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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
Blog Post 1
Published:
title: ‘To be added’ date: 2012-08-14 permalink: /posts/2012/08/blog-post-1/ tags:
- cool posts
- category1
category2
publications
The future is big graphs: a community view on graph processing systems
Published in Journal of Communications of the ACM (CACM 2021) , 2021
Visionary paper towards ensuring the success of big graph processing for the next decade and beyond.
Recommended citation: Sherif Sakr, Angela Bonifati, Hannes Voigt, Alexandru Iosup, Khaled Ammar, Renzo Angles, Walid Aref, Marcelo Arenas, Maciej Besta, Peter A. Boncz, Khuzaima Daudjee, Emanuele Della Valle, Stefania Dumbrava, Olaf Hartig, Bernhard Haslhofer, Tim Hegeman, Jan Hidders, Katja Hose, Adriana Iamnitchi, Vasiliki Kalavri, Hugo Kapp, Wim Martens, M. Tamer Özsu, Eric Peukert, Stefan Plantikow, Mohamed Ragab, Matei R. Ripeanu, Semih Salihoglu, Christian Schulz, Petra Selmer, Juan F. Sequeda, Joshua Shinavier, Gábor Szárnyas, Riccardo Tommasini, Antonino Tumeo, Alexandru Uta, Ana Lucia Varbanescu, Hsiang-Yun Wu, Nikolay Yakovets, Da Yan, and Eiko Yoneki. 2021. The future is big graphs: a community view on graph processing systems. Commun. ACM 64, 9 (September 2021), 62–71. https://doi.org/10.1145/3434642 https://dl.acm.org/doi/10.1145/3434642
Bench-Ranking: A First Step Towards Prescriptive Performance Analyses For Big Data Frameworks
Published in 2021 IEEE International Conference on Big Data (Big Data), 2021
This paper aims to fill this timely research gap by proposing ranking criteria (called Bench-ranking) that provide prescriptive analytics via ranking functions.
Recommended citation: M. Ragab, F. M. Awaysheh and R. Tommasini, "Bench-Ranking: A First Step Towards Prescriptive Performance Analyses For Big Data Frameworks," 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 2021, pp. 241-251, doi: 10.1109/BigData52589.2021.9671277. https://ieeexplore.ieee.org/abstract/document/9671277
Towards Prescriptive Analyses of Querying Large Knowledge Graphs
Published in 26th International Conference on Advances in Databases and Information Systems, 2022
This paper investigates how to enable prescriptive analytics for processing large KGs via ranking functions (called “BenchRank”)
Recommended citation: Ragab, M. (2022). Towards Prescriptive Analyses of Querying Large Knowledge Graphs. In: Chiusano, S., et al. New Trends in Database and Information Systems. ADBIS 2022. Communications in Computer and Information Science, vol 1652. Springer, Cham. https://doi.org/10.1007/978-3-031-15743-1_59 https://link.springer.com/content/pdf/10.1007/978-3-031-15743-1_59.pdf?pdf=inline%20link
ESPRESSO: A Framework for Empowering Search on Decentralized Web
Published in Web Information Systems Engineering – WISE 2023, 2023
ESPRESSO framework aims to enable individuals or applications to search Solid pods at a large scale while pod owners maintain control over access to their data.
Recommended citation: Mohamed Ragab, Yury Savateev, Reza Moosaei, Thanassis Tiropanis, Alexandra Poulovassilis, Adriane Chapman, and George Roussos. 2023. ESPRESSO: A Framework for Empowering Search on Decentralized Web. In Web Information Systems Engineering – WISE 2023: 24th International Conference, Melbourne, VIC, Australia, October 25–27, 2023, Proceedings. Springer-Verlag, Berlin, Heidelberg, 360–375. https://doi.org/10.1007/978-981-99-7254-8_28 https://dl.acm.org/doi/10.1007/978-981-99-7254-8_28
talks
Big Graph Data Analytics.
Published:
Big Graph Data Analytics (Guest Lecture), as part of ”Network Science”, LTAT.02.011, Tartu University, Estonia.
Knowledge Graphs: The Story So Far
Published:
I was honored to be invided by Giza Systems sofware corporation in Egypt for a talk about Knowledge graphs!
teaching
Advanced Databases (LTAT.02.010)
Masters & Ph.D. Level Course, University of Tartu, 1900
Worked in the Advanced Databases (LTAT.02.010), University of Tartu, Tartu, Estonia
Big Data Management (LTAT.02.003)
Masters & Ph.D. Level Course, University of Tartu, 1900
Worked in the Big Data Management (LTAT.02.003), University of Tartu, Tartu, Estonia
Data Engineering (LTAT.02.007)
Masters & Ph.D. Level Course, University of Tartu, 1900
Worked in the “Data Engineering” (LTAT.02.007), University of Tartu, Tartu, Estonia