Science Explorer Interactive view Map

Algorithms and Data Compression

Algorithms and Data Compression is a research topic within Artificial Intelligence. Science Explorer counts 37k research works in it since 1952. 18.3% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the development and optimization of algorithms for compressing and indexing text data, with applications to genomic data, string matching, suffix arrays, and entropy-based compression techniques.

  • Compression
  • Suffix Arrays
  • Text Indexing
  • Data Structures
  • String Matching
  • Genomic Data
  • Entropy
  • Hashing
  • Approximate Matching
  • Algorithms
Research works
37k
fractional, since 1952
In the world top 10%
6.8k
per year above
Top-10% rate
18.3%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
-27%
the tick is no change

Which countries lead Algorithms and Data Compression research?

By volume, the United States and China publish the most (593 and 584 works in 2022–2025).

By volume, 2022–2025

  1. 1 United States 593 works
  2. 2 China 584 works
  3. 3 India 295 works
  4. 4 Germany 171 works
  5. 5 Japan 125 works
  6. 6 France 120 works
  7. 7 United Kingdom 107 works
  8. 8 Italy 100 works
  9. 9 Canada 93 works
  10. 10 Russia 73 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.

United States: 26.2%China: 25.8%India: 13.1%Germany: 7.5%6 others listed: 27.3%26%largest
United States593 · 26.2%China584 · 25.8%India295 · 13.1%Germany171 · 7.5%6 others listed617 · 27.3%

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

Which institutions lead Algorithms and Data Compression research?

By volume in 2022–2025, Centre National de la Recherche Scientifique publishes the most Algorithms and Data Compression research, followed by Tsinghua University and University of Illinois Urbana-Champaign.

By volume, 2022–2025

  1. 1 Centre National de la Recherche Scientifique France 18 works
  2. 2 Tsinghua University China 16 works
  3. 3 University of Illinois Urbana-Champaign United States 15 works
  4. 4 Carnegie Mellon University United States 15 works
  5. 5 University of Helsinki Finland 14 works
  6. 6 Technion – Israel Institute of Technology Israel 14 works
  7. 7 University of Pisa Italy 13 works
  8. 8 National University of Defense Technology China 13 works
  9. 9 Shanghai Jiao Tong University China 13 works
  10. 10 University of Waterloo Canada 12 works

Who are the leading researchers in Algorithms and Data Compression?

The most-cited researchers publishing on Algorithms and Data Compression include T. Adye and B. Stugu.

  1. 1 T. Adye 9.3k citations
  2. 2 B. Stugu 9.3k citations

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

Where is Algorithms and Data Compression research done?

The largest centres of Algorithms and Data Compression research in 2022–2025 are Beijing (China), Shanghai (China), Tokyo (Japan) and Paris (France). Among places with at least 20 works in it, it is an unusually large share of all research in Haifa.

Largest cities, 2022–2025

  1. 1 Beijing China 127 works
  2. 2 Shanghai China 43 works
  3. 3 Tokyo Japan 42 works
  4. 4 Paris France 41 works
  5. 5 Seoul South Korea 39 works
  6. 6 Shenzhen China 37 works
  7. 7 Guangzhou China 30 works
  8. 8 Moscow Russia 30 works
  9. 9 Nanjing China 29 works
  10. 10 Chennai India 27 works

Where it is the local speciality

  1. HaifaIL · 20.8 works9.0×
← less than its size predictsmore →

Location quotient: how much more of its research is in Algorithms and Data Compression than the world average.

See Algorithms and Data Compression on the map

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

Among universities, judged by research, ETH Zurich, Carnegie Mellon University and University of Helsinki 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 15.27%fractional works in this node (log) →share in the world top 10% →ETH Zurich: 12, 32.8%Carnegie Mellon University: 15, 13.5%University of Helsinki: 14, 12.9%University of Pisa: 13, 10.6%Technion – Israel Institute of Technology: 14, 6.6%Tsinghua University: 16, 26.6%Amrita Vishwa Vidyapeetham: 11, 6.1%University of Illinois Urbana-Champaign: 15, 12.1%Czech Technical University in Prague: 10, 6.5%University of Chinese Academy of Sciences: 8, 25.0%ETH ZurichCarnegie Mellon Univ…University of HelsinkiUniversity of Pisa
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
1ETH Zurich Switzerland 63.432.8%5.6×12 -2.5%
2Carnegie Mellon University United States 61.113.5%11.5×15 +34.2%
3University of Helsinki Finland 53.312.9%7.6×14 +15.3%
4University of Pisa Italy 51.510.6%9.1×13 -1.9%
5Technion – Israel Institute of Technology Israel 51.06.6%13.1×14 -19.3%
6Tsinghua University China 49.726.6%2.6×16 -33.4%
7Amrita Vishwa Vidyapeetham India 44.46.1%6.4×11 +316.9%
8University of Illinois Urbana-Champaign United States 43.712.1%5.5×15 -19.2%
9Czech Technical University in Prague Czechia 43.76.5%13.4×10 -2.7%
10University of Chinese Academy of Sciences China 41.825.0%1.6×8 +172.2%

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 Algorithms and Data Compression research growing?

Output in 2018–2022 was 27% lower than in 2013–2017, peaking in 2002.

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.