Shaul Dar
Impact in
- Signal Processing top 2%
- Data Management and Algorithms
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- Advanced Database Systems and Queries
- Caching and Content Delivery
- Distributed systems and fault tolerance
- Advanced Data Storage Technologies
- Peer-to-Peer Network Technologies
Papers in
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- Advanced Database Systems and Queries 6
- Distributed systems and fault tolerance 5
- Distributed and Parallel Computing Systems 1
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- Data Management and Algorithms 5
- Co-authors
- Divesh Srivastava (2 shared papers)Michael Tan (1 shared paper)Björn Þór Jónsson (1 shared paper)Michael J. Franklin (1 shared paper)H. V. Jagadish (2 shared papers)Alon Y. Levy (1 shared paper)Rakesh Agrawal (1 shared paper)Raghu Ramakrishnan (2 shared papers)
- Journals
- ACM SIGMOD Record (1 paper)ACM Transactions on Database Systems (1 paper)Very Large Data Bases (3 papers)Minds at UW (University of Wisconsin) (1 paper)
- Partner nations
- United States
In The Last Decade
Shaul Dar
8 papers receiving 693 citations
Peers
Comparison fields: 5 of 32
- Signal Processing 446
- Computer Networks and Communications 719
- Information Systems 214
- Artificial Intelligence 240
- Computer Vision and Pattern Recognition 77
Countries citing papers authored by Shaul Dar
This map shows the geographic impact of Shaul Dar'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 Shaul Dar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shaul Dar more than expected).
Fields of papers citing papers by Shaul Dar
This network shows the impact of papers produced by Shaul Dar. 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 Shaul Dar. The network helps show where Shaul Dar may publish in the future.
Co-authors
The 8 scholars most cited alongside Shaul Dar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Semantic Data Caching and Replacement | 1996 | 405 |
| 2 | Answering Queries with Aggregation Using Views | 1996 | 200 |
| 3 | 1990 | 84 | |
| 4 | DTL's DataSpot: Database Exploration Using Plain Language | 1998 | 39 |
| 5 | 1994 | 23 | |
| 6 | 1998 | 14 | |
| 7 | 1994 | 12 | |
| 8 | Augmenting databases with generalized transitive closure | 1993 | 5 |
| 9 | 2004 | 0 |
About Shaul Dar
Shaul Dar is a scholar working on Computer Networks and Communications, Signal Processing, Artificial Intelligence, Information Systems and Computational Theory and Mathematics, having authored 9 papers that have together received 782 indexed citations. Recurring topics across this work include Advanced Database Systems and Queries (6 papers), Data Management and Algorithms (5 papers), Distributed systems and fault tolerance (5 papers), Semantic Web and Ontologies (3 papers), Web Data Mining and Analysis (1 paper), Complexity and Algorithms in Graphs (1 paper), Cloud Computing and Resource Management (1 paper) and Distributed and Parallel Computing Systems (1 paper). The work is most often cited by research in Signal Processing (446 citations), Computer Networks and Communications (719 citations), Information Systems (214 citations), Artificial Intelligence (240 citations) and Computer Vision and Pattern Recognition (77 citations). Shaul Dar has collaborated with scholars based in United States. Frequent co-authors include Divesh Srivastava, Michael Tan, Björn Þór Jónsson, Michael J. Franklin, H. V. Jagadish, Alon Y. Levy, Rakesh Agrawal and Raghu Ramakrishnan. Their work appears in journals such as ACM SIGMOD Record, ACM Transactions on Database Systems, Very Large Data Bases and Minds at UW (University of Wisconsin).
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.