Sap Se

Janick Frasch

Robert Meusel

Axel Kuhle

Lukas Brinkmann

Roman Rommel

Jaakob Kind

Atreju Florian Tauschinsky

Jan Patrick Klein

Michael Otto

Santo Bianchino

Minji Lee

Masoud Aghadavoodi Jolfaei

Brian Craig McKellar

Philipp Knuesel

Steffen Knoeller

Benjamin Fischer

Markus Seidl

Florian Weigold

Ralf Schoenfeld

Ralf Vath

Jan Gottweiss

Alexandru Radu

Simon Hoppermann

Andre Sres

Mirko Hin

Linda Jahn

Kuan Lu

Dominik Heere

Sven Peterson

Sven Petersen

Computer-readable media, methods, and systems are disclosed for scheduling a start time and a shutdown time of one or more online resources associated with a multi-cloud resource scheduler. A request from a first user is received to access a multi-cloud resource scheduler associated with one or more online resources. Responsive to the request from the first user, credentials of the first user are validated prior to providing access to the multi-cloud resource scheduler. Based upon validating the credentials of the first user, access to the multi-cloud resource scheduler is provided. Instructions are received from the first user to schedule a start time and a shutdown time of at least one online cloud resource connected to the multi-cloud resource scheduler. An availability of the at least one online cloud resource is established for access by a second user based on the instructions.

Multi-cloud resource scheduler

Computer-readable media, methods, and systems are disclosed for storing and analyzing dynamic data within a semantic data store. The dynamic data comprises one or more types of data having a normalized data schema. A dynamic data manager interfaces with the semantic data store to instruct storage of the data. The data may be received through an event service from either of an external data source or an internal data source.

Dynamic data processing for a semantic data storage architecture

Visualization of time series data includes sending to the data source a request for visualizing sensor data within a canvas having a width of w pixels and covering a visualization time range, each pixel of the canvas being representative of a time duration I, receiving consecutive sets of tuples that each covers a time interval having the time duration I, performing a M4 aggregation comprising generating, from the received tuples, a set of consecutive w groups, each group of the w groups comprising tuples covering a time interval having the time duration I, and determining for each group of the w groups a set of aggregates, and displaying the w sets of aggregates on the canvas of the browser window as a chart, wherein each one of the sets of aggregates is displayed in one of pixel columns of the canvas.

Visualizing a time series relation

In some implementations, there is provided a method, which includes transforming, by the recommendation system, a first data set into the principal component analysis domain; rotating, by the recommendation system, the transformed first data first data set into a common axis system; comparing, by the recommendation system, the rotated, transformed first data set to at least one of a plurality of reference data sets having been rotated into the common axis system; and identifying, by the recommendation system, at least one reference data set, the identifying based on the comparing. Related systems, methods, and articles of manufacture are also disclosed.

PCA-based scoring of the similarity of damage patterns of operational assets

Some embodiments include reception of a time-series of a respective data value generated by each of a plurality of sensors, calculation of a regression associated with a first sensor of the plurality of sensors based on the received plurality of time-series, the regression being a function of the respective data values of the others of the plurality of data sources, reception of respective data values associated with a time from and generated by each the plurality of respective sensors, determination of a predicted value associated with the time for the first sensor based on the regression associated with the first sensor and on the respective data values associated with the time, comparison of the predicted value with the received value associated with the time and generated by the first sensor, and determination of a value indicating a likelihood of an anomaly based on the comparison.

Sensor data anomaly detection

In some implementations, there is provided a method, which includes receiving, at a recommendation system, a failure notice representing a failure detected by a sensor monitoring an object; comparing, by the recommendation system, the failure notice to a plurality of reference failure notices to identify at least one matching failure notice, the comparing based on a domain-by-domain scoring between the failure notice and the plurality of reference failure notices; and generating, by the recommendation system, a message including a suggested solution including at least one task to remedy the failure, the suggested solution obtained from the at least one matching failure notice. Related systems, methods, and articles of manufacture are also disclosed.

Record-based planning in operational maintenance and service

Growing community of inventors

Mannheim, Germany

Janick Frasch