Ahmet Soylu

• Khan, Akif Quddus; Matskin, Mihhail; Prodan, Radu; Bussler, Christoph; Roman, Dumitru & Soylu, Ahmet (2024). Cloud storage cost: a taxonomy and survey. World wide web (Bussum). ISSN 1386-145X. 27(4). doi: 10.1007/s11280-024-01273-4.
• Khan, Akif Quddus; Matskin, Mihhail; Prodan, Radu; Bussler, Christoph; Roman, Dumitru & Soylu, Ahmet (2024). Cloud storage tier optimization through storage object classification. Computing. ISSN 0010-485X. doi: 10.1007/s00607-024-01281-2.
• Khan, Akif Quddus; Nikolov, Nikolay Vladimirov; Matskin, Mihhail; Prodan, Radu; Bussler, Christoph & Roman, Dumitru [Show all 7 contributors for this article] (2024). A Taxonomy for Cloud Storage Cost. Communications in Computer and Information Science (CCIS). ISSN 1865-0929. p. 317–330. doi: 10.1007/978-3-031-51643-6_23. Full text in Research Archive
• Tahmasebi, Shirin; Payberah, Amir H.; Soylu, Ahmet; Roman, Dumitru & Matskin, Mihhail (2023). TRANSQLATION: TRANsformer-based SQL RecommendATION. In He, Jingrui; Palpanas, Themis; Hu, Xiaohua; Cuzzocrea, Alfredo; Dou, Dejing; Slezak, Dominik; Wang, Wei; Gruca, Aleksandra; Lin, Jerry Chun-Wei & Agrawal, Rakesh (Ed.), Proceedings 2023 IEEE International Conference on Big Data Dec 15 - Dec 18, 2023 • Sorrento, Italy. IEEE (Institute of Electrical and Electronics Engineers). ISSN 979-8-3503-2445-7. p. 4703–4711. doi: 10.1109/BigData59044.2023.10386277. Full text in Research Archive
• Zheng, Zhuoxun; Zhou, Baifan; Tan, Zhipeng; Savkovic, Ognjen; Rincon-Yanez, Diego & Nikolov, Nikolay Vladimirov [Show all 9 contributors for this article] (2023). Semantic Cloud System for Scaling Data Science Solutions for Welding at Bosch . CEUR Workshop Proceedings. ISSN 1613-0073. 3632. Full text in Research Archive
• Zheng, Zhuoxun; Savkovic, Ognjen; Nikolov, Nikolay; Phuc Luu, Huu; Soylu, Ahmet & Kharlamov, Evgeny [Show all 7 contributors for this article] (2023). Datalog with External Machine Learning Functions for Automated Cloud Resource Configuration . CEUR Workshop Proceedings. ISSN 1613-0073. 3632. Full text in Research Archive
• Tan, Zhipeng; Zheng, Zhuoxun; Klironomos, Antonis; Gad-Elrab, Mohamed H.; Xiao, Guohui & Soylu, Ahmet [Show all 8 contributors for this article] (2023). Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring . CEUR Workshop Proceedings. ISSN 1613-0073. 3632. Full text in Research Archive
• Tan, Zhipeng; Zhou, Baifan; Zheng, Zhuoxun; Savkovic, Ognjen; Huang, Ziqiang & Gonzalez, Irlan-Grangel [Show all 8 contributors for this article] (2023). Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14266, p. 453–471. doi: 10.1007/978-3-031-47243-5_25. Full text in Research Archive
• Zhou, Baifan; Nikolov, Nikolay Vladimirov; Zheng, Zhuoxun; Luo, Xianghui; Savkovic, Ognjen & Roman, Dumitru [Show all 8 contributors for this article] (2023). Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14266, p. 380–399. doi: 10.1007/978-3-031-47243-5_21. Full text in Research Archive Show summary

  Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges in volume and variety. In that context, distributed computing solutions such as cloud systems are leveraged to parallelise the data processing and reduce computation time. As the cloud systems become increasingly popular, there is increased demand that more users that were originally not cloud experts (such as data scientists, domain experts) deploy their solutions on the cloud systems. However, it is non-trivial to address both the high demand for cloud system users and the excessive time required to train them. To this end, we propose SemCloud, a semantics-enhanced cloud system, that couples cloud system with semantic technologies and machine learning. SemCloud relies on domain ontologies and mappings for data integration, and parallelises the semantic data integration and data analysis on distributed computing nodes. Furthermore, SemCloud adopts adaptive Datalog rules and machine learning for automated resource configuration, allowing non-cloud experts to use the cloud system. The system has been evaluated in industrial use case with millions of data, thousands of repeated runs, and domain users, showing promising results.

• Khan, Akif Quddus; Nikolov, Nikolay Vladimirov; Matskin, Mihhail; Prodan, Radu; Bussler, Christoph & Roman, Dumitru [Show all 7 contributors for this article] (2023). Towards Cloud Storage Tier Optimization with Rule-Based Classification. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14183, p. 205–216. doi: 10.1007/978-3-031-46235-1_13. Full text in Research Archive Show summary

  Cloud storage adoption has increased over the years as more and more data has been produced with particularly high demand for fast processing and low latency. To meet the users’ demands and to provide a cost-effective solution, cloud service providers (CSPs) have offered tiered storage; however, keeping the data in one tier is not a cost-effective approach. Hence, several two-tiered approaches have been developed to classify storage objects into the most suitable tier. In this respect, this paper explores a rule-based classification approach to optimize cloud storage cost by migrating data between different storage tiers. Instead of two, four distinct storage tiers are considered, including premium, hot, cold, and archive. The viability and potential of the approach are demonstrated by comparing cost savings achieved when data was moved between tiers versus when it remained static. The results indicate that the proposed approach has the potential to significantly reduce cloud storage cost, thereby providing valuable insights for organizations seeking to optimize their cloud storage strategies. Finally, the limitations of the proposed approach are discussed along with the potential directions for future work, particularly the use of game theory to incorporate a feedback loop to extend and improve the proposed approach accordingly.

• Benvenuti, Dario; Marrella, Andrea; Rossi, Jacopo; Nikolov, Nikolay Vladimirov; Roman, Dumitru & Soylu, Ahmet [Show all 7 contributors for this article] (2023). A Reference Data Model to Specify Event Logs for Big Data Pipeline Discovery. Lecture Notes in Business Information Processing. ISSN 1865-1348. 490, p. 38–54. doi: 10.1007/978-3-031-41623-1_3. Full text in Research Archive
• Nikolov, Nikolay Vladimirov; Solberg, Arnor; Prodan, Radu; Soylu, Ahmet; Matskin, Mihhail & Roman, Dumitru (2023). Container-Based Data Pipelines on the Computing Continuum for Remote Patient Monitoring. Computer. ISSN 0018-9162. 56(10), p. 40–48. doi: 10.1109/MC.2023.3285414. Full text in Research Archive
• Avram, Calin; Gligor, Adrian; Roman, Dumitru; Soylu, Ahmet; Nyulas, Victoria & Avram, Laura (2023). Machine learning based assessment of preclinical health questionnaires. International Journal of Medical Informatics. ISSN 1386-5056. 180. doi: 10.1016/j.ijmedinf.2023.105248. Full text in Research Archive Show summary

  Background: Within modern health systems, the possibility of accessing a large amount and a variety of data related to patients’ health has increased significantly over the years. The source of this data could be mobile and wearable electronic systems used in everyday life, and specialized medical devices. In this study we aim to investigate the use of modern Machine Learning (ML) techniques for preclinical health assessment based on data collected from questionnaires filled out by patients. Method: To identify the health conditions of pregnant women, we developed a questionnaire that was distributed in three maternity hospitals in the Mureș County, Romania. In this work we proposed and developed an ML model for pattern detection in common risk assessment based on data extracted from questionnaires. Results: Out of the 1278 women who answered the questionnaire, 381 smoked before pregnancy and only 216 quit smoking during the period in which they became pregnant. The performance of the model indicates the feasibility of the solution, with an accuracy of 98 % confirmed for the considered case study. Conclusion: The proposed solution offers a simple and efficient way to digitize questionnaire data and to analyze the data through a reduced computational effort, both in terms of memory and computing power used.

• Layegh, Amirhossein; Hossein Payberah, Amir; Soylu, Ahmet; Roman, Dumitru & Matskin, Mihhail (2023). ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER. In Cuzzocrea, Alfredo; Sharmin, Moushumi; Teranishi, Yuuichi & Towey, Dave (Ed.), 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE conference proceedings. ISSN 979-8-3503-2697-0. doi: 10.1109/COMPSAC57700.2023.00038. Full text in Research Archive
• Khan, Akif Quddus; Nikolov, Nikolay Vladimirov; Matskin, Mihhail; Prodan, Radu; Bussler, Christoph & Roman, Dumitru [Show all 7 contributors for this article] (2023). Towards Graph-based Cloud Cost Modelling and Optimisation. In Cuzzocrea, Alfredo; Sharmin, Moushumi; Teranishi, Yuuichi & Towey, Dave (Ed.), 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE conference proceedings. ISSN 979-8-3503-2697-0. doi: 10.1109/COMPSAC57700.2023.00203. Show summary

  Cloud computing has become an increasingly popular choice for businesses and individuals due to its flexibility, scalability, and convenience; however, the rising cost of cloud resources has become a significant concern for many. The payper-use model used in cloud computing means that costs can accumulate quickly, and the lack of visibility and control can result in unexpected expenses. The cost structure becomes even more complicated when dealing with hybrid or multi-cloud environments. For businesses, the cost of cloud computing can be a significant portion of their IT budget, and any savings can lead to better financial stability and competitiveness. In this respect, it is essential to manage cloud costs effectively. This requires a deep understanding of current resource utilization, forecasting future needs, and optimising resource utilization to control costs. To address this challenge, new tools and techniques are being developed to provide more visibility and control over cloud computing costs. In this respect, this paper explores a graphbased solution for modelling cost elements and cloud resources and potential ways to solve the resulting constraint problem of cost optimisation. We primarily consider utilization, cost, performance, and availability in this context. Such an approach will eventually help organizations make informed decisions about cloud resource placement and manage the costs of software applications and data workflows deployed in single, hybrid, or multi-cloud environments.

• Bakken, Magnus & Soylu, Ahmet (2023). Chrontext: Portable SPARQL queries over contextualised time series data in industrial settings. Expert Systems With Applications. ISSN 0957-4174. 226. doi: 10.1016/j.eswa.2023.120149. Full text in Research Archive Show summary

  Industrial information models are standardised ways of representing industrial devices, equipment, and processes together with the data collected from associated sensors and control systems. Companies invest in such models to enable digitalisation and modular, reusable solutions. They also invest heavily in analytics (e.g. machine learning) based on time series data sets to improve operations. Queries that use such context to retrieve time series data can make industrial data sets more accessible to practitioners performing analytics and application development. Moreover, they can enable scalable deployment of resulting analytical models. Industrial availability constraints require that queries over context and time series should be portable in general, as they should be able retrieve data for training in a cloud setting and production data for deployment in an on-premise setting. Solving this problem is challenging with existing approaches as context and time series data tend to exist in separate, specialised databases. We address the issue by proposing a hybrid query engine, namely Chrontext, in the setting of a SPARQL database hosting the static model, and an arbitrary time series database. We show how with a set of annotations in the knowledge graph, SPARQL queries can be evaluated over such a hybrid architecture. We provide a proof showing that our approach correctly answers SPARQL 1.1. queries. We implement our approach in Rust under the Apache 2.0 license, and use the Apache Arrow-based Polars library together with configurable pushdowns to achieve high performance. We compare the performance of Chrontext against Ontop, one of the most prominent virtual knowledge graph systems, on a synthetic data set based on industrial standards. Data are stored in a S3 data lake and PostgreSQL with the Dremio data lakehouse as the SQL integrator. We find that our approach performs 10× to 85× faster and consumes much less memory than Ontop.

• Su Yilmaz, Vadi; Akdag, Metehan; Dalveren, Yaser; Doruk, Resat Ozgur; Kara, Ali & Soylu, Ahmet (2023). Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images. Diagnostics (Basel). ISSN 2075-4418. 13(4). doi: 10.3390/diagnostics13040651. Show summary

  Brain tumors have been the subject of research for many years. Brain tumors are typically classified into two main groups: benign and malignant tumors. The most common tumor type among malignant brain tumors is known as glioma. In the diagnosis of glioma, different imaging technologies could be used. Among these techniques, MRI is the most preferred imaging technology due to its high-resolution image data. However, the detection of gliomas from a huge set of MRI data could be challenging for the practitioners. In order to solve this concern, many Deep Learning (DL) models based on Convolutional Neural Networks (CNNs) have been proposed to be used in detecting glioma. However, understanding which CNN architecture would work efficiently under various conditions including development environment or programming aspects as well as performance analysis has not been studied so far. In this research work, therefore, the purpose is to investigate the impact of two major programming environments (namely, MATLAB and Python) on the accuracy of CNN-based glioma detection from Magnetic Resonance Imaging (MRI) images. To this end, experiments on the Brain Tumor Segmentation (BraTS) dataset (2016 and 2017) consisting of multiparametric magnetic MRI images are performed by implementing two popular CNN architectures, the three-dimensional (3D) U-Net and the V-Net in the programming environments. From the results, it is concluded that the use of Python with Google Colaboratory (Colab) might be highly useful in the implementation of CNN-based models for glioma detection. Moreover, the 3D U-Net model is found to perform better, attaining a high accuracy on the dataset. The authors believe that the results achieved from this study would provide useful information to the research community in their appropriate implementation of DL approaches for brain tumor detection.

• Khan, Akif Quddus; Nikolov, Nikolay; Matskin, Minhail; Prodan, Radu; Roman, Dumitru & Sahin, Bekir [Show all 8 contributors for this article] (2023). Smart Data Placement Using Storage-as-a-Service Model for Big Data Pipelines. Sensors. ISSN 1424-8220. 23(2). doi: 10.3390/s23020564. Full text in Research Archive Show summary

  Big data pipelines are developed to process data characterized by one or more of the three big data features, commonly known as the three Vs (volume, velocity, and variety), through a series of steps (e.g., extract, transform, and move), making the ground work for the use of advanced analytics and ML/AI techniques. Computing continuum (i.e., cloud/fog/edge) allows access to virtually infinite amount of resources, where data pipelines could be executed at scale; however, the implementation of data pipelines on the continuum is a complex task that needs to take computing resources, data transmission channels, triggers, data transfer methods, integration of message queues, etc., into account. The task becomes even more challenging when data storage is considered as part of the data pipelines. Local storage is expensive, hard to maintain, and comes with several challenges (e.g., data availability, data security, and backup). The use of cloud storage, i.e., storage-as-a-service (StaaS), instead of local storage has the potential of providing more flexibility in terms of scalability, fault tolerance, and availability. In this article, we propose a generic approach to integrate StaaS with data pipelines, i.e., computation on an on-premise server or on a specific cloud, but integration with StaaS, and develop a ranking method for available storage options based on five key parameters: cost, proximity, network performance, server-side encryption, and user weights/preferences. The evaluation carried out demonstrates the effectiveness of the proposed approach in terms of data transfer performance, utility of the individual parameters, and feasibility of dynamic selection of a storage option based on four primary user scenarios.

• Zheng, Zhuoxun; Zhou, Baifan; Soylu, Ahmet & Kharlamov, Evgeny (2022). Towards a Visualisation Ontology for Data Analysis in Industrial Applications. CEUR Workshop Proceedings. ISSN 1613-0073. 3355. Full text in Research Archive
• Khan, Akif Quddus; Nikolov, Nikolay Vladimirov; Matskin, Mihhail; Prodan, Radu; Song, Hui & Roman, Dumitru [Show all 7 contributors for this article] (2022). Smart Data Placement for Big Data Pipelines: An Approach based on the Storage-as-a-Service Model. In Zhao, Xinghui & Sill, Alan (Ed.), 15th IEEE/ACM International Conference on Utility and Cloud Computing. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-6654-6087-3. p. 317–320. doi: 10.1109/UCC56403.2022.00056. Full text in Research Archive
• Song, Hui; Soylu, Ahmet & Roman, Dumitru (2022). Towards Cognitive Self-Management of IoT-Edge-Cloud Continuum based on User Intents. In Zhao, Xinghui & Sill, Alan (Ed.), 15th IEEE/ACM International Conference on Utility and Cloud Computing. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-6654-6087-3. p. 313–316. doi: 10.1109/UCC56403.2022.00055. Full text in Research Archive
• Tahmasebi, Shirin; Hossein Payberah, Amir; Soylu, Ahmet; Roman, Dumitru & Matskin, Mihhail (2022). TranSQL: A Transformer-based Model for Classifying SQL Queries. In IEEE, . (Eds.), Proceedings 21st IEEE international conference on machine learning and applications : ICMLA 2022. IEEE conference proceedings. ISSN 978-1-6654-6283-9. p. 788–793. doi: 10.1109/ICMLA55696.2022.00131. Full text in Research Archive
• Roman, Dumitru; Pultier, Antoine; Ma, Xiang; Soylu, Ahmet & Ulyashin, Alexander (2022). Data quality issues in solar panels installations: a case study. In Nguyen, Phu Hong; Magnanini, Maria Chiara & Sen, Sagar (Ed.), Proceedings of the 2nd International Workshop on Software Engineering and AI for Data Quality in Cyber-Physical Systems/Internet of Things (SEA4DQ 2022). Association for Computing Machinery (ACM). ISSN 978-1-4503-9459-8. p. 24–25. doi: 10.1145/3549037.3564120. Full text in Research Archive Show summary

  Solar photovoltaics (PV) is becoming an important source of global electricity generation. Modern PV installations come with a variety of sensors attached to them for monitoring purposes (e.g., maintenance, prediction of electricity generation, etc.). Data collection (and implicitly the quality of data) from PV systems is becoming essential in this context. In this position paper, we introduce a modern PV mini power plant demo site setup for research purposes and discuss the data quality issues we encountered in operating the power plant.

• Zhou, Dongzhuoran; Zhou, Baifan; Zheng, Zhuoxun; Soylu, Ahmet; Savkovic, Ognjen & Kostylev, Egor [Show all 7 contributors for this article] (2022). ScheRe: Schema Reshaping for Enhancing Knowledge Graph Construction. In Al Hasan, Mohammad & Xiong, Li (Ed.), CIKM'22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM). ISSN 978-1-4503-9236-5. p. 5074–5078. doi: 10.1145/3511808.3557214. Show summary

  Automatic knowledge graph (KG) construction is widely used for e.g. data integration, question answering and semantic search. There are many approaches of automatic KG construction. Among which, an important approach is to map the raw data to a given domain KG schema, e.g., domain ontology or conceptual graph, and construct the entities and properties according to the domain KG schema. However, the existing approaches to construct KGs are not always efficient enough and the resulting KGs are not sufficiently user-friendly. The main challenge arises from the trade-off: the domain KG schema should be knowledge-oriented, to reflect the general domain knowledge; while a KG schema should be dataoriented, to cover all data features. If the former is directly used for KG construction, this can cause issues like a high load of blank nodes, which are technical nodes in the KGs that represent unknown entities. To this end, we propose our ScheRe system in the demo, which relies on a schema reshaping algorithm and other two semantic modules for enhancing KG construction. The demo attendees will use ScheRe to reshape a domain KG schema to data specific KG schema, build KGs with industrial data, and experience more user-friendly querying.

• Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Soylu, Ahmet & Kharlamov, Evgeny (2022). ExeKG: Executable Knowledge Graph System for User-friendly Data Analytics. In Al Hasan, Mohammad & Xiong, Li (Ed.), CIKM'22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM). ISSN 978-1-4503-9236-5. p. 5064–5068. doi: 10.1145/3511808.3557195.
• Lin, Tengteng; Chen, Qiaosheng; Cheng, Gong; Soylu, Ahmet; Ell, Basil & Zhao, Ruoqi [Show all 10 contributors for this article] (2022). ACORDAR: A Test Collection for Ad Hoc Content-Based (RDF) Dataset Retrieval. In Amigó, Enrique; Castells, Pablo; Gonzalo, Julio; Carterette, Ben; Culpepper, J. Shane & Kazai, Gabriella (Ed.), SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022. Association for Computing Machinery (ACM). ISSN 978-1-4503-8732-3. p. 2981–2991. doi: 10.1145/3477495.3531729. Show summary

  Ad hoc dataset retrieval is a trending topic in IR research. Methods and systems are evolving from metadata-based to content-based ones which exploit the data itself for improving retrieval accuracy but thus far lack a specialized test collection. In this paper, we build and release the first test collection for ad hoc content-based dataset retrieval, where content-oriented dataset queries and content-based relevance judgments are annotated by human experts who are assisted with a dashboard designed specifically for comprehensively and conveniently browsing both the metadata and data of a dataset. We conduct extensive experiments on the test collection to analyze its difficulty and provide insights into the underlying task.

• Roman, Dumitru; Prodan, Radu; Nikolov, Nikolay; Soylu, Ahmet; Matskin, Mihhail & Marrella, Andrea [Show all 13 contributors for this article] (2022). Big Data Pipelines on the Computing Continuum: Tapping the Dark Data. Computer. ISSN 0018-9162. 55(11), p. 74–84. doi: 10.1109/MC.2022.3154148. Full text in Research Archive
• Gurcan, Fatih; Dalveren, Gonca Gokce Menekse; Cagiltay, Nergiz Ercil; Roman, Dumitru & Soylu, Ahmet (2022). Evolution of Software Testing Strategies and Trends: Semantic Content Analysis of Software Research Corpus of the Last 40 Years. IEEE Access. ISSN 2169-3536. 10, p. 106093–106109. doi: 10.1109/ACCESS.2022.3211949. Full text in Research Archive
• Thomas, Aleena; Nikolov, Nikolay; Pultier, Antoine; Roman, Dumitru; Elvesæter, Brian & Soylu, Ahmet (2022). SIM-PIPE DryRunner: An approach for testing container-based big data pipelines and generating simulation data. Computer Software and Applications Conference. ISSN 0730-3157. p. 1159–1164. doi: 10.1109/COMPSAC54236.2022.00182. Full text in Research Archive Show summary

  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.

• Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Cheng, Gong; Jimenez-Ruiz, Ernesto & Soylu, Ahmet [Show all 7 contributors for this article] (2022). Query-Based Industrial Analytics over Knowledge Graphs with Ontology Reshaping. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13384, p. 123–128. doi: 10.1007/978-3-031-11609-4_23. Full text in Research Archive
• Zhou, Baifan; Zheng, Zhuoxun; Zhou, Dongzhuoran; Cheng, Gong; Jimenez-Ruiz, Ernesto & Trung-Kien, Tran [Show all 11 contributors for this article] (2022). The Data Value Quest: A Holistic Semantic Approach at Bosch. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13384, p. 287–290. doi: 10.1007/978-3-031-11609-4_42. Full text in Research Archive
• Zhou, Dongzhuoran; Zhou, Baifan; Zheng, Zhuoxun; Kostylev, Egor; Cheng, Gong & Jimenez-Ruiz, Ernesto [Show all 8 contributors for this article] (2022). Enhancing Knowledge Graph Generation with Ontology Reshaping – Bosch Case. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13384, p. 299–302. doi: 10.1007/978-3-031-11609-4_45. Full text in Research Archive
• Fatih, Gurcan; Gonca Gokce Menekse, Dalveren; Nergiz, Ercil Cagiltay & Soylu, Ahmet (2022). Detecting Latent Topics and Trends in Software Engineering Research Since 1980 Using Probabilistic Topic Modeling. IEEE Access. ISSN 2169-3536. 10, p. 74638–74654. doi: 10.1109/ACCESS.2022.3190632. Full text in Research Archive
• Islam, Md Nazmul; Hasan, Mehedi; Hossain, Md Kabir; Alam, Md Golam Rabiul; Uddin, Md Zia & Soylu, Ahmet (2022). Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography. Scientific Reports. ISSN 2045-2322. 12(1). doi: 10.1038/s41598-022-15634-4. Full text in Research Archive
• Roman, Dumitru; Song, Hui; Loupos, Konstantinos; Krousarlis, Thomas; Soylu, Ahmet & Skarmeta, Antonio F. (2022). The Computing Fleet: Managing Microservices-based Applications on the Computing Continuum, 2022 IEEE 19th International Conference on Software Architecture Companion (ICSA-C). IEEE conference proceedings. ISSN 978-1-6654-9493-9. p. 40–44. doi: 10.1109/ICSA-C54293.2022.00015. Full text in Research Archive Show summary

  In this paper we propose the concept of "Computing Fleet" as an abstract entity representing groups of heterogeneous, distributed, and dynamic infrastructure elements across the Computing Continuum (covering the Edge- Fog-Cloud computing paradigms). In the process of using fleets, stakeholders obtain the virtual resources from the fleet, deploy software applications to the fleet, and control the data flow, without worrying about what devices are used in the fleet, how they are connected, and when they may join and exit the fleet. We propose a three-layer reference architecture for the Computing Fleet capturing key elements for designing and operating fleets. We discuss key aspects related to the management of microservices-based applications on the Computing Fleet and propose an approach for deployment and orchestration of microservices-based applications on fleets. Furthermore, we present a software prototype as a preliminary evaluation of the Computing Fleet concept in a concrete Cloud- Edge scenario related to remote patients monitoring.

• Ryen, Vetle; Soylu, Ahmet & Roman, Dumitru (2022). Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review. Future Internet. ISSN 1999-5903. 14(5). doi: 10.3390/fi14050129. Full text in Research Archive
• Soylu, Ahmet; Corcho, Oscar; Elvesæter, Brian; Badenes-Olmedo, Carlos; Yedro Martínez, Francisco & Kovavic, Matej [Show all 13 contributors for this article] (2022). Data Quality Barriers for Transparency in Public Procurement. Information. ISSN 2078-2489. 13(2). doi: 10.3390/info13020099. Full text in Research Archive
• Soylu, Ahmet; Corcho, Oscar; Elvesæter, Brian; Badenes-Olmedo, Carlos; Blount, Tom & Yedro Martínez, Francisco [Show all 13 contributors for this article] (2022). TheyBuyForYou platform and knowledge graph: Expanding horizons in public procurement with open linked data. Semantic Web Journal. ISSN 1570-0844. 13(2), p. 265–291. doi: 10.3233/SW-210442. Full text in Research Archive Show summary

  Public procurement is a large market affecting almost every organisation and individual; therefore, governments need to ensure its efficiency, transparency, and accountability, while creating healthy, competitive, and vibrant economies. In this context, open data initiatives and integration of data from multiple sources across national borders could transform the procurement market by such as lowering the barriers of entry for smaller suppliers and encouraging healthier competition, in particular by enabling cross-border bids. Increasingly more open data is published in the public sector; however, these are created and maintained in siloes and are not straightforward to reuse or maintain because of technical heterogeneity, lack of quality, insufficient metadata, or missing links to related domains. To this end, we developed an open linked data platform, called TheyBuyForYou, consisting of a set of modular APIs and ontologies to publish, curate, integrate, analyse, and visualise an EU-wide, cross-border, and cross-lingual procurement knowledge graph. We developed advanced tools and services on top of the knowledge graph for anomaly detection, cross-lingual document search, and data storytelling. This article describes the TheyBuyForYou platform and knowledge graph, reports their adoption by different stakeholders and challenges and experiences we went through while creating them, and demonstrates the usefulness of Semantic Web and Linked Data technologies for enhancing public procurement.

• Roman, Dumitru; Alexiev, Vladimir; Paniagua, Javier; Elvesæter, Brian; Zernichow, Bjørn Marius von & Soylu, Ahmet [Show all 8 contributors for this article] (2022). The euBusinessGraph ontology: A lightweight ontology for harmonizing basic company information. Semantic Web Journal. ISSN 1570-0844. 13(1), p. 41–68. doi: 10.3233/SW-210424. Full text in Research Archive Show summary

  Company data, ranging from basic company information such as company name(s) and incorporation date to complex balance sheets and personal data about directors and shareholders, are the foundation that many data value chains depend upon in various sectors (e.g., business information, marketing and sales, etc.). Company data becomes a valuable asset when data is collected and integrated from a variety of sources, both authoritative (e.g., national business registers) and non-authoritative (e.g., company websites). Company data integration is however a difficult task primarily due to the heterogeneity and complexity of company data, and the lack of generally agreed upon semantic descriptions of the concepts in this domain. In this article, we introduce the euBusinessGraph ontology as a lightweight mechanism for harmonising company data for the purpose of aggregating, linking, provisioning and analysing basic company data. The article provides an overview of the related work, ontology scope, ontology development process, explanations of core concepts and relationships, and the implementation of the ontology. Furthermore, we present scenarios where the ontology was used, among others, for publishing company data (business knowledge graph) and for comparing data from various company data providers. The euBusinessGraph ontology serves as an asset not only for enabling various tasks related to company data but also on which various extensions can be built upon.

• Roman, Dumitru; Nikolov, Nikolay; Elvesæter, Brian; Soylu, Ahmet; Prodan, Radu & Kimovski, Dragi [Show all 17 contributors for this article] (2021). DataCloud: Enabling the Big Data Pipelines on the Computing Continuum. In Cherfi, Samira; Perini, Anna & Nurcan, Selmin (Ed.), Research Challenges in Information Science: 15th International Conference, RCIS 2021, Limassol, Cyprus, May 11–14, 2021, Proceedings. Springer. ISSN 978-3-030-75017-6. p. 715–717. doi: 10.1007/978-3-030-75018-3. Full text in Research Archive
• Roman, Dumitru; Nikolov, Nikolay; Soylu, Ahmet; Elvesæter, Brian; Song, Hui & Prodan, Radu [Show all 18 contributors for this article] (2021). Big Data Pipelines on the Computing Continuum: Ecosystem and Use Cases Overview. Proceedings of the IEEE Symposium on Computers and Communications. ISSN 1530-1346. doi: 10.1109/ISCC53001.2021.9631410. Full text in Research Archive Show summary

  Organisations possess and continuously generate huge amounts of static and stream data, especially with the proliferation of Internet of Things technologies. Collected but unused data, i.e., Dark Data, mean loss in value creation potential. In this respect, the concept of Computing Continuum extends the traditional more centralised Cloud Computing paradigm with Fog and Edge Computing in order to ensure low latency pre-processing and filtering close to the data sources. However, there are still major challenges to be addressed, in particular related to management of various phases of Big Data processing on the Computing Continuum. In this paper, we set forth an ecosystem for Big Data pipelines in the Computing Continuum and introduce five relevant real-life example use cases in the context of the proposed ecosystem.

• Corodescu, Andrei-Alin; Nikolov, Nikolay; Khan, Akif Quddus; Soylu, Ahmet; Matskin, Mihhail & Payberah, Amir H. [Show all 7 contributors for this article] (2021). Big data workflows: Locality-aware orchestration using software containers. Sensors. ISSN 1424-8220. 21(24), p. 1–27. doi: 10.3390/s21248212. Full text in Research Archive Show summary

  The emergence of the Edge computing paradigm has shifted data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructures. Therefore, data processing solutions must consider data locality to reduce the performance penalties from data transfers among remote data centres. Existing Big Data processing solutions provide limited support for handling data locality and are inefficient in processing small and frequent events specific to the Edge environments. This article proposes a novel architecture and a proof-of-concept implementation for software container-centric Big Data workflow orchestration that puts data locality at the forefront. The proposed solution considers the available data locality information, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare the proposed solution with Argo Workflows and demonstrate a significant performance improvement in the execution speed for processing the same data units. Finally, we carry out experiments with the proposed solution under different configurations and analyze individual aspects affecting the performance of the overall solution.

• Corodescu, Andrei-Alin; Nikolov, Nikolay; Khan, Akif Quddus; Soylu, Ahmet; Matskin, Mihhail & Payberah, Amir [Show all 7 contributors for this article] (2021). Locality-Aware Workflow Orchestration for Big Data. In Chbeir, Richard (Eds.), MEDES '21: Proceedings of the 13th International Conference on Management of Digital EcoSystems. Association for Computing Machinery (ACM). ISSN 978-1-4503-8314-1. p. 62–70. doi: 10.1145/3444757.3485106. Full text in Research Archive Show summary

  The development of the Edge computing paradigm shifts data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructure. Such a paradigm requires data processing solutions that consider data locality in order to reduce the performance penalties from data transfers between remote (in network terms) data centres. However, existing Big Data processing solutions have limited support for handling data locality and are inefficient in processing small and frequent events specific to Edge environments. This paper proposes a novel architecture and a proof-of-concept implementation for software container-centric Big Data workflow orchestration that puts data locality at the forefront. Our solution considers any available data locality information by default, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare our system with Argo workflow and show significant performance improvements in terms of speed of execution for processing units of data using our data locality aware Big Data workflow approach.

• Parnas Gulnes, Maren; Soylu, Ahmet & Roman, Dumitru (2021). A graph-based approach for representing, integrating and analysing neuroscience data: the case of the murine basal ganglia. Data Technologies and Applications. ISSN 2514-9288. 56(3). doi: 10.1108/DTA-12-2020-0303. Full text in Research Archive Show summary

  Purpose – Neuroscience data are spread across a variety of sources, typically provisioned through ad-hoc and non-standard approaches and formats and often have no connection to the related data sources. These make it difficult for researchers to understand, integrate and reuse brain-related data. The aim of this study is to show that a graph-based approach offers an effective mean for representing, analysing and accessing brain-related data, which is highly interconnected, evolving over time and often needed in combination. Design/methodology/approach – The authors present an approach for organising brain-related data in a graph model. The approach is exemplified in the case of a unique data set of quantitative neuroanatomical data about the murine basal ganglia––a group of nuclei in the brain essential for processing information related to movement. Specifically, the murine basal ganglia data set is modelled as a graph, integrated with relevant data from third-party repositories, published through a Web-based user interface and API, analysed from exploratory and confirmatory perspectives using popular graph algorithms to extract new insights. Findings –The evaluation of the graph model and the results of the graph data analysis and usability study of the user interface suggest that graph-based data management in the neuroscience domain is a promising approach, since it enables integration of various disparate data sources and improves understanding and usability of data. Originality/value – The study provides a practical and generic approach for representing, integrating, analysing and provisioning brain-related data and a set of software tools to support the proposed approach. Keywords Graph databases, Neuroscience, Brain-related data, Murine basal ganglia, Data integration, Data analytics, Data visualisation Paper type Research paper

• Nikolov, Nikolay; Dessalk, Yared Dejene; Khan, Akif Quddus; Soylu, Ahmet; Matskin, Mihhail & Payberah, Amir [Show all 7 contributors for this article] (2021). Conceptualization and scalable execution of big data workflows using domain-specific languages and software containers. Internet of Things: Engineering Cyber Physical Human Systems. ISSN 2542-6605. 16, p. 1–19. doi: 10.1016/j.iot.2021.100440. Full text in Research Archive Show summary

  Big Data processing, especially with the increasing proliferation of Internet of Things (IoT) technologies and convergence of IoT, edge and cloud computing technologies, involves handling massive and complex data sets on heterogeneous resources and incorporating different tools, frameworks, and processes to help organizations make sense of their data collected from various sources. This set of operations, referred to as Big Data workflows, requires taking advantage of Cloud infrastructures’ elasticity for scalability. In this article, we present the design and prototype implementation of a Big Data workflow approach based on the use of software container technologies, message-oriented middleware (MOM), and a domain-specific language (DSL) to enable highly scalable workflow execution and abstract workflow definition. We demonstrate our system in a use case and a set of experiments that show the practical applicability of the proposed approach for the specification and scalable execution of Big Data workflows. Furthermore, we compare our proposed approach’s scalability with that of Argo Workflows – one of the most prominent tools in the area of Big Data workflows – and provide a qualitative evaluation of the proposed DSL and overall approach with respect to the existing literature.

• Sahin, Bekir; Yazidi, Anis; Roman, Dumitru; Uddin, Md Zia & Soylu, Ahmet (2021). An Analysis of Data Production Based on the Consistency of Decision Matrices. Lecture Notes in Networks and Systems. ISSN 2367-3370. 307, p. 875–882. doi: 10.1007/978-3-030-85626-7_102. Full text in Research Archive
• Sahin, Bekir; Yazidi, Anis; Roman, Dumitru; Uddin, Md Zia & Soylu, Ahmet (2021). On Converting Crisp Failure Possibility into Probability for Reliability of Complex Systems. Lecture Notes in Networks and Systems. ISSN 2367-3370. 308, p. 622–629. doi: 10.1007/978-3-030-85577-2_73. Full text in Research Archive
• Zhou, Baifan; Svetashova, Yulia; Silva Gusmao, Andre; Soylu, Ahmet; Cheng, Gong & Mikut, Ralf [Show all 8 contributors for this article] (2021). SemML: Facilitating development of ML models for condition monitoring with semantics. Journal of Web Semantics. ISSN 1570-8268. 71, p. 1–21. doi: 10.1016/j.websem.2021.100664. Full text in Research Archive
• Roman, Dumitru; Reeves, Neal; Gonzalez, Esteban; Celino, Irene; Abd El Kader, Shady & Turk, Philip Andreas [Show all 12 contributors for this article] (2021). An analysis of pollution Citizen Science projects from the perspective of Data Science and Open Science. Data Technologies and Applications. ISSN 2514-9288. 55(5), p. 622–642. doi: 10.1108/DTA-10-2020-0253. Full text in Research Archive
• Sahin, Bekir; Yazir, Devran; Soylu, Ahmet & Yip, Tsz Leung (2021). Improved fuzzy AHP based game-theoretic model for shipyard selection. Ocean Engineering. ISSN 0029-8018. 233. doi: 10.1016/j.oceaneng.2021.109060.
• Uddin, Md Zia & Soylu, Ahmet (2021). Human activity recognition using wearable sensors, discriminant analysis, and long short-term memory-based neural structured learning. Scientific Reports. ISSN 2045-2322. 11(1), p. 1–15. doi: 10.1038/s41598-021-95947-y. Full text in Research Archive
• Sahin, Bekir; Yazidi, Anis; Roman, Dumitru & Soylu, Ahmet (2021). Ontology-Based Fault Tree Analysis Algorithms in a Fuzzy Environment for Autonomous Ships. IEEE Access. ISSN 2169-3536. 9, p. 1–18. doi: 10.1109/ACCESS.2021.3061929. Full text in Research Archive
• Soylu, Ahmet; Corcho, Oscar; Elvesæter, Brian; Badenes-Olmedo, Carlos; Martínez, Francisco Yedro & Kovacic, Matej [Show all 12 contributors for this article] (2020). Integrating and analysing public procurement data through a knowledge graph: A demonstration in a nutshell. CEUR Workshop Proceedings. ISSN 1613-0073. 2721(492), p. 34–38. Show summary

  This paper presents a demonstrator of a knowledge graph based approach for integrating and reconciling cross-border and cross-language procurement and company data from distributed data sources. The demonstrator also includes analysis of the resulting knowledge graph, exemplified in anomaly detection and cross-lingual search.

• Roman, Dumitru; Alexiev, Vladimir; Paniagua, Javier; Elvesæter, Brian; Zernichow, Bjørn Marius von & Soylu, Ahmet [Show all 8 contributors for this article] (2020). A Bird's-eye view of euBusinessGraph: A business knowledge graph for company data. CEUR Workshop Proceedings. ISSN 1613-0073. 2721(493), p. 39–43. Show summary

  This poster paper provides an overview of euBusinessGraph – a business knowledge graph for basic company data, together with related artefacts (datasets, ontology, and infrastructure), and its use for creating a prototype for a data marketplace for basic company data. euBusinessGraph was developed by aggregating, linking, and provisioning data from several distributed data sources.

• Roman, Dumitru & Soylu, Ahmet (2020). Enabling the European Business Knowledge Graph for Innovative Data-Driven Products and Services. ERCIM News. ISSN 0926-4981.
• Samuelsen, Simen; Nikolov, Nikolay; Soylu, Ahmet & Roman, Dumitru (2020). An Approach for Representing and Storing RDF Data in Multi-model Databases, 14th International Conference on Metadata and Semantics Research (MTSR 2020). Springer Nature. ISSN 978-3-030-36598-1. Show summary

  The emergence of NoSQL multi-model databases, nativelysupporting scalable and unified storage and querying of various datamodels, presents new opportunities for storing and managing RDF data.In this paper, we propose an approach to store RDF data in multi-modeldatabases. We identify various aspects of representing the RDF datastructure into a multi-model data structure and discuss their advantagesand disadvantages. Furthermore, we implement and evaluate the pro-posed approach in a prototype using ArangoDB—a popular multi-modeldatabase.

• Soylu, Ahmet; Corcho, Oscar; Elvesæter, Brian; Badenes-Olmedo, Carlos; Yedro Martínez, Francisco & Kovacic, Matej [Show all 12 contributors for this article] (2020). Enhancing Public Procurement in the European Union Through Constructing and Exploiting an Integrated Knowledge Graph. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 12507, p. 430–446. doi: 10.1007/978-3-030-62466-8_27. Show summary

  Public procurement is a large market affecting almost every organisation and individual. Governments need to ensure efficiency, transparency, and accountability, while creating healthy, competitive, and vibrant economies. In this context, we built a platform, consisting of a set of modular APIs and ontologies to publish, curate, integrate, analyse, and visualise an EU-wide, cross-border, and cross-lingual procurement knowledge graph. We developed end-user tools on top of the knowledge graph for anomaly detection and cross-lingual document search. This paper describes our experiences and challenges faced in creating such a platform and knowledge graph and demonstrates the usefulness of Semantic Web technologies for enhancing public procurement.

• Dessalk, Yared Dejene; Nikolov, Nikolay; Matskin, Mihhail; Soylu, Ahmet & Roman, Dumitru (2020). Scalable Execution of Big Data Workflows using Software Containers. In Chbeir, Richard; Manolopoulos, Yannis; Damiani, Ernesto & Benslimane, Djamal (Ed.), MEDES '20: Proceedings of the 12th International Conference on Management of Digital EcoSystems. ACM Publications. ISSN 978-1-4503-8115-4. p. 76–83. doi: 10.1145/3415958.3433082. Show summary

  Big Data processing involves handling large and complex data sets, incorporating different tools and frameworks as well as other processes that help organisations make sense of their data collected from various sources. This set of operations, referred to as Big Data workflows, require taking advantage of the elasticity of cloud infrastructures for scalability. In this paper, we present the design and prototype implementation of a Big Data workflow approach based on the use of software container technologies and message-oriented middleware (MOM) to enable highly scalable workflow execution. The approach is demonstrated in a use case together with a set of experiments that demonstrate the practical applicability of the proposed approach for the scalable execution of Big Data workflows. Furthermore, we present a scalability comparison of our proposed approach with that of Argo Workflows - one of the most prominent tools in the area of Big Data workflows.

• Sahin, Bekir; Uddin, Md Zia & Soylu, Ahmet (2020). Towards Optimal Ship Navigation Using Image Processing. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 12509, p. 135–147. doi: 10.1007/978-3-030-64556-4_11. Full text in Research Archive Show summary

  Shipping transportation developed over years with the technological advancements. Modern ship navigation is conducted with the help of Automatic Radar Plotting Aid (ARPA) and Electronic Chart Display and Information System (ECDIS). Location map, marine traffic, geographical conditions, and obstacles in a region can be monitored by these technologies. The obstacles may vary from icebergs and ice blocks to islands, debris, rocks, or other vessels in a given vicinity. In this study, we propose an approach for route optimization using two-dimensional radar images and image segmentation in an environment with obstacles. The navigation algorithm takes image segmentation results as an input and finds the optimal route (i.e. safest and shortest). One of the advantages of this study is that the obstacles are not solely polygonal, but they may be in any shape, size, and color. The proposed approach has some practical and computational limitations; however, the future unmanned vessels could benefit from the improved applications of this route optimization approach in terms of energy consumption, time, and workforce.

• Zhou, Baifan; Svetashova, Yulia; Pychynski, Tim; Baimuratov, Ildar; Soylu, Ahmet & Kharlamov, Evgeny (2020). SemFE: Facilitating ML Pipeline Development with Semantics. In d'Aquin, Mathieu & Dietze, Stefan (Ed.), CIKM 20 : The 29th ACM international conference on information and knowledge management : Virtual Event Ireland, October 19-23, 2020.. ACM Publications. ISSN 9781450368599. p. 3489–3492. doi: 10.1145/3340531.3417436. Show summary

  Machine learning (ML) based data analysis has attracted an increasing attention in the manufacturing industry, however, many challenges hamper their wide spread adoption. The main challenges are the high costs of labour-intensive data preparation from diverse sources and processes, the asymmetrical backgrounds of the experts involved in manufacturing analyses that impede efficient communication between them, and the lack of generalisability of ML models tailored to specific applications. Our semantically enhanced ML pipeline, SemFE, with feature engineering addresses these challenges, serving as a bridge to bring the endeavours of experts together, and making data science accessible to non-ML-experts. SemFE relies on ontologies for discrete manufacturing monitoring that encapsulate domain and ML knowledge; it has five novel semantic modules for automation of ML-pipeline development and user-friendly GUIs. The demo attendees will be able to use our system to build manufacturing monitoring ML pipelines, and to design their own pipelines with minimal prior knowledge of machine learning.

• Sahin, Bekir & Soylu, Ahmet (2020). Intuitionistic fuzzy analytical network process models for maritime supply chain. Applied Soft Computing. ISSN 1568-4946. 96. doi: 10.1016/j.asoc.2020.106614.
• Sahin, Bekir; Yip, Tsz Leung; Tseng, Po-Hsing; Kabak, Mehmet & Soylu, Ahmet (2020). An Application of a Fuzzy TOPSIS Multi-Criteria Decision Analysis Algorithm for Dry Bulk Carrier Selection. Information. ISSN 2078-2489. 11(5). doi: 10.3390/info11050251. Full text in Research Archive
• Sahin, Bekir & Soylu, Ahmet (2020). Multi-Layer, Multi-Segment Iterative Optimization for Maritime Supply Chain Operations in a Dynamic Fuzzy Environment. IEEE Access. ISSN 2169-3536. 8, p. 144993–145005. doi: 10.1109/ACCESS.2020.3014968. Full text in Research Archive
• Abd El Kader, Shady; Nikolov, Nikolay; Zernichow, Bjørn Marius von; Cutrona, Vincenzo; Palmonari, Matteo & Elvesæter, Brian [Show all 8 contributors for this article] (2019). Modeling and publishing French business register (SIRENE) data as linked data using the EubusinessGraph ontology. CEUR Workshop Proceedings. ISSN 1613-0073. 2549.
• Sajid, Salhia; Zernichow, Bjørn Marius von; Soylu, Ahmet & Roman, Dumitru (2019). Predictive Data Transformation Suggestions in Grafterizer Using Machine Learning . Communications in Computer and Information Science (CCIS). ISSN 1865-0929. 1057 CCIS, p. 137–149. doi: 10.1007/978-3-030-36599-8_12. Full text in Research Archive Show summary

  Data preprocessing is a crucial step in data analysis. A substantial amount of time is spent on data transformation tasks such as data formatting, modification, extraction, and enrichment, typically making it more convenient for users to work with systems that can recommend most relevant transformations for a given dataset. In this paper, we propose an approach for generating relevant data transformation suggestions for tabular data preprocessing using machine learning (specifically, the Random Forest algorithm). The approach is implemented for Grafterizer, a Web-based framework for tabular data cleaning and transformation, and evaluated through a usability study.

• Soylu, Ahmet; Elvesæter, Brian; Turk, Philip; Roman, Dumitru; Corcho, Oscar & Simperl, Elena [Show all 10 contributors for this article] (2019). An Overview of the TBFY Knowledge Graph for Public Procurement. CEUR Workshop Proceedings. ISSN 1613-0073. 2456. Show summary

  A growing amount of public procurement data is being made available in the EU for the purpose of improving the effectiveness, efficiency, transparency, and accountability of government spending. However, there is a large heterogeneity, due to the lack of common data formats and models. To this end, we developed an ontology network for representing and linking tender and company data and ingested relevant data from two prominent data providers into a knowledge graph, called TBFY. In this poster paper, we present an overview of our knowledge graph.

• Klungre, Vidar Norstein; Soylu, Ahmet; Jimenez-Ruiz, Ernesto; Kharlamov, Evgeny & Giese, Martin (2019). Query Extension Suggestions for Visual Query Systems Through Ontology Projection and Indexing. New generation computing. ISSN 0288-3635. 37(4), p. 361–392. doi: 10.1007/s00354-019-00071-1. Full text in Research Archive
• Soylu, Ahmet; Elvesæter, Brian; Turk, Philip; Roman, Dumitru; Corcho, Oscar & Simperl, Elena [Show all 8 contributors for this article] (2019). Towards an Ontology for Public Procurement Based on the Open Contracting Data Standard. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11701 LNCS, p. 230–237. doi: 10.1007/978-3-030-29374-1_19. Show summary

  The release of a growing amount of open procurement data led to various initiatives for harmonising the data being provided. Among others, the Open Contracting Data Standard (OCDS) is highly relevant due to its high practical value and increasing traction. OCDS defines a common data model for publishing structured data throughout most of the stages of a contracting process. OCDS is document-oriented and focuses on packaging and delivering relevant data in an iterative and event-driven manner through a series of releases. Ontologies, beyond providing uniform access to heterogeneous procurement data, could enable integration with related data sets such as with supplier data for advanced analytics and insight extraction. Therefore, we developed an ontology, the “OCDS ontology”, by using OCDS’ main domain perspective and vocabulary, since it is an essential source of domain knowledge. In this paper, we provide an overview of the developed ontology.

• Soylu, Ahmet & Kharlamov, Evgeny (2019). Navigating OWL 2 Ontologies Through Graph Projection. Communications in Computer and Information Science (CCIS). ISSN 1865-0929. 846, p. 113–119. doi: 10.1007/978-3-030-14401-2_10. Full text in Research Archive Show summary

  Ontologies are powerful, yet often complex, assets for rep- resenting, exchanging, and reasoning over data. Particularly, OWL 2 ontologies have been key for constructing semantic knowledge graphs. Ability to navigate ontologies is essential for supporting various knowledge engineering tasks such as querying and domain exploration. To this end, in this short paper, we describe an approach for projecting the non-hierarchical topology of an OWL 2 ontology into a graph. The approach has been implemented in two tools, one for visual query formulation and one for faceted search, and evaluated under different use cases.

• Simperl, Elena; Corcho, Oscar; Grobelnik, Marko; Roman, Dumitru; Soylu, Ahmet & Ruíz, María Jesús Fernández [Show all 12 contributors for this article] (2019). Towards a Knowledge Graph Based Platform for Public Procurement. Communications in Computer and Information Science (CCIS). ISSN 1865-0929. 846, p. 317–323. doi: 10.1007/978-3-030-14401-2_29. Full text in Research Archive
• Kharlamov, Evgeny; Skjæveland, Martin G; Hovland, Dag; Mailis, Theofilos; Jimenez-Ruiz, Ernesto & Xiao, Guohui [Show all 9 contributors for this article] (2018). Finding Data Should be Easier than Finding Oil. In Abe, Naoki; Liu, Huan; Hu, Xiaohua; Ahmed, Nesreen; Qiao, Mu; Song, Yang; Kossmann, Donald; Liu, Bing; Lee, Kisung; Tang, Jiliang; He, Jingrui & Saltz, Jeffrey (Ed.), 2018 IEEE International Conference on Big Data (Big Data), Seattle, 10-13 Dec. 2018. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-5035-6. doi: 10.1109/BigData.2018.8622035. Full text in Research Archive Show summary

  The competitiveness of modern enterprises heavily depends on their ability to make the right business decisions by relying on efficient and timely analysis of the right business critical data. In large and data intensive companies such as Equinor, a Norwegian multinational oil and gas company with more than 20,000 employees, gathering such data is not a trivial task due to the growing size and complexity of corporate information sources. As a result, the data gathering task is often the most time-consuming part of the decision making process, in particular when it comes to the work processes of Equinor’s Exploration geologists that should find in a timely manner New exploitable accumulations of oil or gas in given areas by analysing data about these areas. In this work we present our experience in addressing this data challenge tast at Equinor. We have developed and deployed at Equinor a semantic data access system that relies on the Ontology Based Data Access (OBDA) approach. Our system is based on our solid theoretical contributions and has been extensively evaluated at Equinor

• Simperl, Elena; Corcho, Oscar; Grobelnik, Marko; Roman, Dumitru; Soylu, Ahmet & Jesús Fernández Ruíz, María [Show all 12 contributors for this article] (2018). TheyBuyForYou: Enabling Procurement Data Value Chains. Communications in Computer and Information Science (CCIS). ISSN 1865-0929. 1115, p. 179–186. doi: 10.1007/978-3-030-63161-1_15.
• Soylu, Ahmet; Corcho, Oscar; Simperl, Elena; Roman, Dumitru; Martínez, Francisco Yedro & Taggart, Chris [Show all 13 contributors for this article] (2018). Towards Integrating Public Procurement Data into a Semantic Knowledge Graph. CEUR Workshop Proceedings. ISSN 1613-0073. 2262, p. 1–4. Full text in Research Archive Show summary

  Public procurement accounts for a substantial part of the public investment and global economy. Therefore, improving effectiveness, efficiency, transparency and accountability of government procurement is of broad interest. To this end, in this poster paper, we present our approach for integrating procurement data, including public spending and corporate data, from multiple sources across the EU into a semantic knowledge graph. We are aiming to improve procurement processes through supporting multiple stake holders, such as government agencies, companies, control authorities, journalists, researchers, and individual citizens.

• Soylu, Ahmet & Kharlamov, Evgeny (2018). Making Complex Ontologies End User Accessible via Ontology Projections. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11341, p. 295–303. doi: 10.1007/978-3-030-04284-4_20. Full text in Research Archive Show summary

  Ontologies are a powerful mechanism to structure domains of interest. They have successfully been applied in medical domain, industry and other important areas. Despite the simplicity of ontological vocabularies that consist of classes and properties, ontologies can relate elements of the vocabulary with the help of axioms in a very non-trivial way. Thus, the relationship between classes and properties can become hardly accessible by end users thus affecting the practical value of ontologies. Indeed, it is essential for end users to be able to navigate or browse through an ontology, to get a big picture of what classes there are and what they have in common in terms of other related classes and properties. This helps end users in effectively performing various knowledge engineering tasks such as querying and domain exploration. To this end, in this short paper, we describe an approach to project OWL 2 ontologies into graphs and show how to leverage this approach in practical systems for visual query formulation and faceted search that we tested in various scenarios.

• Klungre, Vidar Norstein; Soylu, Ahmet; Giese, Martin; Waaler, Arild & Kharlamov, Evgeny (2018). On Enhancing Visual Query Building over KGs Using Query Logs. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11341, p. 77–85. doi: 10.1007/978-3-030-04284-4_6. Full text in Research Archive Show summary

  Knowledge Graphs have recently gained a lot of attention and have been successfully applied in both academia and industry. Since KGs may be very large: they may contain millions of entities and triples relating them to each other, to classes, and assigning them data values, it is important to provide endusers with effective tools to explore information encapsulated in KGs. In this work we present a visual query system that allows users to explore KGs by intuitively constructing tree-shaped conjunctive queries. It is known that systems of this kind suffer from the problem of information overflow: when constructing a query the users have to iteratively choose from a potentially very long list of options, sich as, entities, classes, and data values, where each such choice corresponds to an extension of the query new filters. In order to address this problem we propose an approach to substantially reduce such lists with the help of ranking and by eliminating the so-called deadends, options that yield queries with no answers over a given KG.

• Kharlamov, Evgeny; Hovland, Dag; Skjæveland, Martin G; Bilidas, Dimitris; Jimenez-Ruiz, Ernesto & Xiao, Guohui [Show all 16 contributors for this article] (2017). Ontology Based Data Access in Statoil. Journal of Web Semantics. ISSN 1570-8268. 44, p. 3–36. doi: 10.1016/j.websem.2017.05.005. Full text in Research Archive
• Kharlamov, Evgeny; Mailis, Theofilos; Mehdi, Gulnar; Neuenstadt, Christian; Özçep, Özgür Lütfü & Roshchin, Mikhail [Show all 16 contributors for this article] (2017). Semantic access to streaming and static data at Siemens. Journal of Web Semantics. ISSN 1570-8268. 44, p. 54–74. doi: 10.1016/j.websem.2017.02.001. Full text in Research Archive
• Soylu, Ahmet; Kharlamov, Evgeny; Zheleznyakov, Dmitriy; Jimenez-Ruiz, Ernesto; Giese, Martin & Skjæveland, Martin G [Show all 11 contributors for this article] (2017). OptiqueVQS: a Visual Query System over Ontologies for Industry . Semantic Web Journal. ISSN 1570-0844. p. 1–28. doi: 10.3233/sw-180293. Full text in Research Archive
• Soylu, Ahmet; Giese, Martin; Schlatte, Rudolf; Jimenez-Ruiz, Ernesto; Kharlamov, Evgeny & Özçep, Özgür [Show all 8 contributors for this article] (2017). Querying industrial stream-temporal data: An ontology-based visual approach. Journal of Ambient Intelligence and Smart Environments. ISSN 1876-1364. 9(1), p. 77–95. doi: 10.3233/AIS-160415. Full text in Research Archive
• Soylu, Ahmet; Giese, Martin; Jimenez-Ruiz, Ernesto; Kharlamov, Evgeny; Schlatte, Rudolf & Neuenstadt, Christian [Show all 11 contributors for this article] (2016). Ontology-based visual querying with OptiqueVQS: Statoil and Siemens cases. CEUR Workshop Proceedings. ISSN 1613-0073. 1818, p. 34–39.
• Kharlamov, Evgeny; Mailis, Theofilis P.; Bereta, Konstantina; Bilidas, Dimitris; Brandt, Sebastian & Jimenez-Ruiz, Ernesto [Show all 20 contributors for this article] (2016). A Semantic Approach to Polystores. In Joshi, James (Eds.), Proceedings 2016 IEEE International Conference on Big Data. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4673-9005-7. p. 2565–2573. doi: 10.1109/BigData.2016.7840898.
• Kharlamov, Evgeny; Brandt, Sebastian; Giese, Martin; Jimenez-Ruiz, Ernesto; Kotidis, Yannis & Lamparter, Steffen [Show all 17 contributors for this article] (2016). Enabling semantic access to static and streaming distributed data with optique: demo. In Gal, Avigdor; Weidlich, Matthias; Kalogeraki, Vana & Venkasubramanian, Nalini (Ed.), Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems, DEBS '16, Irvine, CA,USA, June 20 -24. ACM Publications. ISSN 978-1-4503-4021-2. p. 350–353. doi: 10.1145/2933267.2933290.
• Soylu, Ahmet; Giese, Martin; Schlatte, Rudolf; Jiménez-Ruiz, Ernesto; Özcep, Özgür & Brandt, Sebastian (2016). A visual query system for stream data access over ontologies. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 9989, p. 161–166. doi: 10.1007/978-3-319-47602-5_32.
• Soylu, Ahmet; Giese, Martin; Schlatte, Rudolf; Jiménez-Ruiz, Ernesto; Ozcep, Ozgur & Brandt, Sebastian (2016). Domain experts surfing on stream sensor data over ontologies. CEUR Workshop Proceedings. ISSN 1613-0073. 1588, p. 11–20.
• Soylu, Ahmet; Giese, Martin; Jiménez-Ruiz, Ernesto; Kharlamov, Evgeny; Zheleznyakov, Dmitriy & Horrocks, Ian (2016). Ontology-based end-user visual query formulation: Why, what, who, how, and which? Universal Access in the Information Society. ISSN 1615-5289. p. 1–33. doi: 10.1007/s10209-016-0465-0. Full text in Research Archive

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• Fensel, Anna; Ozaki, Ana; Dumitru, Roman & Soylu, Ahmet (2023). Rules and Reasoning - 7th International Joint Conference, RuleML+RR 2023, Oslo, Norway, September 18-20, 2023, Proceedings. Springer Publishing Company. ISBN 978-3-031-45071-6. 14244(1). 269 p.
• Moschoyiannis, Sotiris; Penaloza Nyssen, Rafael; Vanthienen, Jan; Soylu, Ahmet & Roman, Dumitru (2021). Rules and Reasoning. 5th International Joint Conference, RuleML+RR 2021, Leuven, Belgium, September 8–15, 2021, Proceedings. Springer. ISBN 978-3-030-91166-9. 12851(1). 250 p.
• Gutiérrez-Basulto, Víctor; Kliegr, Tomas; Soylu, Ahmet; Giese, Martin & Roman, Dumitru (2020). Rules and Reasoning. 4th International Joint Conference, RuleML+RR 2020, Oslo, Norway, June 29 – July 1, 2020, Proceedings. Springer. ISBN 978-3-030-57977-7. 12173(1). 187 p.

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• Kliegr, Tomáš; Gutierrez-Basulto, Victor & Soylu, Ahmet (2023). Introduction to the Special Issue on Logic Rules and Reasoning: Selected Papers from the 4th International Joint Conference on Rules and Reasoning (RuleML+RR 2020). Theory and Practice of Logic Programming. ISSN 1471-0684. p. 1–4. doi: 10.1017/S1471068423000042.
• Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Soylu, Ahmet & Kharlamov, Evgeny (2022). Executable Knowledge Graph for Transparent Machine Learning in Welding Monitoring at Bosch. In Al Hasan, Mohammad & Xiong, Li (Ed.), CIKM'22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM). ISSN 978-1-4503-9236-5. p. 5102–5103. doi: 10.1145/3511808.3557512. Show summary

  With the development of Industry 4.0 technology, modern industries such as Bosch’s welding monitoring witnessed the rapid widespread of machine learning (ML) based data analytical applications, which in the case of welding monitoring has led to more efficient and accurate welding monitoring quality. However, industrial ML is affected by the low transparency of ML towards non-ML experts needs. The lack of understanding by domain experts of ML methods hampers the application of ML methods in industry and the reuse of developed ML pipelines, as ML methods are often developed in an ad hoc manner for specific problems. To address these challenges, we propose the concept and a system of executable Knowledge Graph (KG), which formally encode ML knowledge and solutions in KGs, which serve as common language between ML experts and nonML experts, thus facilitate their communication and increase the transparency of ML methods. We evaluated our system extensively with an industrial use case at Bosch, showing promising results.

• Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Khan, Akif Quddus; Soylu, Ahmet & Kharlamov, Evgeny (2022). Towards A Statistic Ontology for Data Analysis in Smart Manufacturing.
• Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Cheng, Gong; Jimenez-Ruiz, Ernesto & Soylu, Ahmet [Show all 7 contributors for this article] (2022). Executable Knowledge Graphs for Machine Learning: A Bosch Case of Welding Monitoring.
• Zhou, Dongzhuoran; Zhou, Baifan; Zheng, Zhuoxun; Soylu, Ahmet; Cheng, Gong & Jimenez-Ruiz, Ernesto [Show all 8 contributors for this article] (2022). Ontology Reshaping for Knowledge Graph Construction: Applied on Bosch Welding Case.
• Parnas Gulnes, Maren; Soylu, Ahmet & Roman, Dumitru (2021). Graph-based Management of Neuroscience Data: Representation, Integration and Analysis. ERCIM News. ISSN 0926-4981.
• Kratochvil, Renate; Khanamiryan, Mariya; Soylu, Ahmet & Kvålshaugen, Ragnhild (2021). Organizational context factors that stimulate digital mindsets to unfold in digital transformation. Academy of Management Proceedings. ISSN 0065-0668.
• Gutiérrez-Basulto, Víctor; Soylu, Ahmet; Roman, Dumitru; Kliegr, Tomáš & Giese, Martin (2020). Preface. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 12173, p. v–vi.
• Soylu, Ahmet & Elvesæter, Brian (2020). Enhancing Public Procurement in the European Union Through Constructing and Exploiting an Integrated Knowledge Graph.
• Sahin, Bekir & Soylu, Ahmet (2020). An Automatic Anomaly Detection System (AADS) for Fully Autonomous Ships. ERCIM News. ISSN 0926-4981. 122.
• Turk, Philip; Soylu, Ahmet; Simperl, Elena; Corcho, Oscar; Grobelnik, Marko & Roman, Dumitru [Show all 13 contributors for this article] (2018). TheyBuyForYou: Enabling Procurement Data Value Chains.
• Soylu, Ahmet; Giese, Martin; Jimenez-Ruiz, Ernesto; Kharlamov, Evgeny; Schlatte, Rudolf & Neuenstadt, Christian [Show all 11 contributors for this article] (2016). Ontology-based Visual Querying with OptiqueVQS: Statoil and Siemens Cases. CEUR Workshop Proceedings. ISSN 1613-0073. 1818.

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