SAP

The present disclosure involves systems, software, and computer implemented methods for process control for warehouse operations. One example method includes identifying, for a warehouse, a warehouse request that includes a set of warehouse request items. A data model is used to identify, for each warehouse request item, a warehouse process data object instance associated with a warehouse process for handling the warehouse request and a warehouse process control configuration data object instance for the warehouse process data object instance that defines both a sequence of operational steps of the warehouse process and physical movement of goods through the warehouse. Progress of each warehouse request item is tracked using warehouse task objects and an intermediary warehouse progress object. New warehouse task objects are created based on the warehouse process control configuration object. A determination is made that a final warehouse task has been completed for the warehouse request item.

Systems and methods described herein relate to extending knowledge graph data structures. A system accesses a knowledge graph data structure stored in a data store and preprocesses it to generate a modified knowledge graph data structure by replacing instance nodes with class nodes. The system automatically detects a pattern through processing of the modified knowledge graph data structure by initiating a path from a starting class node and recursively extending the path, until a stopping condition is met, with at least one property-class combination from a current class node in the path having an occurrence frequency that exceeds an occurrence threshold. The system then adjusts the knowledge graph data structure based on the detected pattern.

Methods, systems, and computer-readable storage media for a hierarchy function result cache that supports dynamic conditions. An example method includes identifying, in a database system, a request to execute a query that includes a hierarchy function. Dependency objects of the hierarchy function are collected. In response to determining that each dependency object is cacheable, dynamic condition information is collected that includes dynamic conditions of the collected dependency objects and corresponding objects of the dynamic conditions. A cache key is generated based on the collected dependency objects and collected dynamic condition information. A hierarchy function result cache is searched for a cache result that matches the cache key. In response to locating the cache result that matches the cache key, the cache result is identified as a hierarchy function result without executing the hierarchy function. The hierarchy function result us used to generate a query result for the query.

In an example embodiment, a software solution is provided that automates various aspects of a Period End Close (PEC) process and its evaluation. This provides a proactive and autonomous system that addresses complications that occur with prior art ERP systems involving PEC activities and provides graphical user interfaces for interacting with the system.

Some embodiments are directed to controlling task execution in a hyperscaler. An estimated future success metric for a task may be compared to a minimum acceptable success threshold and in response to determining that the likelihood of successful completion is below the minimum acceptable success threshold, the task is be queued for deferred execution, or otherwise passed to a scheduling mechanism of the hyperscaler for execution.

Methods, systems, and computer-readable storage media for receiving a first job with a first time-series of a first type of historic resource utilization and a second time-series of a second type of historic utilization, receiving a second job with a third time-series of the first type of historic resource utilization and a fourth time-series of the second type of historic utilization, determining a first correlation coefficient between the first time-series and the third time-series, determining a second correlation coefficient between the second time-series and the fourth time-series, combining the first correlation coefficient with the second correlation coefficient to generate a first total correlation coefficient, and in response to the first total correlation coefficient being below a threshold, transmitting the first job and the second job as a first job pair to a first executor of the plurality of job executors to be executed concurrently by the first executor.

Some embodiments are directed scheduling maintenance in a computer system. This may include calculating for future time frame, a risk factor indicative of a likelihood of violation of one or more predefined system performance requirements, the risk factor being based on a predicted quantitative compliance metric and on an estimated impact of the proposed maintenance activity.

A computer-implemented method can receive a vulnerability report for a software. The vulnerability report specifies a vulnerable library used by the software and a path of the vulnerable library within the software. The method can generate a summary of the vulnerability report using a generative artificial intelligence (AI) model, retrieve, from a bug report database, a set of relevant bug reports specifying the vulnerable library, generate a synopsis for the set of relevant bug reports using the generative AI model, generate multiple preliminary decisions on validity of the vulnerability report using the generative AI model based on the summary of the vulnerability report and the synopsis for the set of relevant bug reports, and generate a final decision on validity of the vulnerability report based on the multiple preliminary decisions. Related systems and software for implementing the method are also disclosed.

Each event type in a business process is represented by a node in a process discovery graph. Semantic clustering merges nodes based on a commonality between them. As a result, a graphical user interface (GUI) may be presented that shows fewer nodes. This process may be repeated, using different commonalities at each level. A user may select a node to zoom in, showing the constituent nodes. The frequency of an event or its other attribute values may be compared to a predetermined threshold to determine if the event is a blocking event. The GUI may indicate that the node for an event is a blocking event. When the node for the event is merged with other nodes using semantic clustering, the blocking status of the node is transferred to the merged node. After zooming in on the blocking event, the GUI allows the user to select the blocking event to cause display of a recommendation.

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.