=Paper=
{{Paper
|id=Vol-1383/paper26
|storemode=property
|title=Smart Data Access: Semantic Web Technologies for Energy Diagnostics
|pdfUrl=https://ceur-ws.org/Vol-1383/paper26.pdf
|volume=Vol-1383
|dblpUrl=https://dblp.org/rec/conf/semweb/Waltinger14
}}
==Smart Data Access: Semantic Web Technologies for Energy Diagnostics==
https://ceur-ws.org/Vol-1383/paper26.pdf
Smart Data Access:
Semantic Web Technologies for Energy Diagnostics
Dr. Ulli Waltinger
Siemens AG - Corporate Technology - Research & Technology Center
Otto-Hahn-Ring 6 - 81739 Munich, Germany
ulli.waltinger@siemens.com
In today`s (big) data-intensive world, scalable technologies enabling the efficient management,
storage and analysis of large data set are needed. However, the underlying logic of the
emerging data-driven business is very different to the established understanding of the
traditional often technology-driven industries. As large and complex data are generate almost
everywhere in exponentially growth, it is becoming challenging to process and analyze them
efficiently by utilizing traditional data analytic and mining techniques.
Semantic web technologies and data mining techniques for unified information access and
predictive analytics bring together a multidisciplinary skill set that allows and supports the
combination of actual and expected values to plan, predict, and monitor business scenarios and
their impact throughout an organization. These techniques play nowadays a key role for
challenges such as the optimization of complex system behavior, real-time decision support in
operational processes, condition monitoring for predictive maintenance such as failures and
fatigue detection, and to increase the efficiency of remote monitoring operations.
Especially the processing of data in diagnostics and search related purposes as for instance in
alarm management systems become more and more complicated, which can be attributed to
the following constraints: [Volume] The diagnosis process, the search for root causes or the
calculation of key performance indicators relies on handling large amounts of data. Nowadays,
collected data sums up to hundreds of TB for individual use cases (Waltinger et al. 2014).
[Velocity] In addition to the large amounts of data, more and more data is generated every day.
Archived and/or continuous incoming live/streaming data have to be included into the diagnose
process to achieve proper results (Giese et al. 2013). [Variety] Different vendors of machines or
single components, coupled with historical or compatibility reasons, lead to multiple different
logical and physical data representations and forms. Providing a unified and efficient access to
all the different logical models is complex and cumbersome. [Veracity] Finally, the aspect of
data quality - faulty or missing information leads to high expenses for companies for several
reasons. Bad decisions based on wrong information may lead to accidents, resulting in machine
damage or even human harm. Additional costs are generated when internal employees are
unable to find their required knowledge in time or at all. Consequently, expensive external
experts are required (Feldman and Sherman 2001). Varying data representations and the
difficulties with processing unstructured data, require additional support for engineers with
predefined search queries or diagnostic tools. The search queries have to be updated and
adapted to the different logical representations or new unstructured events regularly. Engineers
in the oil and gas industry spend about 30% to 70% of their time searching for data and
assessing the quality of the data (Alcook 2009).
Due to the steady development of new key technologies within the area of semantic web and
standards like SPARQL Protocol and RDF Query Language (SPARQL) or Web Ontology
Language (OWL), new approaches and promising ideas emerge to solve diagnosis and search
problems also in the area of energy diagnostics. As for instance, automating and offering a
general applicable natural language interface (Waltinger et al. 2013) and/or Ontology-based
interpretation (Tran et al. 2007) reduces the error-proneness and simplifies the query
optimization, therefore speeding up the response time. Hence, reducing this amount of time will
lead to great benefits for the engineers and companies itself.
In this talk, we present two different business-driven use cases derived from the domain of
Energy diagnostics that builds heavily upon semantic web technologies. We describe the
motivation and current needs for semantic web technologies to industry data, where eligible
technologies and data storage possibilities are analyzed. Within the first use case, we describe
the benefit of automatic SPARQL query construction (Lehmann et al 2011) for effective natural
language queries by unifying the information derived from the Linked Data Cloud with Corporate
Repositories. In the second use case, we describe the benefit of separating Ontology-based
data modeling and associated large-scale diagnostic sensor data within a real-time processing
setup. We analyze the performance (Schmidt et al., 2010) of using RDBMS, RDF and Triple
Stores for the knowledge representation. The proposed approaches will be evaluated on the
basis of a query catalog by means of query efficiency, accuracy, and data structure
performance. The results show, that natural language access to industry data using ontology’s,
is a simple but effective approach to improve diagnosis and data search for a broad range of
users. Furthermore, virtual RDF graphs do support the DB-driven knowledge graph
representation process (Kumar et al, 2011), but do not perform efficient under industry
conditions in terms of performance and scalability.
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