We present a description and analysis of the data access challenge in the Siemens Energy. We advocate for Ontology Based Data Access (OBDA) as a suitable Semantic Web driven technology to address the challenge. We derive requirements for applying OBDA in Siemens, review existing OBDA systems and discuss their limitations with respect to the Siemens requirements. We then introduce the Optique platform as a suitable OBDA solution for Siemens. Finally, we describe our preliminary installation and evaluation of the platform in Siemens. † The research was supported by the FP7 grant Optique (n. 318338).
We present a description and analysis of the data access challenge in Siemens Energy. We advocate Ontology Based Data Access (OBDA) as a suitable Semantic Web driven technology to address the challenge. We derive requirements for applying OBDA in Siemens, review existing OBDA systems and discuss their limitations with respect to the Siemens requirements. We then introduce the Optique platform as a suitable OBDA solution for Siemens. The platform is based on a number of novel techniques and components including a deployment module, BootOX for ontology and mapping bootstrapping, a query language STARQL that allows for a uniform querying of both streaming and static data, a highly optimised backend, ExaStream, for processing such data, and a query formulation interface, OptiqueVQS, that allows to formulate STARQL queries without prior knowledge of its formal syntax. Finally, we describe our installation and evaluation of the platform in Siemens.
We present a description and analysis of the data access challenge in Siemens Energy. We advocate Ontology Based Data Access (OBDA) as a suitable Semantic Web driven technology to address the challenge. We derive requirements for applying OBDA in Siemens, review existing OBDA systems and discuss their limitations with respect to the Siemens requirements. We then introduce the Optique platform as a suitable OBDA solution for Siemens. The platform is based on a number of novel techniques and components including a deployment module, BootOX for ontology and mapping bootstrapping, a query language STARQL that allows for a uniform querying of both streaming and static data, a highly optimised backend, ExaStream, for processing such data, and a query formulation interface, OptiqueVQS, that allows to formulate STARQL queries without prior knowledge of its formal syntax. Finally, we describe our installation and evaluation of the platform in Siemens.
Automatic processing of data for the purpose of determining operating states and identifying faults has become essential for many modern industrial systems. Typical sources of this data include hundreds of sensors mounted at the industrial machinery measuring qualities such as temperature, vibration, pressure, and many more. However, sensors are complex technical devices, which means that they can fail and their readings may contain noise or imprecise values. Such low quality data makes it hard to solve the original task of assessing system and process status. We present an approach which brings together several well-known techniques from computer science and statistics and enhances monitoring of technical systems by improving results of detection and correction of data quality issues in sensor data. The application domain and the dependencies between its objects are represented as a knowledge-based model, while statistics identifies data anomalies, such as outlying or missing values, in sensor measurement data. Combining information from the knowledge-based model and statistical computations allows to validate and improve data analysis results. We demonstrate the proposed approach on a real-world industrial use case from the power generation domain. Our evaluation shows that the combined solution improves precision indexes while maintaining high accuracy and recall values.
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