Semantic Framework for Industrial Analytics and Diagnostics
Authors: Gulnar Mehdi, Sebastian Brandt, Mikhail Roshchin, Thomas Runkler
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We aim to demonstrate our prototype for semantic analytics for different diagnostic tasks and describe our experiences in the context of a class of semantic applications for Siemens use-case. Our initial use-case comes from the domain of remote monitoring of Siemens gas and steam turbines where users today have difficulty to analyze their performance because different machines have different sensors to contribute, different configurations, sensor tags, thresholds values and compositional structure. With our solution as depicted in Figure. 1, we overcome these problems by i) quering in a domain-specific language against an abstract domain model rather than the actual heterogeneous data sources. For example, as a domain expert would formulate, we are able to return sensor observation values including the time of observation between 2015-08-14 and 2015-08-20 being produced by sensors that observes performance Speed & Power and that are of interest for parts of type Gas Turbine and where the observed values are within the sensors measurement range . |
| Researcher Affiliation | Collaboration | 1Siemens Corporate Technology, Munich, Germany {firstname.lastname}@siemens.com 2Technical University of Munich, Germany |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that the code is open-source or available. |
| Open Datasets | No | The paper refers to an 'initial use-case' from 'remote monitoring of Siemens gas and steam turbines' but does not provide any concrete access information (link, DOI, repository, or formal citation with authors/year) for this or any other dataset used in the experiments. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like KNIME and an OBDA system, but does not provide specific version numbers for any of these components, which is necessary for a reproducible description of software dependencies. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings. |