Sap Se

Dimitrij Raev

Stefan Kraus

Joerg Weller

Joerg Franke

Hari Venkatesh Subramanian

Marco Pesarese

Kefeng Wang

Andreas Seifried

Florian Roll

Helmut Holthoff

Julian Frank

Kieran Turley

Ruixing Yang

Birgitta Bruegel

Waldemar Isajkin

Dirk Riemer

Mikhail Goncharov

Techniques and solutions are provided for improving query execution. Data models can be complex, which is often reflected in queries against such data models. The present disclosure provides a query refactoring technique where a complex query, such as a query expressed as a single select statement, can be formulated as a series of less complex queries. The workload of a database can be reduced by combining results of the less complex queries outside of the database. The present disclosure provides a framework for implementing these techniques, where the framework includes a virtual cube, a calculation engine, and one or more operations, which can all be implemented as classes in a programming language, and where a generic class or interface can help guide users in developing subclasses that provide a reformulation or refactoring of a complex query.

Query refactoring framework

Techniques and solutions are provided for processing query requests from a software application, such as one having a user interface model, using an analytical data protocol that accesses an analytic query. Often, user interface models access data using transactional data protocols, which can limit analytical actions that can be performed through a user interface, particularly actions altering data presented or a data format as compared with pre-defined analytical objects. A query request associated with a user interface query model is received and converted to be executable using at least one analytical query model object. The request, in an analytical protocol, is submitted to a virtual data model. The query request in the analytical protocol is converted to be used with an analytic query defined in the virtual data model. The converted query request is executed against a data store and query results are returned to a user interface layer.

Data retreival framework and use thereof

Techniques and solutions are provided to assist users in creating an instance of a model object definition that can be used in generating user interface displays using data accessed using an analytic query. Often, the development of user interface displays, including binding aspects of a display to data, requires significant technical knowledge, and the user interface displays and supporting objects are often hardcoded for particular use scenarios. Disclosed techniques facilitate the creation of user interface displays by allowing users to select particular definitional elements of analytic queries and analytical data objects defined with respect thereto. The definitional elements are stored in an instance of a model object definition. Using disclosed techniques, users can create and modify user interface displays using a low code/no code approach.

User interface models providing dynamic analytical capability

Techniques and solutions are provided for updating or augmenting consolidated data that is produced using base data. The consolidated data can include data that is aggregated by various grouping criteria. After a set of consolidated data is determined, the base data may change, one or more rules used to calculate the consolidated data may change, or it may be desired to see data that is more granular than that included in the consolidated data. After consolidated data is provided to a user, a user issues a data augmentation request. The data augmentation request causes the base data, which may have been updated, to be processed to provide updated data, where the processing includes grouping operations used in producing the consolidated data. The updated data is provided to a client in response to the data augmentation request.

Dynamic update of consolidated data based on granular data values

Data is received that defines a rule mining run including a scope of a search and at least one data source to be searched. In response, the at least one data source is polled to obtain rules responsive to the rule mining run. Each rule can specify one or more actions to take as part of a computer-implemented process when certain conditions are met. A list of rules (i.e., a proposed subset of the obtained rules) can then be generated using at least one machine learning model. The generated list of rule can then be displayed in a graphical user interface. Related apparatus, systems, techniques and articles are also described.

Machine learning-based rule mining algorithm

In an example embodiment, a specialized in-memory database abstraction component is introduced in a cloud cluster. The in-memory database abstraction component may receive lifecycle commands from a client-facing application and interface with a container service to create an in-memory database resource. When parameters are received by the in-memory database abstraction component from the client-facing application, the in-memory database abstraction component may act to validate the parameters, determine if a service plan is available, and determine whether the parameters meet the service plan requirements. If the service plan requirements are not met, the in-memory database abstraction component translates the parameters for the in-memory database resource.

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Mannheim, Germany

Dimitrij Raev