C-SPARQL Extension for Sampling RDF Graphs Streams

Dia, Amadou Fall; Kazi-Aoul, Zakia; Boly, Aliou; Chabchoub, Yousra

doi:10.1007/978-3-319-65406-5_2

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…The proposed middleware facilitates interacting between things without further processing. Also, [52] apply ontological concepts and semantic stream processing technologies to facilitate combination, comparison, and visualization of heterogeneous data from various sources using C-SPARQL [53] as the stream processor, and reasoning capabilities through SPARQL extensions [54].…”

Section: Related Research On the Application Of Big Data Analyticsmentioning

confidence: 99%

A Distributed Stream Processing Middleware Framework for Real-Time Analysis of Heterogeneous Data on Big Data Platform: Case of Environmental Monitoring

Akanbi

Masinde

2020

Sensors

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In recent years, the application and wide adoption of Internet of Things (IoT)-based technologies have increased the proliferation of monitoring systems, which has consequently exponentially increased the amounts of heterogeneous data generated. Processing and analysing the massive amount of data produced is cumbersome and gradually moving from classical ‘batch’ processing—extract, transform, load (ETL) technique to real-time processing. For instance, in environmental monitoring and management domain, time-series data and historical dataset are crucial for prediction models. However, the environmental monitoring domain still utilises legacy systems, which complicates the real-time analysis of the essential data, integration with big data platforms and reliance on batch processing. Herein, as a solution, a distributed stream processing middleware framework for real-time analysis of heterogeneous environmental monitoring and management data is presented and tested on a cluster using open source technologies in a big data environment. The system ingests datasets from legacy systems and sensor data from heterogeneous automated weather systems irrespective of the data types to Apache Kafka topics using Kafka Connect APIs for processing by the Kafka streaming processing engine. The stream processing engine executes the predictive numerical models and algorithms represented in event processing (EP) languages for real-time analysis of the data streams. To prove the feasibility of the proposed framework, we implemented the system using a case study scenario of drought prediction and forecasting based on the Effective Drought Index (EDI) model. Firstly, we transform the predictive model into a form that could be executed by the streaming engine for real-time computing. Secondly, the model is applied to the ingested data streams and datasets to predict drought through persistent querying of the infinite streams to detect anomalies. As a conclusion of this study, a performance evaluation of the distributed stream processing middleware infrastructure is calculated to determine the real-time effectiveness of the framework.

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Section: Related Research On the Application Of Big Data Analyticsmentioning

confidence: 99%

A Distributed Stream Processing Middleware Framework for Real-Time Analysis of Heterogeneous Data on Big Data Platform: Case of Environmental Monitoring

Akanbi

Masinde

2020

Sensors

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“…In the implementation, we use for the RDF models and some processing Apache Jena 5 , a POS Tagger of OpenNLP 6 and WordNet of extJWNL 7 . The adapted version of the Cobweb algorithm is implemented by modifying the Cob-Web algorithm of MOA 8 .…”

Section: Algorithmsmentioning

confidence: 99%

“…Le-Phuoc et al [20] focuses on RDF stream processing, but without emphasising on summaries. Dia et al [7] extend SPARQL for supporting RDF stream sampling, which is dierent from diverse entity summarisation.…”

Section: Streamingmentioning

confidence: 99%

Dynamic Diverse Summarisation in Heterogeneous Graph Streams: a Comparison between Thesaurus/Ontology-based and Embeddings-based Approaches

Pavlopoulou

2020

IJGC

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Nowadays, there is a lot of attention drawn in smart environments, like Smart Cities and Internet of Things. These environments generate data streams that could be represented as graphs, which can be analysed in real-time to satisfy user or application needs. The challenges involved in these environments, ranging from the dynamism, heterogeneity, continuity, and high-volume of these real-world graph streams create new requirements for graph processing algorithms. We propose a dynamic graph stream summarisation system with the use of embeddings that provides expressive graphs while ensuring high usability and limited resource usage. In this paper, we examine the performance comparison between our embeddings-based approach and an existing thesaurus/ontology-based approach (FACES) that we adapted in a dynamic environment with the use of windows and data fusion. Both approaches use conceptual clustering and top-k scoring that can result in expressive, dynamic graph summaries with limited resources. Evaluations show that sending top-k fused diverse summaries, results in 34% to 92% reduction of forwarded messages and redundancy-awareness with an F-score ranging from 0.80 to 0.95 depending on the k compared to sending all the available information without top-k scoring. Also, the summaries' quality follows the agreement of ideal summaries determined by human judges. The summarisation approaches come with the expense of reduced system performance. The thesaurus/ontology-based approach proved 6 times more latency-heavy and 3 times more memory-heavy compared to the most expensive embeddings-based approach while having lower throughput but provided slightly better quality summaries.

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