Query Performance Evaluation of Sensor Data Integration Methods for Knowledge Graphs

Steindl, Gernot; Kästner, Wolfgang

doi:10.1109/bdkcse48644.2019.9010668

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Thus, the knowledge graph acts as a semantic abstraction layer [100] where data can stay in the original local database, e.g., within an SQL database of the SCADA system. Data are only loaded when accessed, which also enhances performance [101]. For more detailed information about the data access, we refer to our previous work in references [22,63].…”

Section: Data Dimensionmentioning

confidence: 99%

Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems

et al. 2022

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Digitalization and concepts such as digital twins (DT) are expected to have huge potential to improve efficiency in industry, in particular, in the energy sector. Although the number and maturity of DT concepts is increasing, there is still no standardized framework available for the implementation of DTs for industrial energy systems (IES). On the one hand, most proposals focus on the conceptual side of components and leave most implementation details unaddressed. Specific implementations, on the other hand, rarely follow recognized reference architectures and standards. Furthermore, most related work on DTs is done in manufacturing, which differs from DTs in energy systems in various aspects, regarding, for example, multiple time-scales, strong nonlinearities and uncertainties. In the present work, we identify the most important requirements for DTs of IES. We propose a DT platform based on the five-dimensional DT modeling concept with a low level of abstraction that is tailored to the identified requirements. We address current technical implementation barriers and provide practical solutions for them. Our work should pave the way to standardized DT platforms and the efficient encapsulation of DT service engineering by domain experts. Thus, DTs could be easy to implement in various IES-related use cases, host any desired models and services, and help get the most out of the individual applications. This ultimately helps bridge the interdisciplinary gap between the latest research on DTs in the domain of computer science and industrial automation and the actual implementation and value creation in the traditional energy sector.

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Section: Data Dimensionmentioning

confidence: 99%

Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems

et al. 2022

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“…Steindl et al further evaluated the query execution times of three different methods to integrate time series data into knowledge graphs [15]. The authors found that both Ontop and custom property functions were superior to storing the time series data inside the knowledge graph.…”

Section: Related Workmentioning

confidence: 99%

“…The different events and time series values can be materialized inside the triple store. Due to the high volume and velocity of measurements however, this approach would result in inefficient query times [15] and data processing in general [5]. Because of these limitations, a dedicated time series database was favored instead.…”

Section: Use Case: Process-based Access To Event Logsmentioning

confidence: 99%

Accessing and Interpreting OPC UA Event Traces based on Semantic Process Descriptions

Tom¹,

Hranisavljevic²,

Fay³

2022

Preprint

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The analysis of event data from production systems is the basis for many applications associated with Industry 4.0. However, heterogeneous and disjoint data is common in this domain. As a consequence, contextual information of an event might be incomplete or improperly interpreted which results in suboptimal analysis results. This paper proposes an approach to access a production systems' event data based on the event data's context (such as the product type, process type or process parameters). The approach extracts filtered event logs from a database system by combining: 1) a semantic model of a production system's hierarchical structure, 2) a formalized process description and 3) an OPC UA information model. As a proof of concept we demonstrate our approach using a sample server based on OPC UA for Machinery Companion Specifications.

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Ontology-Based Model Identification of Industrial Energy Systems

Steindl

Kästner

2020

2020 IEEE 29th International Symposium on Industrial Electronics (ISIE)

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Query Performance Evaluation of Sensor Data Integration Methods for Knowledge Graphs

Cited by 4 publications

References 10 publications

Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems

Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems

Accessing and Interpreting OPC UA Event Traces based on Semantic Process Descriptions

Ontology-Based Model Identification of Industrial Energy Systems

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