Publications

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

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

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

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