Abstract: 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.

Abstract: System and methods for configuring a technical system based on generated rules and building the technical system. The technical system and the generated rules are given in graph representations including the following steps: defining rules by a user and representing the rules in a graphical interface, converting the rules from the graphical interface into a programming language and/or a natural language, validating the rules for the technical system, checking the compatibility of the rules, serializing the rules for storage in a file system or a database, using the serialized rules to configure the technical system, and building the configured technical system.

Abstract: A solution for automated column type annotation maps each column contained in a table to a column annotation class. A pre-processor transforms the table into a numerical tensor representation by outputting a sequence of cell tokens for each cell in the table. A table encoder encodes the sequences of cell tokens and a column annotation label for each column into body cell embeddings. A body pooling component processes the body cell embeddings to provide column representations. A classifier classifies the column representations to provide for each column, confidence scores for each column annotation class. The method concludes with comparing the highest confidence score for each column with a threshold, and, if the highest confidence score for each column is above the threshold, annotating each column with the respective column annotation class.

Abstract: A classification model is trained with elements from several data sources, with the elements including sensor data mounted in an industrial plant, and with the labels indicating a semantic type for each of the elements. The classification model is retrained with an adaptive learning algorithm implementing active learning and/or incremental learning, until the classification model is capable of mapping each element of the data sources to one of the semantic types. The method and system provide a semantic mapping for sensor data. The automated or semi-automated creation of the semantic mapping loosens the coupling between a domain expert and data scientist, serves as a bridge and reduces workload, speeding up data modeling and data integration steps. It provides inexperienced users with access to domain expertise. Re-use of data models is facilitated, which simplifies further integration and exchange activities. The adaptive learning algorithm provides an incremental enhancement of the classification model.

Abstract: The disclosed relates to a system for generating a refined query, whereby the system comprises or is coupled with a search engine for searching through a tree of query modification operations, whereby the root node of said tree is an empty node which represents a given initial query, and comprises at least one processor which is configured to perform the following steps: a) defining a set of query modification operators which can be inserted into said tree; b) receiving a second set of reference query results; c) receiving a first set of current query results from a currently given query comprising one or more triple patterns; d) contrasting the first set of query results with the second set of query results by assessing the differences between the two query results; e) running the search engine which is configured to perform the following steps: f) selecting a node of said tree by a computed score derived from the assessed result; g) selecting any query modification operator of the defined set of query modif

Abstract: The disclosed relates to a system for generating a refined query, whereby the system comprises or is coupled with a search engine for searching through a tree of query modification operations, whereby the root node of said tree is an empty node which represents a given initial query, and comprises at least one processor which is configured to perform the following steps: a) defining a set of query modification operators which can be inserted into said tree; b) receiving a second set of reference query results; c) receiving a first set of current query results from a currently given query comprising one or more triple patterns; d) contrasting the first set of query results with the second set of query results by assessing the differences between the two query results; e) running the search engine which is configured to perform the following steps: f) selecting a node of said tree by a computed score derived from the assessed result; g) selecting any query modification operator of the defined set of query

Abstract: Provided is a recommendation engine to provide automatically recommendations for the completion of an engineering project, the recommendation engine including: a first artificial intelligence, AI, module adapted to provide latent representations of a sequence of selected items; and a second artificial intelligence, AI, module adapted to process the latent representations of the sequence of selected items provided by the first artificial intelligence, AI, module to generate at least one sequence of complementary items required to complement the sequence of selected items to provide a complete sequence of items output via an interface as a recommendation to complete the engineering project.

Abstract: The invention is directed to a computer-implemented method for determining at least one completed item of at least one product solution, comprising the steps of: a. Providing at least one input data set with at least one partial item of the at least one product solution; wherein b. the at least one partial item comprises at least one initial feature; c. Complementing the at least one partial item of the at least one product solution with at least one additional alternative feature using a trained machine learning model on the basis of at least one partial item of the at least one product solution to determine a plurality of alternative complete items of the at least one product solution; and d. Determining at least one evaluated complete item of the plurality of alternative items of the at least one product solution as output data set using a market impact evaluation. Further, the invention relates to a corresponding computer program product and system.

Abstract: Provided is a computer-implemented method and platform for context aware sorting of items available for configuration of a system during a selection session, the method including the steps of providing a numerical input vector, V, representing items selected in a current selection session as context; calculating a compressed vector, Vcomp, from the numerical input vector, V, using an artificial neural network, ANN, adapted to capture non-linear dependencies between items; multiplying the compressed vector, Vcomp, with a weight matrix, EI, derived from a factor matrix, E, obtained as a result of a tensor factorization of a stored relationship tensor, Tr, representing relations, r, between selections of items performed in historical selection sessions, available items and their attributes to compute an output score vector, S; and sorting automatically the available items for selection in the current selection session according to relevance scores of the computed output score vector, S.

Abstract: A plurality of basic simulations independent of one another are carried out, which determine respective remaining service life predictions for the machine. The remaining service life predictions and characteristic data are fed to a neural network, which outputs weights for the remaining service life predictions. A final prediction is calculated from the remaining service life predictions by weighting the remaining service life predictions relative to one another. A hybrid model is produced, which results from the combination of the basic simulations with the neural network. The remaining service life can be predicted not only for a small number of machines for which a specific simulation model has been manually created. The hybrid model enables condition monitoring for any further types and configurations of machines that merely belong to the same machine class. The basic simulations can therefore also be applied to previously unknown machines.

Abstract: Hidden Features are locally extracted from Industrial Data of the industrial system by a Local Application executed on a local computer of a customer. The Hidden Features are uploaded to an external computer of a service provider. A Domain Model for the industrial system is externally determined from an Industrial Model Library (IML) on the external computer based on the uploaded Hidden Features by an External Algorithm including at least one Machine Learning Model (MLM) executed on the external computer. The determined Domain Model for the industrial system is provided to the customer. The at least one MLM has been trained on ranking most appropriate Domain Models for industrial systems based on Hidden Features of the respective industrial systems. The most appropriate Domain Models represent all relevant technical aspects of the respective industrial systems.

Abstract: A plurality of basic simulations independent of one another are carried out, which determine respective remaining service life predictions for the machine. The remaining service life predictions and characteristic data are fed to a neural network, which outputs weights for the remaining service life predictions. A final prediction is calculated from the remaining service life predictions by weighting the remaining service life predictions relative to one another. A hybrid model is produced, which results from the combination of the basic simulations with the neural network. The remaining service life can be predicted not only for a small number of machines for which a specific simulation model has been manually created. The hybrid model enables condition monitoring for any further types and configurations of machines that merely belong to the same machine class. The basic simulations can therefore also be applied to previously unknown machines.

Abstract: System and methods for configuring a technical system based on generated rules and building the technical system. The technical system and the generated rules are given in graph representations including the following steps: defining rules by a user and representing the rules in a graphical interface, converting the rules from the graphical interface into a programming language and/or a natural language, validating the rules for the technical system, checking the compatibility of the rules, serializing the rules for storage in a file system or a database, using the serialized rules to configure the technical system, and building the configured technical system.