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Your search keyword '"Ahmet Soylu"' showing total 4 results
Start Over Author "Ahmet Soylu" Publisher institute of electrical and electronics engineers (ieee)
4 results on '"Ahmet Soylu"'

1. Big Data Pipelines on the Computing Continuum: Tapping the Dark Data

Author

Dumitru Roman, Radu Prodan, Nikolay Nikolov, Ahmet Soylu, Mihhail Matskin, Andrea Marrella, Dragi Kimovski, Brian Elvesaeter, Anthony Simonet-Boulogne, Giannis Ledakis, Hui Song, Francesco Leotta, and Evgeny Kharlamov

Subjects
Pipelines, Big Data, General Computer Science, Database systems, Data integrity, Information integrity
Abstract

Big Data pipelines are essential for leveraging Dark Data, i.e., data collected but not used and turned into value. However, tapping their potential requires going beyond existing approaches and frameworks for Big Data processing. The Computing Continuum enables new opportunities for managing Big Data pipelines concerning efficient management of heterogeneous and untrustworthy resources. This article discusses the Big Data pipelines lifecycle on the Computing Continuum, its associated challenges and outlines a future research agenda in this area.

Published
2022

2. SIM-PIPE DryRunner: An approach for testing container-based big data pipelines and generating simulation data

Author

Aleena Thomas, Nikolay Nikolov, Antoine Pultier, Dumitru Roman, Brian Elvesaeter, and Ahmet Soylu

Subjects
Sandbox, Big data pipelines, Dry run, Testing, Software containers, Sandboxes, Simulation
Abstract

Big data pipelines are becoming increasingly vital in a wide range of data intensive application domains such as digital healthcare, telecommunication, and manufacturing for efficiently processing data. Data pipelines in such domains are complex and dynamic and involve a number of data processing steps that are deployed on heterogeneous computing resources under the realm of the Edge-Cloud paradigm. The processes of testing and simulating big data pipelines on heterogeneous resources need to be able to accurately represent this complexity. However, since big data processing is heavily resource-intensive, it makes testing and simulation based on historical execution data impractical. In this paper, we introduce the SIM - PIPE Dry Runner approach - a dry run approach that deploys a big data pipeline step by step in an isolated environment and executes it with sample data; this approach could be used for testing big data pipelines and realising practical simulations using existing simulators.

Published
2022

3. TRANSQL : A Transformer-based Model for Classifying SQL Queries

Author

Tahmasebi, Shirin, Payberah, Amir H., Paragraph, Ahmet Soylu, Roman, Dumitru, Matskin, Mihhail, Tahmasebi, Shirin, Payberah, Amir H., Paragraph, Ahmet Soylu, Roman, Dumitru, and Matskin, Mihhail

Abstract

Domain-Specific Languages (DSL) are becoming popular in various fields as they enable domain experts to focus on domain-specific concepts rather than software-specific ones. Many domain experts usually reuse their previously-written scripts for writing new ones; however, to make this process straightforward, there is a need for techniques that can enable domain experts to find existing relevant scripts easily. One fundamental component of such a technique is a model for identifying similar DSL scripts. Nevertheless, the inherent nature of DSLs and lack of data makes building such a model challenging. Hence, in this work, we propose TRANSQL, a transformer-based model for classifying DSL scripts based on their similarities, considering their few-shot context. We build TRANSQL using BERT and GPT-3, two performant language models. Our experiments focus on SQL as one of the most commonly-used DSLs. The experiment results reveal that the BERT-based TRANSQL cannot perform well for DSLs since they need extensive data for the fine-tuning phase. However, the GPT-based TRANSQL gives markedly better and more promising results., QC 20230613

Published
2022
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4. Ontology-Based Fault Tree Analysis Algorithms in a Fuzzy Environment for Autonomous Ships

Author

Anis Yazidi, Dumitru Roman, Ahmet Soylu, and Bekir Sahin

Subjects
Fuzzy sets, Model developments, General Computer Science, Computer science, Reliability (computer networking), Rules, autonomous ships, 020101 civil engineering, 02 engineering and technology, Ontology (information science), Fuzzy logic, 0201 civil engineering, Safety engineering, model development, 0202 electrical engineering, electronic engineering, information engineering, Ontologies, General Materials Science, Fault tree analysis, Fault tree analyses, Ontology, rules, General Engineering, Model development, Collision, Autonomous ships, fault tree analysis, fuzzy sets, Domain knowledge, Embedding, 020201 artificial intelligence & image processing, lcsh:Electrical engineering. Electronics. Nuclear engineering, Algorithm, lcsh:TK1-9971
Abstract

This study deals with fault tree analysis algorithms based on an ontology-based approach in a fuzzy environment. We extend fuzzy fault tree analysis by embedding ontology-based fault tree structures. The ontology-based approach allows conducting advanced analyses grounded on rich domain knowledge. Two approaches as the conventional approach and rule-based method are applied for the calculations of failure probability and analysis of the fault tree. Collision and grounding of an autonomous ship are investigated in two scenarios. The structural design, probability calculations, and sensitivity analyses prove that the proposed system is applicable, robust, accurate and reliable. Thus, the propounded ontology-based fault tree algorithms fill a gap in safety engineering by its capability of comprehensiveness.

Published
2021

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