Mikhail Roshchin

How Semantic Technologies Can Enhance Data Access at Siemens Energy

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.

Optique: OBDA Solution for Big Data

Accessing the relevant data in Big Data scenarios is increasingly difficult both for end-user and IT-experts, due to the volume, variety, and velocity dimensions of Big Data.This brings a hight cost overhead in data access for large enterprises. For instance, in the oil and gas industry, IT-experts spend 30-70% of their time gathering and assessing the quality of data [1]. The Optique project ( http://www.optique-project.eu/ ) advocates a next generation of the well known Ontology-Based Data Access (OBDA) approach to address the Big Data dimensions and in particular the data access problem. The project aims at solutions that reduce the cost of data access dramatically.

Capturing Industrial Information Models with Ontologies and Constraints

This paper describes the outcomes of an ongoing collaboration between Siemens and the University of Oxford, with the goal of facilitating the design of ontologies and their deployment in applications. Ontologies are often used in industry to capture the conceptual information models underpinning applications. We start by describing the role that such models play in two use cases in the manufacturing and energy production sectors. Then, we discuss the formalisation of information models using ontologies, and the relevant reasoning services. Finally, we present SOMM—a tool that supports engineers with little background on semantic technologies in the creation of ontology-based models and in populating them with data. SOMM implements a fragment of OWL 2 RL extended with a form of integrity constraints for data validation, and it comes with support for schema and data reasoning, as well as for model integration. Our preliminary evaluation demonstrates the adequacy of SOMM’s functionality and performance.

Semantic access to streaming and static data at 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.

Model-based Knowledge Extraction for Automated Monitoring and Control

Abstract Typically, Plant Lifecycle Management Systems (PLMS) provide rich functionality for universal asset management and engineering during a design phase of production systems. Completing actual realization of these production systems and bringing them into operational mode turns out that necessary information from a PLMS, provided already during engineering step, will not be coupled with an appropriate system any more. It remains so as well, even when it is necessary to call back specific engineering background information for some scenario (e.g. for automated monitoring and control). Our approach presented here aims in finding an effective solution for this issue comprising the following: (1) a formal logic-based model for flexible information acquisition from a PLMS, and (2) an automated reasoning mechanism, which can be flexibly adopted for an implementation of various applications of the operational mode (e.g. diagnostic functionality). To evaluate our proposed concepts and techniques, we focus on the implementation example using the Siemens PLMS product COMOS.

A diagnostics framework based on abductive description logic reasoning

This paper presents a flexible approach to automated diagnostics for complex technical systems, built on a firm theoretical and methodological basis. To this end we devise a conceptual logic-based model for diagnostics inspired by ISO standards, and subsequently investigate different mappings of this formal model into the framework of relaxed abduction, a novel non-standard description logic (DL) reasoning task introduced lately. This framework allows for the robust interpretation of potentially incomplete information with respect to imperfect diagnostic models. We investigate the use of both causal and anti-causal models representing the diagnostic knowledge, and evaluate a prototypical application of the approach to steam and gas turbine diagnostics with encouraging results.

Semantic data integration and monitoring in the railway domain

Information integration is a key for further growth of efficiency in management decisions for the railway domain. In the context of the EU project InteGRail (funded in the 6th Framework Programme) an integration approach leveraged by ontologies known from the Semantic Web and logic-based reasoning mechanisms has been successfully demonstrated. To this effect existing heterogeneous monitoring data acquired across the European railways (in the context of rolling stock, infrastructure, operations and traffic management) is logically integrated according to a formal information model. Based on distributed reasoning mechanism decentralized data and inferred knowledge does not have to be aggregated in a central repository but can be transparently accessed by applications independently from where it is acquire. We explain how the proposed techniques facilitate integration, analysis and interpretation of distributed observation data in the railway domain. Finally the implementation of the presented approach is presented by a demonstration scenario, which integrates existing real-world data for symptom identification and fault detection.

Semantic Rule-Based Equipment Diagnostics

Industrial rule-based diagnostic systems are often data-dependant in the sense that they rely on specific characteristics of individual pieces of equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers. In this work we address these problems by relying on Ontology-Based Data Access: we use ontologies to mediate the equipment and the rules. We propose a semantic rule language, sigRL, where sensor signals are first class citizens. Our language offers a balance of expressive power, usability, and efficiency: it captures most of Siemens data-driven diagnostic rules, significantly simplifies authoring of diagnostic tasks, and allows to efficiently rewrite semantic rules from ontologies to data and execute over data. We implemented our approach in a semantic diagnostic system, deployed it in Siemens, and conducted experiments to demonstrate both usability and efficiency.

Semantic Rules for Machine Diagnostics: Execution and Management

Rule-based diagnostics of equipment is an important task in industry. In this paper we present how semantic technologies can enhance diagnostics. In particular, we present our semantic rule language sigRL that is inspired by the real diagnostic languages used in Siemens. SigRL allows to write compact yet powerful diagnostic programs by relying on a high level data independent vocabulary, diagnostic ontologies, and queries over these ontologies. We study computational complexity of SigRL: execution of diagnostic programs, provenance computation, as well as automatic verification of redundancy and inconsistency in diagnostic programs.

SemDia: Semantic Rule-Based Equipment Diagnostics Tool

Rule-based diagnostics of power generating equipment is an important task in industry. In this demo we present how semantic technologies can enhance diagnostics. In particular, we present our semantic rule language sigRL that is inspired by the real diagnostic languages in Siemens. SigRL allows to write compact yet powerful diagnostic programs by relying on a high level data independent vocabulary, diagnostic ontologies, and queries over these ontologies. We present our diagnostic system SemDia. The attendees will be able to write diagnostic programs in SemDia using sigRL over 50 Siemens turbines. We also present how such programs can be automatically verified for redundancy and inconsistency. Moreover, the attendees will see the provenance service that SemDia provides to trace the origin of diagnostic results.