Dmitri V. Kalashnikov

56 papers receiving 1.8k citations

Peers

Dmitri V. Kalashnikov
Comparison fields: 5 of 60
  • Signal Processing 1.1k
  • Computer Networks and Communications 848
  • Artificial Intelligence 725
  • Management Science and Operations Research 562
  • Information Systems 515
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Marios Hadjieleftheriou United States
Chee-Yong Chan Singapore
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Dmitri V. Kalashnikov relative to Marios Hadjieleftheriou United States Marios Hadjieleftheriou's profile →
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Citations per year

Countries citing papers authored by Dmitri V. Kalashnikov

Since Specialization
Citations

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

Fields of papers citing papers by Dmitri V. Kalashnikov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dmitri V. Kalashnikov. 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 Dmitri V. Kalashnikov. The network helps show where Dmitri V. Kalashnikov may publish in the future.

Co-authorship network of co-authors of Dmitri V. Kalashnikov

This figure shows the co-authorship network connecting the top 25 collaborators of Dmitri V. Kalashnikov. A scholar is included among the top collaborators of Dmitri V. Kalashnikov 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 Dmitri V. Kalashnikov. Dmitri V. Kalashnikov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 2
2
The Secret Life of Wikipedia Tables.
0
3
DBChEx: Interactive Exploration of Data and Schema Change.
4
4 5
5 20
6 13
7 54
8 1
9 4
10
Exploiting Web querying for Web People Search in WePS2
14
11 22
12 35
13 23
14 12
15 275
16 27
17 16
18 21
19
Efficient evaluation of continuous range queries on moving objects
6
20 218

About Dmitri V. Kalashnikov

Dmitri V. Kalashnikov is a scholar working on Signal Processing, Management Science and Operations Research and Geography, Planning and Development, having authored 57 papers that have together received 2.0k indexed citations. Recurring topics across this work include Data Management and Algorithms (24 papers), Data Quality and Management (23 papers) and Advanced Database Systems and Queries (15 papers). The work is most often cited by research in Signal Processing (1.1k citations), Geography, Planning and Development (295 citations) and Management Science and Operations Research (562 citations). Dmitri V. Kalashnikov has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Sunil Prabhakar, Sharad Mehrotra, Reynold Cheng, Zhaoqi Chen, Susanne E. Hambrusch, Yuni Xia, Walid G. Aref, Nalini Venkatasubramanian, Divesh Srivastava and Liyan Zhang. Their work appears in journals such as IEEE Transactions on Image Processing, IEEE Transactions on Knowledge and Data Engineering and IEEE Transactions on Computers.

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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