Sap Se

Aavishkar Bharara

Ankita Prabhu

Prabal Mahanta

Bhavneet Kaur

Ashwani Kumar Luhaniwal

Anil Rao Arun

Ramya K S

Swarnima Sneh

Amritha Pulikool

Deepanshi Katoch

Anbarasu Ayyasami

Shrijan Shrivastav

Technologies are described for managing database queries based on bill of materials (BOM). For example, a set of database queries can be optimized (e.g., in terms of time and sequence of execution) based on a BOM explosion. In some implementations, database queries are managed by generating a BOM for an application programming interface (API) call. Various data structures can be created from the BOM, which can comprise procedure information, application and action information, and/or hierarchy information. A logical sequence of execution can be determined based, at least in part, on one or more of the data structures, where the logical sequence of execution comprises a plurality of database queries to execute the API call. The logical sequence of execution can be optimized to produce an optimized sequence of execution. The API call can be executed according to the optimized sequence of execution.

Bill of materials traversal to handle logical units of work for in-memory databases

Systems and methods include receiving an exception raised by a database storage system in response to a database operation requested by an application, providing, in response to the exception, the application with a pointer to a failover framework comprising a memory store, receiving, from the application and at the failover framework, a request to write first data, storing, in response to the request to write first data, the first data in a tree structure of the memory store in chronological order, receiving, from the application and at the failover framework, a request to read second data, and, in response to the request to read second data, sorting the tree structure primarily by user and secondarily by chronological order and reading the second data from the sorted tree structure.

Failover system for database unavailability

A significant correlation framework is provided herein for translating input commands to intents. The input commands may be natural language commands, received from a variety of input channels, which may be translated to intents or other runtime-bindable execution objects. The significant correlation framework may use interpreter nodes for translating the input commands by calculating the strength of correlation between an input command and an intent. The significant correlation framework may analyze the sequence of intents or the timing of translated intents to enhance the accuracy of the translation. The significant correlation framework may maintain a history of command translations, and may compare current translations against the history to improve accuracy of the translations. The significant correlation framework may switch between a depth-first mapping method and a breadth-first mapping method. Depth-first mapping may translate commands through a single interpreter node. Breadth-first mapping may translate commands using multiple interpreter nodes.

Significant correlation framework for command translation

Examples of workflow replication and execution error analysis are provided herein. Data describing how a user interacts with a software application and describing the context within which the user is working is recorded and provided to a user workflow replication system when an execution error occurs. A simulation of the execution error can be performed by replicating a configuration of the software application and/or the computer system that executed the software application and then performing functions specified in the provided data. The results of the simulation of the execution error can then be analyzed according to a number of scenarios.

User workflow replication for execution error analysis

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Aavishkar Bharara