A Complete Software Stack for IoT Time-Series Analysis that Combines Semantics and Machine Learning—Lessons Learned from the Dyversify Project

Paepe, Dieter De; Hautte, Sander Vanden; Steenwinckel, Bram; Moens, Pieter; Vaneessen, Jasper; Vandekerckhove, Steven; Volckaert, Bruno; Ongenae, Femke; Hoecke, Sofie Van

doi:10.3390/app112411932

Cited by 5 publications

(2 citation statements)

References 53 publications

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“…The Lambda architecture provides a means for dealing with vast amounts of data that uses a hybrid strategy to enable interaction with stream processing and batch processing approaches [30,31]. The lambda structure is made up of three layers: (1) Data processing in batches for precompiling vast sets of information, (2) real-time or speed computing to reduce latencies through performing real-time analyses as data comes, and (3) the serving layer that allows to reply to inquiries, connecting with inquiries, and providing outcomes of the computations.…”

Section: Proof-of-concept Of Lambda Architecturementioning

confidence: 99%

Digital banking fortification: a real-time isolation forest architecture for detecting online transaction fraud

Abbassi,

E L Mendili,

Gahi

2024

Eng. Res. Express

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Since the use of the Internet has increased exponentially, numerous organizations, including the financial industry, offer services online. As a result, financial scams are expanding in quantity and complexity worldwide, resulting in massive revenue losses and making digital fraudulent transactions a severe issue. Abnormal attempts and illegal access are instances of these dangers that fraudulent activity detection systems must identify. Machine learning and data mining approaches have been extensively used to address this issue in recent years.However, these approaches must be enhanced regarding real-time detection speed, tackling enormous amounts of data, and finding undiscovered attack patterns. Consequently, the present study provides a real-time architecture for averting and identifying digital transaction fraud, which relies on the Isolation Forest (IForest) approach and big data analytic tools, including Spark Streaming, sparkling water, Kafka, and PostgreSQL. This architecture seeks to improve present detection strategies by increasing accuracy for detection when considering enormous amounts of data. Two real datasets of online transactional fraud are used to assess the proposed architecture, and the findings are compared to relevant studies. The investigation results showed that IForest performed flawlessly, achieving an accuracy of 0.99 in two datasets.

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Section: Proof-of-concept Of Lambda Architecturementioning

confidence: 99%

Digital banking fortification: a real-time isolation forest architecture for detecting online transaction fraud

Abbassi,

E L Mendili,

Gahi

2024

Eng. Res. Express

View full text Add to dashboard Cite

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“…RMLStreamer-SISO has seen uptake in multiple projects -covering different use cases in different architectures -to process streaming data and generate RDF streams. Largest validation was in research and development (R&D) projects between imec and Flemish companies such as DyVerSIFy on streaming data analysis and visualisation [24,20], together with Televic Rail on IoT data, DAIQUIRI 15 together with VRT on sport sensor data, and ESSENCE and H2020 project MOS2S 16 on media data. Other projects include DiSSeCt 17 on health data and transport data [4].…”

Section: Use Casesmentioning

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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The RML Ontology: A Community-Driven Modular Redesign After a Decade of Experience in Mapping Heterogeneous Data to RDF

Iglesias-Molina,

Van Assche,

Arenas-Guerrero

et al. 2023

Lecture Notes in Computer Science

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The Relational to RDF Mapping Language (R2RML) became a W3C Recommendation a decade ago. Despite its wide adoption, its potential applicability beyond relational databases was swiftly explored. As a result, several extensions and new mapping languages were proposed to tackle the limitations that surfaced as R2RML was applied in real-world use cases. Over the years, one of these languages, the RDF Mapping Language (RML), has gathered a large community of contributors, users, and compliant tools. So far, there has been no well-defined set of features for the mapping language, nor was there a consensus-marking ontology. Consequently, it has become challenging for non-experts to fully comprehend and utilize the full range of the language’s capabilities. After three years of work, the W3C Community Group on Knowledge Graph Construction proposes a new specification for RML. This paper presents the new modular RML ontology and the accompanying SHACL shapes that complement the specification. We discuss the motivations and challenges that emerged when extending R2RML, the methodology we followed to design the new ontology while ensuring its backward compatibility with R2RML, and the novel features which increase its expressiveness. The new ontology consolidates the potential of RML, empowers practitioners to define mapping rules for constructing RDF graphs that were previously unattainable, and allows developers to implement systems in adherence with [R2]RML.Resource type: Ontology/License: CC BY 4.0 InternationalDOI: 10.5281/zenodo.7918478/URL: http://w3id.org/rml/portal/

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A Complete Software Stack for IoT Time-Series Analysis that Combines Semantics and Machine Learning—Lessons Learned from the Dyversify Project

Cited by 5 publications

References 53 publications

Digital banking fortification: a real-time isolation forest architecture for detecting online transaction fraud

Digital banking fortification: a real-time isolation forest architecture for detecting online transaction fraud

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

The RML Ontology: A Community-Driven Modular Redesign After a Decade of Experience in Mapping Heterogeneous Data to RDF

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