Semantic Access to Streaming and Static Data at Siemens

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

doi:10.2139/ssrn.3199299

Cited by 18 publications

(24 citation statements)

References 57 publications

(33 reference statements)

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“…There are efforts on semantic integration of streaming with archival data designed to operate on RDF, such as [11], [12], or efforts towards a framework for the integration of distributed heterogeneous streaming and stored data sources through ontological models, e.g. in [13].…”

Section: Related Workmentioning

confidence: 99%

SPARTAN: Semantic integration of big spatio-temporal data from streaming and archival sources

Santipantakis

Glenis

Patroumpas

et al. 2020

Future Generation Computer Systems

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An ever-increasing number of applications in critical domains, such as maritime and aviation, generate, collect, manage and process spatio-temporal data related to the mobility of entities. This wealth of data can be exploited for various purposes, towards improving the safety of operations, reducing economical costs, and increasing dependability: The major issue to achieve these objectives is increasing predictability of moving objects' trajectories and events. To achieve this purpose in a data-driven way we need to exploit in integrated manners data from a variety of disparate and heterogeneous data sources, both streaming and archival, regarding -among other -surveillance, weather, and contextual data.Motivated by this fact, in this paper, we propose a framework for semantic integration of big mobility data with other data sources that are necessary to data analytics tasks, providing a unified representation of such data. Notable features of our framework include the real-time generation of data synopses of moving entities' trajectories, the efficient and flexible transformation of data from heterogeneous and big data sources in RDF, and the spatio-temporal link discovery between spatio-temporal entities in diverse data sources. The design and implementation of our framework uses big data technologies (Apache Flink and Kafka), and our experimental evaluation demonstrates the efficiency and scalability of the proposed framework using large, real-life datasets. The ever-increasing size of spatio-temporal data and the unprecedented rate of data generation from a wide variety of sources regarding the situation awareness and monitoring in critical domains raise the need for scalable, real-time management and analysis of mobility data. Several data analysis tasks rely on moving entities' trajectories, while trajectory detection and prediction are typically used to optimize everyday, real-life operations. However, using only the kinematic information provided by surveillance sources is far from sufficient, when at the same time a wealth of other sources, including for instance, weather and contextual 1 information is available too. Consequently, one of the major challenges is to enrich surveillance data, providing meaningful information about moving entities' trajectories, also annotating trajectories with related events, thereby creating enriched trajectories [1, 2]. Addressing this challenge calls for real-time processing and semantic integration of surveillance data with other, streaming and archival, data sources [3].Our work is motivated by the need to advance the management and integrated exploitation of voluminous and heterogeneous data-at-rest (archival data) and data-in-motion (streaming data) sources, so as to significantly promote safety and effectiveness of critical operations for large numbers of moving entities in large geographical areas. Challenges throughout the Big Data ecosystem, with special focus on surveillance systems, concern effective detection and 1 By contextual data, we refer to data other...

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Section: Related Workmentioning

confidence: 99%

SPARTAN: Semantic integration of big spatio-temporal data from streaming and archival sources

Santipantakis

Glenis

Patroumpas

et al. 2020

Future Generation Computer Systems

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“…A few mature OBDA systems have also recently emerged: pioneering MASTRO [22], commercial Stardog [71] and Ultrawrap [81], and the Optique platform [33] based on the query answering engine Ontop [61,77]. By providing a semantic end-to-end connection between users and multiple distributed data sources (and thus making the IT expert middleman redundant), OBDA has attracted the attention of industry, with companies such as Siemens [53] and Statoil [52] experimenting with OBDA technologies to streamline the process of data access for their engineers. 3 …”

Section: Projectmanager(x) ∨ ∃Z Managesproject(x Z)mentioning

confidence: 99%

Ontology-Mediated Queries

Bienvenu

Kikot

Kontchakov

et al. 2018

J. ACM

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We give solutions to two fundamental computational problems in ontology-based data access with the W3C standard ontology language OWL 2 QL: the succinctness problem for first-order rewritings of ontology-mediated queries (OMQs), and the complexity problem for OMQ answering. We classify OMQs according to the shape of their conjunctive queries (treewidth, the number of leaves) and the existential depth of their ontologies. For each of these classes, we determine the combined complexity of OMQ answering, and whether all OMQs in the class have polynomial-size first-order, positive existential and nonrecursive datalog rewritings. We obtain the succinctness results using hypergraph programs, a new computational model for Boolean functions, which makes it possible to connect the size of OMQ rewritings and circuit complexity.

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“…Existing evaluation metrics used in related fields such as snippet generation for ontologies [28] and documents [16] are mainly based on a human-created ground truth. However, an RDF dataset may contain millions of RDF triples, e.g., when it wrapped from a large database [23,18,19,33], or streaming data [25,24], or comes from a manufacturing environment [37,22,26] being much larger than an ontology schema or a document. It would be difficult, if not impossible, to manually identify the optimum snippet as the ground truth.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Evaluating Snippet Generation for Dataset Search

Wang

Chen

et al. 2019

Lecture Notes in Computer Science

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Reusing existing datasets is of considerable significance to researchers and developers. Dataset search engines help a user find relevant datasets for reuse. They can present a snippet for each retrieved dataset to explain its relevance to the user's data needs. This emerging problem of snippet generation for dataset search has not received much research attention. To provide a basis for future research, we introduce a framework for quantitatively evaluating the quality of a dataset snippet. The proposed metrics assess the extent to which a snippet matches the query intent and covers the main content of the dataset. To establish a baseline, we adapt four state-of-the-art methods from related fields to our problem, and perform an empirical evaluation based on real-world datasets and queries. We also conduct a user study to verify our findings. The results demonstrate the effectiveness of our evaluation framework, and suggest directions for future research.

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Semantic Access to Streaming and Static Data at Siemens

Cited by 18 publications

References 57 publications

SPARTAN: Semantic integration of big spatio-temporal data from streaming and archival sources

SPARTAN: Semantic integration of big spatio-temporal data from streaming and archival sources

Ontology-Mediated Queries

A Framework for Evaluating Snippet Generation for Dataset Search

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