Ontology-Based Integration of Streaming and Static Relational Data with Optique

Kharlamov, Evgeny; Brandt, Sebastian; Jiménez-Ruiz, Ernesto; Kotidis, Yannis; Lamparter, Steffen; Mailis, Theofilos; Neuenstadt, Christian; Özçep, Özgür Lütfü; Pinkel, Christoph; Svingos, Christoforos; Zheleznyakov, Dmitriy; Horrocks, Ian; Ioannidis, Yannis; Moeller, Ralf

doi:10.1145/2882903.2899385

Cited by 39 publications

(24 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such tasks require simultaneous processing of (i) sequences of digitally encoded coherent signals produced and transmitted from thousands of gas and steam turbines, generators, and compressors installed in power plants, and (ii) static data that include the structure of relevant equipment, history of its exploitation and repairs, and even weather conditions. These data are scat- * This paper extends our earlier accepted demo [4] with a more detailed demo scenario and the STREAMVQS system. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionsupporting

confidence: 58%

Enabling semantic access to static and streaming distributed data with optique

Kharlamov

Brandt

Giese

et al. 2016

Proceedings of the 10th ACM International Conference on Distributed and Event-Based Systems

View full text Add to dashboard Cite

Real-time processing of data coming from multiple heterogeneous data streams and static databases is a typical task in many industrial scenarios such as diagnostics of large machines. A complex diagnostic task may require a collection of up to hundreds of queries over such data. Although many of these queries retrieve data of the same kind, such as temperature measurements, they access structurally different data sources. In this work, we show how Semantic Technologies implemented in our system OPTIQUE can simplify such complex diagnostics by providing an abstraction layerontology-that integrates heterogeneous data. In a nutshell, OP-TIQUE allows complex diagnostic tasks to be expressed with just a few high-level semantic queries, which can be easily formulated with our visual query formulation system. OPTIQUE can then automatically enrich these queries, translate them into a large collection of low-level data queries, and finally optimise and efficiently execute the collection in a heavily distributed environment.

show abstract

Section: Introductionsupporting

confidence: 58%

Enabling semantic access to static and streaming distributed data with optique

Kharlamov

Brandt

Giese

et al. 2016

Proceedings of the 10th ACM International Conference on Distributed and Event-Based Systems

View full text Add to dashboard Cite

show abstract

“…Second, the section about STARQL extends previously presented material [9,10] with an in-depth comparison with existing approaches and systems, as well as with more details and explanations of the language. Third, most of the ExaStream techniques and the evaluation over the Siemens data are presented in this paper for the first time and were not reported in previous papers [11,12]. Fourth, the OptiqueVQS section is significantly extended compared to our earlier paper [9] since the system became much more mature over time.…”

Section: Introductionmentioning

confidence: 91%

“…Rstream outputs every triple in the window, Istream only outputs triples inserted into the window, and Dstream outputs only deleted ones. Next to cascaded streams, which can be seen as temporal sub-queries, STARQL offers the possibility of querying historically recorded data or even comparing them to a live stream (see [12,43]). Those different kinds of input streams (possibly using different kinds of window widths and slides) can additionally be synchronized in STARQL by one or more pulse functions, allowing for a regular query output for possibly asynchronous input.…”

Section: Starql Query Languagementioning

confidence: 99%

Semantic access to streaming and static data at Siemens

Kharlamov

Mailis

Mehdi

et al. 2017

Journal of Web Semantics

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…in [13]. Recently, in [14,15], an approach for integration of streaming with static relational data has been proposed. The Graph of Things [16] targets an IoT setting where many sources provide data for integration and querying, and supports spatial and temporal data, but it is not optimised for mobility data.…”

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

View full text Add to dashboard Cite

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...

show abstract

Ontology-Based Integration of Streaming and Static Relational Data with Optique

Cited by 39 publications

References 13 publications

Enabling semantic access to static and streaming distributed data with optique

Enabling semantic access to static and streaming distributed data with optique

Semantic access to streaming and static data at Siemens

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

Contact Info

Product

Resources

About