Blar i ODA Open Digital Archive på forfatter "Kharlamov, Evgeny"

Viser treff 1-17 av 17

    • ACORDAR: A Test Collection for Ad Hoc Content-Based (RDF) Dataset Retrieval 

      Lin, Tengteng; Chen, Qiaosheng; Cheng, Gong; Soylu, Ahmet; Ell, Basil; Zhao, Ruoqi; Shi, Qing; Wang, Xiaxia; Gu, Yu; Kharlamov, Evgeny (IR: Research and Development in Information Retrieval;SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Conference object, 2022)
      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 ...

    • Big Data Pipelines on the Computing Continuum: Ecosystem and Use Cases Overview 

      Roman, Dumitru; Nikolov, Nikolay; Soylu, Ahmet; Elvesæter, Brian; Song, Hui; Prodan, Radu; Kimovski, Dragi; Marrella, Andrea; Leotta, Francesco; Matskin, Mihhail; Ledakis, Giannis; Theodosiou, Konstantinos; Simonet-Boulogne, Anthony; Perales, Fernando; Kharlamov, Evgeny; Ulisses, Alexandre; Solberg, Arnor; Ceccarelli, Raffaele (Proceedings of the IEEE Symposium on Computers and Communications;2021 IEEE Symposium on Computers and Communications (ISCC), Peer reviewed; Journal article, 2021-12-15)
      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 ...

    • Big Data Pipelines on the Computing Continuum: Tapping the Dark Data 

      Roman, Dumitru; Prodan, Radu; Nikolov, Nikolay; Soylu, Ahmet; Matskin, Mihhail; Marrella, Andrea; Kimovski, Dragi; Elvesæter, Brian; Simonet-Boulogne, Anthony; Ledakis, Giannis; Song, Hui; Leotta, Francesco; Kharlamov, Evgeny (Computer;Volume: 55, Issue: 11, Peer reviewed; Journal article, 2022-10-25)
      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 ...

    • DataCloud: Enabling the Big Data Pipelines on the Computing Continuum 

      Roman, Dumitru; Nikolov, Nikolay; Elvesæter, Brian; Soylu, Ahmet; Prodan, Radu; Kimovski, Dragi; Marrella, Andrea; Leotta, Francesco; Benvenuti, Dario; Matskin, Mihhail; Ledakis, Giannis; Simonet-Boulogne, Anthony; Perales, Fernando; Kharlamov, Evgeny; Ulisses, Alexandre; Solberg, Arnor; Ceccarelli, Raffaele (Lecture Notes in Business Information Processing;Volume 415, Conference object, 2021-05)
      With the recent developments of Internet of Things (IoT) and cloud-based technologies, massive amounts of data are generated by heterogeneous sources and stored through dedicated cloud solutions. Often organizations generate ...

    • Datalog with External Machine Learning Functions for Automated Cloud Resource Configuration 

      Zheng, Zhuoxun; Savkovic, Ognjen; Phuc Luu, Huu; Soylu, Ahmet; Kharlamov, Evgeny; Zhou, Baifan (Peer reviewed; Journal article, 2023)
      Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges. In that context, distributed computing solutions such as cloud systems are ...

    • Enhancing Knowledge Graph Generation with Ontology Reshaping – Bosch Case 

      Zhou, Dongzhuoran; Zhou, Baifan; Zheng, Zhuoxun; Kostylev, Egor V.; Cheng, Gong; Jimenez-Ruiz, Ernesto; Soylu, Ahmet; Kharlamov, Evgeny (Lecture Notes in Computer Science (LNCS);Volume 13384, Peer reviewed; Journal article, 2022-07-20)
      In the context of Industry 4.0 and Internet of Things (IoT), modern manufacturing and production lines are equipped with software systems and sensors that constantly collect and send a high volume of data.

    • Executable Knowledge Graph for Transparent Machine Learning in Welding Monitoring at Bosch 

      Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Soylu, Ahmet; Kharlamov, Evgeny (Chapter; Peer reviewed; Conference object, 2022)
      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 ...

    • ExeKG: Executable Knowledge Graph System for User-friendly Data Analytics 

      Zhuoxun, Zheng; Zhou, Baifan; Zhou, Dongzhuoran; Soylu, Ahmet; Kharlamov, Evgeny (CIKM: Conference on Information and Knowledge Management;, Chapter; Peer reviewed; Conference object, 2022)
      Data analytics including machine learning (ML) is essential to extract insights from production data in modern industries. However, industrial ML is affected by: the low transparency of ML towards non-ML experts; poor and ...

    • Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring 

      Tan, Zhipeng; Zheng, Zhuoxun; Klironomos, Antonis; Gad-Elrab, Mohamed H.; Xiao, Guohui; Soylu, Ahmet; Kharlamov, Evgeny; Zhou, Baifan (Peer reviewed; Journal article, 2023)
      Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathematical mappings via machine learning (ML). ...

    • Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case 

      Tan, Zhipeng; Zhou, Baifan; Zheng, Zhuoxun; Savkovic, Ognjen; Huang, Ziqiang; Gonzalez, Irlan-Grangel; Soylu, Ahmet; Kharlamov, Evgeny (Lecture Notes in Computer Science (LNCS);, Chapter; Peer reviewed; Conference object; Journal article, 2023)
      Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathematical mappings via machine learning (ML). ...

    • Query-Based Industrial Analytics over Knowledge Graphs with Ontology Reshaping 

      Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Cheng, Gong; Jimenez-Ruiz, Ernesto; Soylu, Ahmet; Kharlamov, Evgeny (Lecture Notes in Computer Science (LNCS);Volume 13384, Peer reviewed; Journal article, 2022-07-20)
      Industrial analytics that includes among others equipment diagnosis and anomaly detection heavily relies on integration of heterogeneous production data. Knowledge Graphs (KGs) as the data format and ontologies as the ...

    • Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case 

      Zhou, Baifan; Nikolov, Nikolay Vladimirov; Zheng, Zhuoxun; Luo, Xianghui; Savkovic, Ognjen; Roman, Dumitru; Soylu, Ahmet; Kharlamov, Evgeny (Chapter; Peer reviewed; Conference object; Journal article, 2023)
      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 ...

    • ScheRe: Schema Reshaping for Enhancing Knowledge Graph Construction 

      Zhou, Dongzhuoran; Zhou, Baifan; Zheng, Zhuoxun; Soylu, Ahmet; Savkovic, Ognjen; Kostylev, Egor; Kharlamov, Evgeny (Chapter; Peer reviewed; Conference object, 2022)
      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 ...

    • Semantic Cloud System for Scaling Data Science Solutions for Welding at Bosch 

      Zheng, Zhuoxun; Zhou, Baifan; Tan, Zhipeng; Savkovic, Ognjen; Rincon-Yanez, Diego; Nikolov, Nikolay Vladimirov; Roman, Dumitru; Soylu, Ahmet; Kharlamov, Evgeny (Peer reviewed; Journal article, 2023)
      Background and Challenges. Industry 4.0 focuses on smart factories that rely on IoT tech- nology for automation. This produces massive amounts of production data, increasing the demand for data-driven solutions and cloud ...

    • Semantic Modeling, Development and Evaluation for the Resistance Spot Welding Industry 

      Yahya, Muhammad; Zhou, Baifan; Breslin, John G.; Ali, Muhammad Intizar; Kharlamov, Evgeny (Peer reviewed; Journal article, 2023)
      The ongoing industrial revolution termed Industry 4.0 (I4.0) has borne witness to a series of profound changes towards increasing smart automation, particularly in the industrial sectors of automotive, aerospace, manufacturing, ...

    • SemML: Facilitating development of ML models for condition monitoring with semantics 

      Zhou, Baifan; Svetashova, Yulia; Silva Gusmao, Andre; Soylu, Ahmet; Cheng, Gong; Miku, Ralf; Waaler, Arild Torolv Søetorp; Kharlamov, Evgeny (Journal of Web Semantics;Volume 71, November 2021, 100664, Peer reviewed; Journal article, 2021-10-22)
      Monitoring of the state, performance, quality of operations and other parameters of equipment and production processes, which is typically referred to as condition monitoring, is an important common practice in many ...

    • Towards a Visualisation Ontology for Data Analysis in Industrial Applications 

      Zheng, Zhuoxun; Zhou, Baifan; Soylu, Ahmet; Kharlamov, Evgeny (Peer reviewed; Journal article, 2022)