Doug Burdick
Impact in
- Signal Processing top 2%
- Data Management and Algorithms
- Information Systems top 1%
- Data Mining Algorithms and Applications
Papers in
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- Advanced Database Systems and Queries 5
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- Data Management and Algorithms 4
- Co-authors
- Manuel Calimlim (2 shared papers)Johannes Gehrke (2 shared papers)Jason Flannick (1 shared paper)Shivakumar Vaithyanathan (2 shared papers)T. S. Jayram (1 shared paper)Raghu Ramakrishnan (1 shared paper)Prasad Deshpande (1 shared paper)Ken Smith (2 shared papers)
- Journals
- The VLDB Journal (1 paper)IEEE Transactions on Knowledge and Data Engineering (1 paper)PubMed (1 paper)Minds at UW (University of Wisconsin) (1 paper)
- Partner nations
- United StatesSpain
In The Last Decade
Doug Burdick
11 papers receiving 888 citations
Doug Burdick's Hit Papers
Peers
Comparison fields: 5 of 57
- Signal Processing 395
- Information Systems 685
- Computational Theory and Mathematics 465
- Artificial Intelligence 428
- Computer Networks and Communications 274
Countries citing papers authored by Doug Burdick
This map shows the geographic impact of Doug Burdick'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 Doug Burdick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Doug Burdick more than expected).
Fields of papers citing papers by Doug Burdick
This network shows the impact of papers produced by Doug Burdick. 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 Doug Burdick. The network helps show where Doug Burdick may publish in the future.
Co-authors
The 25 scholars most cited alongside Doug Burdick, 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 | MAFIA: a maximal frequent itemset algorithm for transactional databases Hit paper breakdown → | 2002 | 498 |
| 2 | 2005 | 202 | |
| 3 | 2006 | 139 | |
| 4 | 2010 | 50 | |
| 5 | OLAP over imprecise data with domain constraints | 2007 | 25 |
| 6 | 2010 | 7 | |
| 7 | Vesicular transport: implications for cell polarity. | 1996 | 6 |
| 8 | 2014 | 4 | |
| 9 | 2013 | 3 | |
| 10 | 2017 | 3 | |
| 11 | 2016 | 2 |
About Doug Burdick
Doug Burdick is a scholar working on Computer Networks and Communications, Signal Processing, Artificial Intelligence, Management Science and Operations Research and Information Systems, having authored 11 papers that have together received 939 indexed citations. Recurring topics across this work include Advanced Database Systems and Queries (5 papers), Semantic Web and Ontologies (4 papers), Data Management and Algorithms (4 papers), Data Quality and Management (3 papers), Data Mining Algorithms and Applications (2 papers), Banking stability, regulation, efficiency (1 paper), Cloud Computing and Resource Management (1 paper) and FinTech, Crowdfunding, Digital Finance (1 paper). The work is most often cited by research in Signal Processing (395 citations), Information Systems (685 citations), Computational Theory and Mathematics (465 citations), Artificial Intelligence (428 citations) and Computer Networks and Communications (274 citations). Doug Burdick has collaborated with scholars based in United States and Spain. Frequent co-authors include Manuel Calimlim, Johannes Gehrke, Jason Flannick, Shivakumar Vaithyanathan, T. S. Jayram, Raghu Ramakrishnan, Prasad Deshpande, Ken Smith, Raghu Ramakrishnan and Peter Mork. Their work appears in journals such as The VLDB Journal, IEEE Transactions on Knowledge and Data Engineering, PubMed 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.