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Topic · Signal Processing

Data Management and Algorithms

Data Management and Algorithms is a research topic within Signal Processing. Science Explorer counts 55k research works in it since 1950. 21.9% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the mining, analysis, and querying of GPS trajectories and moving object data. It covers topics such as trajectory clustering, skyline computation, similarity search, top-k query processing, probabilistic databases, location prediction, and semantic trajectory modeling.

  • Trajectory Data Mining
  • GPS Trajectories
  • Skyline Operator
  • Similarity Search
  • Spatial Databases
  • Top-k Query Processing
  • Probabilistic Databases
  • Location Prediction
  • Clustering Algorithms
  • Semantic Trajectories
Research works
55k
fractional, since 1950
In the world top 10%
12k
per year above
Top-10% rate
21.9%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
-27%
the tick is no change

Which countries lead Data Management and Algorithms research?

By volume, China and the United States publish the most (1.5k and 721 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 1.5k works
  2. 2 United States 721 works
  3. 3 India 351 works
  4. 4 Germany 251 works
  5. 5 France 218 works
  6. 6 Japan 158 works
  7. 7 Italy 154 works
  8. 8 United Kingdom 136 works
  9. 9 Canada 119 works
  10. 10 Brazil 99 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 40.6%United States: 19.4%India: 9.4%Germany: 6.8%6 others listed: 23.8%41%largest
China1,510 · 40.6%United States721 · 19.4%India351 · 9.4%Germany251 · 6.8%6 others listed884 · 23.8%

Shares of the rows listed above, not of the whole node.

Which institutions lead Data Management and Algorithms research?

By volume in 2022–2025, Tsinghua University publishes the most Data Management and Algorithms research, followed by Wuhan University and National University of Defense Technology.

By volume, 2022–2025

  1. 1 Tsinghua UniversityChina 33 works
  2. 2 Wuhan UniversityChina 31 works
  3. 3 National University of Defense TechnologyChina 25 works
  4. 4 Centre National de la Recherche ScientifiqueFrance 23 works
  5. 5 Harbin Institute of TechnologyChina 23 works
  6. 6 Zhejiang UniversityChina 23 works
  7. 7 Shanghai Jiao Tong UniversityChina 23 works
  8. 8 University of Electronic Science and Technology of ChinaChina 22 works
  9. 9 Chinese Academy of SciencesChina 22 works
  10. 10 Beihang UniversityChina 20 works

Who are the leading researchers in Data Management and Algorithms?

The most-cited researchers publishing on Data Management and Algorithms include Li Fei-Fei, Ion Stoica and Thomas S. Huang.

  1. 1 Li Fei-Fei United States 17k citations
  2. 2 Ion Stoica United States 9.6k citations
  3. 3 Thomas S. Huang United States 7.4k citations
  4. 4 Philip S. Yu United States 6.6k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Data Management and Algorithms research done?

The largest centres of Data Management and Algorithms research in 2022–2025 are Beijing (China), Shanghai (China), Wuhan (China) and Nanjing (China).

Largest cities, 2022–2025

  1. 1 Beijing China 315 works
  2. 2 Shanghai China 104 works
  3. 3 Wuhan China 90 works
  4. 4 Nanjing China 81 works
  5. 5 Xi'an China 65 works
  6. 6 Hangzhou China 65 works
  7. 7 Hong Kong China 64 works
  8. 8 Chengdu China 63 works
  9. 9 Paris France 63 works
  10. 10 Changsha China 57 works
See Data Management and Algorithms on the map

Where is the best place to study Data Management and Algorithms?

Among universities, judged by research, Hong Kong University of Science and Technology, Nanyang Technological University and Yunnan University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 26.36%fractional works in this node (log) →share in the world top 10% →Hong Kong University of Science and Technology: 20, 22.2%Nanyang Technological University: 18, 38.6%Yunnan University: 17, 13.8%University of Electronic Science and Technology of China: 22, 27.4%City University of Hong Kong: 9, 43.3%Wuhan University: 31, 25.8%Technical University of Munich: 19, 25.0%Aalborg University: 13, 24.8%Tsinghua University: 33, 24.4%Carnegie Mellon University: 14, 18.3%Nanyang Technologica…University of Electr…Hong Kong University…Yunnan University
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Hong Kong University of Science and TechnologyHong Kong 61.222.2%9.9×20 -46.7%
2 Nanyang Technological UniversitySingapore 58.638.6%4.4×18 -7.8%
3 Yunnan UniversityChina 55.913.8%8.7×17 +139.8%
4 University of Electronic Science and Technology of ChinaChina 52.527.4%3.8×22 +90.3%
5 City University of Hong KongHong Kong 51.243.3%3.1×9 -5.6%
6 Wuhan UniversityChina 50.925.8%4.9×31 -14.7%
7 Technical University of MunichGermany 49.225.0%4.7×19 +24.8%
8 Aalborg UniversityDenmark 48.324.8%5.0×13 +69.7%
9 Tsinghua UniversityChina 45.424.4%3.2×33 -28.4%
10 Carnegie Mellon UniversityUnited States 44.718.3%6.8×14 +29.8%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Data Management and Algorithms research growing?

Output in 2018–2022 was 27% lower than in 2013–2017, peaking in 2009. The fastest-growing topics are Data Management and Algorithms.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.