RDF stream processing with CQELS framework for real-time analysis

Phuoc, Danh Le; Dao-Tran, Minh; Tuan, Anh; Duc, Manh Nguyen; Hauswirth, Manfred

doi:10.1145/2675743.2772586

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…This limit can be notable in streaming sensor data, posing a challenge of timely data processing. In this case, direct access to various ESN data sources for VKG-based virtualization and the incorporation of RDF stream processing [81] languages (e.g., C-SPARQL [82], CQELS [83]) into the VKG-based federation integration approach would be an appropriate research direction for future studies in this area.…”

Section: Limitations In Streaming Sensor Data Handlingmentioning

confidence: 99%

On the Use of Virtual Knowledge Graphs to Improve Environmental Sensor Data Accessibility

Wu,

Orlandi,

O'Sullivan

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The rapid proliferation of Environmental Sensor Networks (ESNs) used for monitoring environmental systems such as meteorology and air quality, as well as advances in database technologies (e.g., SQL) has made significant progress in sensor data management. Notwithstanding the strength of these databases, they can inevitably lead to a data heterogeneity problem as a result of isolated databases with distinct data schema, which are expensive to be accessed and pre-processed when the data is consumed spanning multiple databases. Recently, knowledge graphs have been used as one of the most popular integration frameworks to address this data heterogeneity problem from the perspective of establishing an interoperable semantic schema (a.k.a. ontology). However, the majority of proposed knowledge graphs in this domain are a product of an ETL (Extraction-Transform-Load) approach with all the data physically stored in a triplestore. In contrast, this paper examines an approach of virtualizing knowledge graphs on top of the SQL databases as the means to provide a federated data integration approach for enhanced heterogeneous Environmental Sensor Networks' data access, bringing with it the promise of more cost-efficiency in terms of I/O, storage, etc. In addition, this work also considers some motivating application scenarios regarding the efficiency of time series data access. Based on a performance comparison between the proposed integration approach and some popular triplestores, the proposed approach has a significant edge over triplestores in multiple time series structuring and acquisition.

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Section: Limitations In Streaming Sensor Data Handlingmentioning

confidence: 99%

On the Use of Virtual Knowledge Graphs to Improve Environmental Sensor Data Accessibility

Wu,

Orlandi,

O'Sullivan

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…(1) extensions of the SPARQL web query language, e.g., C-SPARQL [7], Morph-streams [14], and CQELS [36]; (2) extensions of ontology languages to streams e.g., by…”

Section: Stream Processing and Stream Reasoningmentioning

confidence: 99%

Towards a Semantically Enriched Local Dynamic Map

et al. 2018

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With the increasing availability of Cooperative Intelligent Transport Systems, the Local Dynamic Map (LDM) is becoming a key technology for integrating static, temporary, and dynamic information in a geographical context. However, existing ideas do not leverage the full potential of the LDM approach, as an LDM contains streaming data and varying implicit information which are not captured by current models. We aim to provide a semantically enriched LDM that applies Semantic Web technologies, in particular ontologies, in combination with spatial stream databases. This allows us to define an enhanced world model, to derive model properties, to infer new information, and to offer expressive query capabilities over streams. We introduce our envisioned architecture which includes an LDM ontology, an integration and annotation framework, and a stream query answering component. We also sketch three application scenarios that illustrate the usability and benefits of our approach, thus we provide an in-depth validation of the scenarios in an experimental prototype.

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“…On the one hand, different frameworks were proposed to handle data streams, e.g., Flink, Spark or Storm [6,26,19]. On the other hand, RDF stream processing (RSP) engines, e.g., CQELS and C-SPARQL [16,1,5], were widely studied and perform high-throughput analysis of RDF streams with low memory footprints [16]. Yet, these stream processing frameworks are not substantially used in the domain of RDF graph generation from streaming data sources, despite the demand of these mature RSP engines for more RDF streams.…”

Section: Introductionmentioning

confidence: 99%

RMLStreamer-SISO: An RDF Stream Generator from Streaming Heterogeneous Data

Min

Haesendonck

Meester

et al. 2022

The Semantic Web – ISWC 2022

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Stream-reasoning query languages such as CQELS and C-SPARQL enable query answering over RDF streams. Unfortunately, there currently is a lack of efficient RDF stream generators to feed RDF stream reasoners. State-of-the-art RDF stream generators are limited with regard to the velocity and volume of streaming data they can handle. To efficiently generate RDF streams in a scalable way, we extended the RMLStreamer to also generate RDF streams from dynamic heterogeneous data streams. This paper introduces a scalable solution that relies on a dynamic window approach to generate RDF streams with low latency and high throughput from multiple heterogeneous data streams. Our evaluation shows that our solution outperforms the state-of-theart by achieving millisecond latency (compared to seconds that state-ofthe-art solutions need), constant memory usage for all workloads, and sustainable throughput of around 70,000 records/s (compared to 10,000 records/s that state-of-the-art solutions take). This opens up the access to numerous data streams for integration with the semantic web.

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RDF stream processing with CQELS framework for real-time analysis

Cited by 9 publications

References 17 publications

On the Use of Virtual Knowledge Graphs to Improve Environmental Sensor Data Accessibility

On the Use of Virtual Knowledge Graphs to Improve Environmental Sensor Data Accessibility

Towards a Semantically Enriched Local Dynamic Map

RMLStreamer-SISO: An RDF Stream Generator from Streaming Heterogeneous Data

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