Semantic access to streaming and static data at Siemens

Kharlamov, Evgeny; Mailis, Theofilos; Mehdi, Gulnar; Neuenstadt, Christian; zep, zgr; Roshchin, Mikhail; Solomakhina, Nina; Soylu, Ahmet; Svingos, Christoforos; Brandt, Sebastian; Giese, Martin; Ioannidis, Yannis; Lamparter, Steffen; Mller, Ralf; Kotidis, Yannis; Waaler, Arild

doi:10.1016/j.websem.2017.02.001

Cited by 82 publications

(10 citation statements)

References 53 publications

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“…Thus far, what they have published is to represent an RDF stream as a sequence of time-annotated graphs <g [t]>, where g is an RDF graph and t is a timestamp [29]. This approach has already been used by Siemens and in the European project Optique to annotate streams [30,31], using the query language STARQL. The Optique platform is a complete platform, including a deployment module, BootOX for ontology and mapping bootstrapping, the query language STARQL, a backend, ExaStream, which process the data, and a query interface, OptiqueVQS [32], allowing to write queries for non-experts, without knowledge of the query syntax.…”

Section: Related Workmentioning

confidence: 99%

IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services

Elsaleh

Enshaeifar

Rezvani

et al. 2020

Sensors

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With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally, semantics are heavy to process and not ideal for Internet of Things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT.

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Section: Related Workmentioning

confidence: 99%

IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services

Elsaleh

Enshaeifar

Rezvani

et al. 2020

Sensors

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show abstract

“…The Optique solution, as an end-to-end OBDA framework including OptiqueVQS, has been deployed in Statoil [51,53,56] and Siemens [50,52,57]. The specifics of OBDA setup, such as mappings, query transformation, deployment, and query answering (i.e., the part B of scenario B) are also beyond the scope of this article.…”

Section: Contributionsmentioning

confidence: 99%

“…We chose STARQL since it supports OBDA, but OptiqueVQS could potentially generate queries in any other language. Technical details of STARQL are beyond the scope of this article; interested readers are referred to relevant material [57,66,92].…”

Section: Optiquevqsmentioning

confidence: 99%

OptiqueVQS: A visual query system over ontologies for industry

Soylu

Kharlamov

Zheleznyakov

et al. 2018

Self Cite

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An important application of semantic technologies in industry has been the formalisation of information models using OWL 2 ontologies and the use of RDF for storing and exchanging application data. Moreover, legacy data can be virtualised as RDF using ontologies following the ontology-based data access (OBDA) approach. In all these applications, it is important to provide domain experts with query formulation tools for expressing their information needs in terms of queries over ontologies. In this work, we present such a tool, OptiqueVQS, which is designed based on our experience with OBDA applications in Statoil and Siemens and on best HCI practices for interdisciplinary engineering environments. OptiqueVQS implements a number of unique techniques distinguishing it from analogous query formulation systems. In particular, it exploits ontology projection techniques to enable graph-based navigation over an ontology during query construction. Secondly, while OptiqueVQS is primarily ontology driven, it exploits sampled data to enhance selection of data values for some data attributes. Finally, OptiqueVQS is built on well-grounded requirements, design rationale, and quality attributes. We evaluated OptiqueVQS with both domain experts and casual users and qualitatively compared our system against prominent visual systems for ontology-driven query formulation and exploration of semantic data. OptiqueVQS is available online and can be downloaded together with an example OBDA scenario.

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“…Manufacturing processes involve a variety of physical resources and operational processes, so the first step in establishing M 2 ES Apps System is defining all the related resources and processes uniquely. The concept of industrial dataspace is introduced to meet requirements imposed by M 2 ES Apps System, which refers to the semantic representation [40] of multi-source and heterogeneous data. Each resource, whether it is a physical one like a machine or a virtual one like an order, will be registered as an entity in industrial dataspace.…”

Section: Es Apps System Architecturementioning

confidence: 99%

Mini-MES: A Microservices-Based Apps System for Data Interconnecting and Production Controlling in Decentralized Manufacturing

Jiang

Liu

2019

Applied Sciences

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Manufacturing factories and their resources are moving towards being networked, flat, flexible, and diversified in the context of social manufacturing. It is urgent to manage the increasingly complex and variable manufacturing processes effectively in such cross-factory production. This paper proposes a brand-new microservices-based Mini-MES (M2ES) Apps System, which adopts the decentralized and distributed architecture to comply with the needs of software supported manufacturing. The basic properties of the M2ES Apps System, such as its running logic and its data organization, are discussed, and a real-world industrial case is described to illustrate how it works. Results show that the proposed M2ES Apps System has better performance, reliability, and scalability than the initial information systems.

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Semantic access to streaming and static data at Siemens

Cited by 82 publications

References 53 publications

IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services

IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services

OptiqueVQS: A visual query system over ontologies for industry

Mini-MES: A Microservices-Based Apps System for Data Interconnecting and Production Controlling in Decentralized Manufacturing

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