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This graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
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A word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Gupta, P., Mhedhbi, A., & Salihoğlu, S. (2021). Columnar storage and list-based processing for graph database management systems. Proceedings of the VLDB Endowment, 14(11), 2491-2504. External link
Kankanamge, C., Sahu, S., Mhedhbi, A., Chen, J., & Salihoğlu, S. (2017, May). Graphflow: An Active Graph Database [Paper]. ACM International Conference on Management of Data (SIGMOD 2017), Chicago, Illinois, USA. External link
Mhedhbi, A., Deshpande, A., & Salihoğlu, S. (2024). Modern Techniques For Querying Graph-structured Databases. Foundations and Trends in Databases, 14(2), 72-185. External link
Mhedhbi, A., Deshpande, A., & Salihoglu, S. (2024). Modern techniques for querying graph-structured databases. Foundations and Trends in Databases, 14(2), 72-185. External link
Mhedhbi, A. (2023). GraphflowDB : scalable query processing on graph-structured relation External link
Mhedhbi, A., & Salihoğlu, S. (2022). Modern techniques for querying graph-structured relations: foundations, system implementations, and open challenges. Proceedings of the VLDB Endowment, 15(12), 3762-3765. External link
Mhedhbi, A., Gupta, P., Khaliq, S., & Salihoğlu, S. (2021, April). A+ Indexes: Tunable and Space-Efficient Adjacency Lists in Graph Database Management Systems [Paper]. 37th IEEE International Conference on Data Engineering (ICDE 2021), Chania, Greece. External link
Mhedhbi, A., Lissandrini, M., Kuiper, L., Waudby, J., & Szárnyas, G. (2021, June). LSQB: a large-scale subgraph query benchmark [Paper]. 4th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES 2021) and Network Data Analytics (NDA 2021) (11 pages). External link
Mhedhbi, A., Kankanamge, C., & Salihoğlu, S. (2021). Optimizing One-time and Continuous Subgraph Queries using Worst-case Optimal Joins. ACM Transactions on Database Systems, 46(2), 6 (45 pages). External link
Mhedhbi, A., & Salihoglu, S. (2019). Optimizing subgraph queries by combining binary and worst-case optimal joins. Proceedings of the VLDB Endowment, 12(11), 1692-1704. External link
Sahu, S., Mhedhbi, A., Salihoğlu, S., Lin, J., & Özsu, M. T. (2020). The ubiquity of large graphs and surprising challenges of graph processing: extended survey. The VLDB Journal, 29(2), 595-618. External link
Sahu, S., Mhedhbi, A., Salihoğlu, S., Lin, J., & Özsu, M. T. (2017). The ubiquity of large graphs and surprising challenges of graph processing. Proceedings of the VLDB Endowment, 11(4), 420-431. External link