Countries citing papers authored by Assaf Schuster
Since
Specialization
Citations
This map shows the geographic impact of Assaf Schuster's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Assaf Schuster with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Assaf Schuster more than expected).
This network shows the impact of papers produced by Assaf Schuster. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Assaf Schuster. The network helps show where Assaf Schuster may publish in the future.
Co-authorship network of co-authors of Assaf Schuster
This figure shows the co-authorship network connecting the top 25 collaborators of Assaf Schuster.
A scholar is included among the top collaborators of Assaf Schuster based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Assaf Schuster. Assaf Schuster is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Silberstein, Mark, et al.. (2021). Fine-tuning giant neural networks on commodity hardware with automatic pipeline model parallelism.. USENIX Annual Technical Conference. 381–396.5 indexed citations
4.
Cuzzocrea, Alfredo, Assaf Schuster, Gianni Vercelli, & Massimiliano Nolich. (2019). Privacy-Preserving OLAP-Based Monitoring of Data Streams: The PP-OMDS Approach.. CINECA IRIS Institutial Research Information System (University of Genoa).1 indexed citations
Gabel, Moshe, Kento Sato, Daniel Keren, Satoshi Matsuoka, & Assaf Schuster. (2015). Latent Fault Detection With Unbalanced Workloads. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 118–124.5 indexed citations
Gabel, Moshe, Daniel Keren, & Assaf Schuster. (2013). Communication-efficient Outlier Detection for Scale-out Systems.. Very Large Data Bases. 19–24.3 indexed citations
9.
Friedman, Arik, Ran Wolff, & Assaf Schuster. (2007). Providing k-anonymity in data mining. The VLDB Journal. 17(4). 789–804.67 indexed citations
10.
Silberstein, Mark, et al.. (2006). Materializing Highly Available Grids.. 321–323.1 indexed citations
11.
Schuster, Assaf, et al.. (2006). Grid resource management for data mining applications.1 indexed citations
Stankovski, Vlado, et al.. (2004). A Service-Centric Perspective for Data Mining in Complex Problem Solving Environments.. Parallel and Distributed Processing Techniques and Applications. 780–787.12 indexed citations
Factor, Michael, Assaf Schuster, & Konstantin Shagin. (2003). JavaSplit: A Runtime for Execution of Monolithic Java Programs on Heterogeneous Collections of Commodity Workstations. Cluster Computing. 110–117.16 indexed citations
16.
Heyman, Tamir, Daniel Geist, Orna Grümberg, & Assaf Schuster. (2000). Achieving Scalability in Parallel Reachability Analysis of Very Large Circuits.1 indexed citations
17.
Schuster, Assaf, et al.. (1999). Harnessing The Power of Fast, Low Latency, Networks for Software DSMs.2 indexed citations
Schuster, Assaf, et al.. (1997). Millipede: a user-level NT-based distributed shard memory system with thread migration and dynamic run-time optimization of memory references. 19–19.2 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.