How Semantic Technologies Can Enhance Data Access at Siemens Energy

Abstract

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).

Querying industrial stream-temporal data: An ontology-based visual approach1

“…The study also shows that the learnability of OptiqueVQS is high as participants did not receive any substantial training. Earlier, three other user experiments were conducted: one with casual users [54] and two others with domain experts at Statoil and Siemens on nonstreaming scenarios [53,25]. These studies revealed similar results and confirmed that OptiqueVQS could address various user types and different scenarios with high efficiency, effectiveness and user satisfaction.…”

“…Therefore, in order to prevent high downtime costs, Siemens runs several service centres, each responsible for remote monitoring and diagnostics of such many thousands of appliances. Data is stored in several thousand databases with varied schemas and the size of the data is in the order of hundreds of terabytes, e.g., there is about 15 GB of data associated to a single turbine, and it currently grows with the average rate of 30 GB per day [25]. Service centres have two main categories of tasks:…”

“…Figure 2 presents the service process triggered after a malfunction of a unit (i.e., reactive scenario) [25]. In this case, a service ticket requesting assistance is created either manually or automatically by a diagnostic system.…”

“…Thus, unforeseen situations may lead to significant delays of up to several hours or even days due to miscommunication, high workload of IT personnel, complexity of query formulation, and long query execution times. In average, up to 35 queries require modification every month, up to 10% of queries are changed throughout a year, and several new queries are developed monthly [25].…”

“…E-mail: ahmet.soylu@ntnu.no open up new possibilities for industries such as oil and gas, power, mining, and agriculture [45,42,25,41]. For example, operators can recognise hazardous conditions by actively monitoring stream sensor data coming from plant equipment such as pumps, motors, and turbines, or analyse historical sensor data in the event of a problem for a proper diagnosis.…”