Sap Se

Kefeng Wang

Hartmut K Vogler

Aaby Sivakumar

Raghavendra Deshpande

Prakash Ponshankaarchinnusamy

Priyanka Gupta

Mamatha M Raj

Rashmi Rk

Andreas Doehrn

Sonali Jha

Gurinder Bahl

Rathnakumar G

Kumar Rishav

Anil Kumar R

Thakur Nishant Singh

Dimitrij Raev

Sven Hader

Torsten Heise

Andreas Seifried

Tarek Auel

Lukas Mairon

Maiko Mueller

Sudhir Rao

Michael Boettcher

Kieran Turley

Birgitta Bruegel

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

A method of enhancing one or more fields of a master database record based on a consumption of one or more data points provided via a third-party data subscription service is disclosed. An aggregated view corresponding to a master database record is accessed. The aggregated view identifies correspondences between the one or more fields of the master database record and the one or more data points provided via the third-party data subscription service. Based on a determination that one or more consumption criteria are met, the one or more data points are consumed and stored in a central database. The one or more fields of the master database record are updated based on the one or more consumed data points. The enhanced master database record is provided via a network to one or more computerized processes associated with one or more consumers of the enhanced master database record.

Data cloud&#x2014;platform for data enrichment

Techniques and solutions are described for determining association rules using a machine learning technique on a subset of data to which the association rules might apply, and from which they can be determined. In particular, association rules are determined by tracking changes to attribute values of data objects having a type. The changed attribute value can be used as a consequent in an association rule. Values of other attributes of data objects having the changed attribute value can be used as antecedents in association rules. Values used in antecedents can be constrained, such as by limiting values to those associated with scope attributes or values satisfying a threshold occurrence frequency. In some cases, determined association rules can be automatically implemented, such as to process input or stored data for data objects of the type.

Efficient data relationship mining using machine learning

Transaction data is received from a remote client computing device that includes user-generated entries in each of a plurality of fields. Thereafter, it can be determined, using an outlier detection algorithm, that values for one or more of the entries is an outlier. Data can then be provided (e.g., displayed in a visual display, loaded into memory, stored in physical persistence, transmitted to a remote computing system, etc.). The outlier algorithm can be based on a number of similar records g, a number of distinct values d(g) in the similar records, and a number of same values s in the similar records. Related apparatus, systems, techniques and articles are also described.

Intelligent outlier data detection

Provided are systems and methods for generating a hierarchy. In one example, a method includes receiving a first data graph including a plurality of nodes and links connecting the plurality of nodes, receiving a second data graph including a second plurality of nodes and links connecting the second plurality of nodes, generating a hierarchy based on the first data graph and the second data graph, where the generated hierarchy includes a plurality of levels, nodes from the first data graph arranged on at least one level, nodes from the second data graph arranged on at least one other level, and links connecting the nodes from the first data graph and the nodes from the second data graph, and indicating a relationships between the first and second data items, and outputting the generated hierarchy for at least one of display and further processing.

Dynamic hierarchy generation based on graph data

Methods and systems are described for receiving data including a hierarchy change log comprising change records specifying changes made to hierarchical data. The hierarchical data includes hierarchically arranged nodes. Change records are grouped according to a key corresponding to each record. Using a record consolidation algorithm, a consolidated view of the hierarchy change log is generated. A consolidated view can be generated by sorting, for each group, the change records into an ascending temporal order based on their respective timestamps to form a sequence of change records. The consolidated view is then displayed on an electronic visual display. Related apparatus, systems, techniques and articles are also described.

Hierarchically stored data processing

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Wiesloch, Germany

Kefeng Wang