Douglas Burdick

597 citations
19 papers · 345 · h-index 10

Impact in

Papers in

Douglas Burdick

18 papers receiving 318 citations

Peers

Douglas Burdick
Comparison fields: 5 of 48
  • Management Science and Operations Research 106
  • Computational Mathematics 4
  • Information Systems 134
  • Artificial Intelligence 169
  • Information Systems and Management 33
Replace Florian Schoppmann with:
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Tim Mattson United States
Kee Siong Ng Australia
Hannes Mühleisen Netherlands
Zoi Kaoudi Germany
Abdul Quamar United States
Arvid Heise Germany
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Citations per field
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Citations per year

Countries citing papers authored by Douglas Burdick

Since Specialization
Citations

This map shows the geographic impact of Douglas 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 Douglas Burdick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Douglas Burdick more than expected).

Fields of papers citing papers by Douglas Burdick

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Douglas 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 Douglas Burdick. The network helps show where Douglas Burdick may publish in the future.

Co-authors

The 25 scholars most cited alongside Douglas Burdick, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Douglas Burdick Line = papers co-authored together Douglas Burdick links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 201461
2 202156
3
DBLife: A Community Information Management Platform for the Database Research Community (Demonstration)
200754
4
Extracting, Linking and Integrating Data from Public Sources: A Financial Case Study.
201137
5
SystemML's Optimizer: Plan Generation for Large-Scale Machine Learning Programs.
201428
6 201622
7
MAFIA: A Performance Study of Mining Maximal Frequent Itemsets.
200320
8 201514
9 201511
10 20209
11 20146
12 20175
13 20185
14 20155
15 20194
16 20194
17 20172
18 20161
19 20191

About Douglas Burdick

Douglas Burdick is a scholar working on Management Science and Operations Research, Information Systems, Computer Networks and Communications, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 19 papers that have together received 345 indexed citations. Recurring topics across this work include Data Quality and Management (11 papers), Advanced Database Systems and Queries (7 papers), Data Mining Algorithms and Applications (4 papers), Semantic Web and Ontologies (4 papers), Web Data Mining and Analysis (2 papers), Cloud Computing and Resource Management (2 papers), Graph Theory and Algorithms (2 papers) and Big Data and Business Intelligence (2 papers). The work is most often cited by research in Management Science and Operations Research (106 citations), Computational Mathematics (4 citations), Information Systems (134 citations), Artificial Intelligence (169 citations) and Information Systems and Management (33 citations). Douglas Burdick has collaborated with scholars based in United States, Hungary and Germany. Frequent co-authors include Lucian Popa, Shivakumar Vaithyanathan, Berthold Reinwald, Prithviraj Sen, Yuanyuan Tian, Shirish Tatikonda, Matthias Böehm, Warren Shen, Pedro DeRose and Fei Chen. Their work appears in journals such as Proceedings of the VLDB Endowment, ACM Transactions on Database Systems, Journal of Computer and System Sciences, Conference on Innovative Data Systems Research and SSRN Electronic Journal.

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.

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