Siemens Aktiengesellschaft

Siemens Schweiz Ag

Tobias Aigner

Markus Sauer

Nejc Zupan

Saurabh Narayan Singh

Swathi Shyam Sunder

Prithvi Raj Ramakrishnaraja

Yogesh Kamath

Yogesh Borkhade

Nataliia Rümmele

Rani Joseph

A device for adding equipment data of a piece of equipment into a distributed database system includes a first communication interface designed to receive database configuration information from the distributed database system, a second communication interface coupled to the piece of equipment and designed to receive equipment data a configuration module designed to select at least one configuration parameter for configuring a data providing module, test at least one selected configuration parameter on the basis of the database configuration information, output the test result, and configure the data providing module, an identification module designed to provide identification information, a registration module designed to register the piece of equipment on the basis of the identification information for the distributed database system, and a data providing module designed to add the equipment data with respect to the identification information into the distributed database.

Device and method for adding equipment data into a distributed database system

Provided is a method for operating a field device. The field device is configured for interacting with a control device. According to the method, a base key is received from a key generation device. A current constraint parameter relating to a predefined operating constraint for operating the field device is determined. A key is generated based on the base key and the current constraint parameter. The field device interacts with the control device using the generated key.

Constrained operation of a field device

A system and method for data management is provided. The method includes obtaining a dataset from a data source by a processing unit. The dataset includes a plurality of datapoints, and each of the datapoints belongs to a column among a plurality of columns. Further, an ontology label for at least one column in the dataset is predicted using a machine learning model. The predicted ontology label is associated with an ontology comprising a plurality of ontology labels. Further, a mapping between the dataset and the ontology is generated based on the relation between the predicted ontology label and the column. Furthermore, the datapoints are classified with respect to the ontology labels based on the mapping generated. The classified datasets are outputted on a user interface.

System and method for data management

In a computer-implemented method for storing data in a network of linked computing units () using a distributed transaction database (GDB), a distributed transaction database (GDB) in the form of a distributed graph database formed using nodes is used, and data is stored in at least one node (N) of the graph database, wherein the node (N) is stored using a real sub-quantity of the computing units () of the network. The computer program product can be loaded directly into a storage device of an electronic computing unit () and has program means in order to early out the steps of the method when the program is ran in a computing unit. The network of linked computing units () stores a distributed transaction database (GDB) in the form of a distributed graph database comprising nodes (N) in which data is stored according to such a method.

Computer-implemented method for storing data using a distributed transaction database, computer program product, and network

Various embodiments of the teachings herein include a computer-aided method for transforming data in a relational database, containing sensor measurements, into RDF data blocks of a graph database. The method may include: providing a R2RML mapping file; breaking down and converting the data using the mapping file and a first mapping parser; generating a generation of RDF data blocks; and storing the generation as a database. After the data have been broken down and converted, checking a quality of the obtained R2RML mapping and creating a second R2RML mapping file, on the basis of which the relational data are broken down and converted into RDF data blocks. The second R2RML mapping, during the preparation of the relational data into RDF data blocks, automatically stops the processing of relational data that are not to be resolved and thus optimizes the energy efficiency of the preparation.

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Munich, Germany

Tobias Aigner