execution; and executing the optimized logical query plan.

Abstract: Software performance evaluation and support is achieved proactively in a cloud architecture by collecting Key Performance Indicators (KPIs) of software performance across multiple tenants. Such software performance KPIs can include but are not limited to data volumes, backend calculation runtimes, and front end request runtimes. Influencing factors of a software performance model (e.g., model complexity, calculation complexity; application complexity) may be gathered. The collected data is referenced as a training corpus to create an Artificial Intelligence (AI) revealing correlation between model characteristic(s) and software performance. The AI is applied in the form of predictive analytics and/or Robotic Process Automation (RPA) to proactively identify software performance challenges and issue alerts. Embodiments may define lower thresholds and/or performance degradations that result in alert triggering.

Abstract: A method includes determining a correlation feature shared among at least a portion of a plurality of tracking documents including a first tracking document and a second tracking document. The method includes receiving a first batch of tracking documents associated with a logistics process. The tracking documents includes the correlation feature and logistics information associated with the logistics process. The method includes generating a first correlation between the first tracking document and the second tracking document. The method includes receiving a second batch of tracking documents. The second batch includes a third tracking document including a change in the logistics information indicated by the first tracking document. The method includes generating a second correlation between the third tracking document and the second tracking document. The method includes sending a status of the logistics process. Related systems and computer program products are also provided.

Abstract: This disclosure describes systems, software, and computer implemented methods for generating a flexibly scalable database with native internationalization. This can include partitioning a dataset into a group of subsets based on one or more attributes; initiating a plurality of in-memory database instances in a containerized computing environment, wherein there is at least one in-memory database instance in the plurality of in-memory database instances for each database subset of the group of subsets; loading, by a particular in-memory database instance, a particular database subset; receiving a query at a database gateway from an application layer, wherein the query identifies a particular attribute of data to be queried; identifying, by the database gateway, the particular database subset; directing, by the database gateway, the query to the particular in-memory database instance, and receiving a result; and returning the result to the application layer.

Abstract: In an example embodiment, a persistence model is utilized that allows the storage of value lists in a referenceable and reusable manner. This allows for two lifecycle options for value lists: (i) schema-dependent and (ii) schema-independent. Thus, the lifecycle of all involved entities (e.g., schemas, values, correspondences, etc.) is managed. This enables easier upgrades, downgrades, and sidegrades. The persistence is a directed graph, which comprises nodes and directed edges. This persistence can then be used to recommend additional correspondences to a user.

Abstract: In an example embodiment, a templating and detemplating component that allows for transparent integration with a cloud environment irrespective of the implementation is provided. It allows for two distinct ways of making changes in the cloud environment: (1) execution of commands through a command line tool; and (2) creation of custom resources by a user or system component.

Abstract: Methods, systems, and computer-readable storage media for querying database systems using queries generated by prompting of LLMs to generate query statements from natural language, where a LLM system provides a set of query statements in response to user input that is provided in natural language, the set of query statements includes a first query statement provided using an in-context prompt, a second query statement provided using a chain-of-thought (CoT) prompt, a third query statement provided using a contrastive CoT prompt, and a fourth query statement provided using a mixture-of-experts (MoE) prompt, and a query statement is selected from the set of query statements and is used to query a database system, which returns a query result responsive to the query statement.

Abstract: Provided are systems and methods that facilitate the upgrade of multiple versions of a software application within a containerized environment. In one example, the method may include importing a software update of a software application into a prior version of the software application in a containerized environment, executing a test of the prior version of the software application with the software update included therein via the containerized environment and display results of the execution of the test via a user interface, modifying the prior version of the software application with the software update included therein while the prior version of the software application with the software update included therein is within the containerized environment, and delivering an instance of the modified prior version of the software application from the containerized environment to a productive environment.

Abstract: Techniques for automating a software application subscription process are presented herein. In an example, in response to receiving a subscription request from a client device, a subscription automator extracts a plurality of parameters from the subscription request. Also, the subscription automator retrieves a given script template from an automation framework, where the given script template corresponds to a given cloud service application to which one or more users wish to subscribe. The subscription automator generates an executable script by populating the first script template with the plurality of parameters. Next, the subscription automator causes the first executable script to be executed to initiate a subscription process of the given cloud service application. Then, the subscription automator creates subscriptions to the given cloud service application for the one or more users as a result of initiating the subscription process.

Abstract: Systems and processes for evaluating algorithms for aligning weakly-annotated data to recognized characters in a document are provided. In a method for evaluating an algorithm for aligning annotation data to recognized characters, strong annotations and weak-to-strong annotations, which are generated by applying a weak-to-strong annotation alignment algorithm, for a document are received and matched to generate respective pairs of matched annotations. For each pair of matched annotations, respective metrics are calculated including comparisons of aspects of the strong annotations to the weak-to-strong annotations. The respective metrics are aggregated, and an indication of the aggregated metrics are output to a graphical user interface or targeted application. Aggregated metrics determined for different weak-to-strong annotation alignment algorithms may be compared in order to select or adjust an algorithm to be used for Optical Character Recognition (OCR) operations.

Abstract: Systems and methods described herein relate to the assignment of user devices to workload clusters. Resource utilization on a plurality of user devices is monitored. A device agent on each user device may be used to monitor the resource utilization. A workload execution request identifies resource requirements of a cluster workload. The workload execution request is assigned to a cluster based on the resource requirements of the cluster workload and the resource utilization on the plurality of user devices. The cluster comprises the plurality of user devices. The workload execution request is caused to be executed on the cluster. Each user device executes user workloads and a respective portion of the cluster workload.

Abstract: Techniques and solutions are provided for generating data that complies with a definition for a data artifact, such as attribute datatypes, attribute value constraints, or attribute referential constraints. In addition, the data is semantically appropriate for what a given attribute of the data artifact represents, such as a name or an email address. A schema stored in a computing system is accessed to retrieve a definition of a data artifact. The schema is parsed and information about attributes for the data artifact are added to a prompt. The prompt is submitted to a natural language generator. The natural language generator generates data sets, such as rows of a table, in response to the prompt. The data sets are then processed and inserted into a data repository.

Abstract: Confidentiality and privacy of user comments are preserved by encryption of comments stored on the cloud. A migration engine and a crypto server including an encryption engine, are positioned behind a firewall from the cloud storage. In response to a comment migration instruction (e.g., •onboarding, •offboarding, or •key rotation), the migration engine performs batch processing across the firewall to retrieve a comment and comment metadata from the cloud storage. The migration engine forwards the comment/comment metadata behind the firewall to the crypto server. Based upon the comment metadata, the encryption engine uses a key for encrypting or decrypting the comment. Once encrypted or decrypted, the comment is returned to the migration engine for forwarding across the firewall and storage on the cloud. Encrypting or decrypting comments using a key determined from comment metadata, can impart flexibility to comment migration (e.g., allowing an administrator to selectively rotate compromised or expired keys).

Abstract: Systems and methods include determination of a location of each of a plurality of movable service hubs, generation of a first map depicting the determined locations of each of the hubs, association of a first service with a first location of the depicted locations, wherein the first service is provided to a first hub located at the first location based on the first map, determination that one or more of the hubs has moved more than a threshold distance, determination of a second location of each of the hubs and generate a second map depicting the determined second locations of each of the hubs, and association of a second service with a third location of the depicted second locations, wherein the second service is provided to a second hub located at the third location based on the second map.

Abstract: A computer-implemented method can receive a submitted vulnerability specifying a vulnerability type and steps to reproduce a suspected bug of a software, determine a first base model score based on keywords appeared in the vulnerability form, determine a second base model score based on vulnerability type-specific contextual information extracted from the vulnerability form, determine a first probability modifier based on features identified from the vulnerability form, determine a second probability modifier based on mapping any file attachment associated with the vulnerability form to a corresponding step, determine a third probability modifier based on pairing and grouping selected steps, determine a vulnerability score based on the first and second base model scores, and the first, second, and third probability modifiers, and classify the vulnerability form as valid or invalid based on the vulnerability score. Related systems and software for implementing the method are also disclosed.

Abstract: A computing system receives a machine learning model that has been trained using historical event data to predict deviations between scheduled and actual event outcomes. Example training include temporal attributes, contextual attributes, or observed deviation patterns. The system processes attributes of a planned event using the trained model to generate a predicted deviation. If the predicted deviation exceeds a configurable threshold, the system initiates at least one of: generating an alert, blocking execution of related operations, adjusting scheduling parameters, modifying execution conditions, receiving user input to override an alert or adjust scheduling data, or dynamically updating threshold parameters based on historical deviation trends or real-time conditions. The technology can be integrated into workflow automation, scheduling optimization, and decision-support applications to improve planning accuracy, operational efficiency, and predictive reliability across various domains.

Abstract: Dynamic logging adjustment can be performed during a debug session to facilitate issue resolution. A user of a software platform can initiate a debug session with a specified logging profile. The logging profile includes respective log levels for backend components of the software platform which only apply to server requests associated with the debug session. During the debug session, the user attempts to reproduce the issue which triggers corresponding server requests. The server determines whether a given server request is part of the debug session, and if so, dynamically adjusts the log level for actions associated with the server request. A debug session identifier for the debug session is inserted in log entries associated with the server request. After the debug session has concluded, server logs can be filtered by the debug session identifier to obtain a targeted set of log entries pertaining to the issue.

Abstract: In some implementations, there is provided query execution optimization for a top k query that includes in response to a first value being less than a first heap value, selectively materializing a second value corresponding to a second field and inserting the first value and the second value into the heap, and in response to the first value not being less that the first heap value, skipping the first incoming element and proceeding, when available, to another incoming element. Related systems, methods and articles of manufacture are also disclosed.