01 / CORPUS
671 artwork or project records collected from selected top venues.
> OPEN /FIELDS/CMA.DEF
Field Definition // SSD-PEDIA KNOWLEDGE SYSTEM
Computational Media Art
A working definition for artistic practices in which computational processes participate materially in the work.
LIVE SCHEMATICCH. 01—04
Definition.
Computational Media Art refers to a public-facing artistic practice in which computational elements make a significant contribution to at least one constitutive phase of the work—inspiration, collection, operation, or exhibition—and thereby shape its form and meaning.
CMA = Artistic Practice × Significant Computational Participation × Public Accessibility
Public Accessibility means that a work enters a public cultural field where others can view, experience, participate in, or discuss it. It does not mean “public domain” in the copyright sense.
Four constitutive phases.
CMA is not defined by a single device or medium. It is identified by where computation materially enters artistic practice: as concept, as captured material, as operative process, or as audience-facing exhibition system.
01INSPIRATION — AI, databases, network systems, algorithmic logic, simulation, software systems, platform logics, computational culture, or digital archives directly shape the work’s theme, question, motive, or problem framing.02COLLECTION — cameras, sensors, motion capture, microphones, EEG or biometric capture, datasets, network scraping, digital archives, audience input, satellite imagery, or surveillance footage materially feed the work.03OPERATION — AI/ML, algorithms, software logic, coding, generative systems, simulation, rendering, data visualization, computer vision, real-time computation, procedural systems, robotic control, or model-based processing produce or transform the work.04EXHIBITION — interactive screens, multi-screen systems, immersive projection, XR/VR/AR/MR, real-time interfaces, game engines, sensor-feedback systems, network interfaces, web-based display, server/cloud systems, remote participation, robots, or live computational interfaces shape the public encounter.
NOTE: Ordinary documentation, generic technology mentions, routine video playback, ordinary projection, digital editing, or simple web archiving are not sufficient unless computation shapes the concept, material, process, or encounter of the work.
Why measure CMA as a field pattern?
This section tests whether the CMA definition appears as a visible pattern in contemporary technology-art venues, rather than as a purely theoretical category.
The corpus covers works and projects published from 2020 to 2025 across Ars Electronica Prix, SIGGRAPH Art Papers, SIGGRAPH Art Gallery, SIGGRAPH Asia Art Papers, and SIGGRAPH Asia Art Gallery.
02 / RANGE
Six annual datasets, 2020–2025, grouped by source venue and track.
03 / METHOD
Metadata and descriptions were organized into a database, then read through the CMA rubric.
04 / RULE
isCMA is true if at least one feature is present: inspiration, collection, operation, or exhibition.
METHOD NOTE: Classification is AI-assisted and rubric-based, then manually reviewed. Results remain conditioned by source metadata, abstracts, venue track differences, and the available description of each work.
CMA statistical visualization.
The figures below use ASCII data drawings instead of conventional charts. They keep year, source, and feature differences visible so the corpus is not collapsed into a single undifferentiated total.
[ FIGURE 01 ] CMA BY YEAR / SOURCE TRACK
Each row shows the number of works classified as CMA within a source track. The yearly total at right reports isCMA / all records.
LEGEND: █ ARS ▓ SIG-GALLERY ▒ SIG-PAPER ░ SA-PAPER ▫ SA-GALLERY
2025 ███████████████████████████████████████████ 47
▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ 16
▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ 17
░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 29
▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫ 16 TOTAL 125 / 127 98.4%
2024 █████████████████████████████████ 33
▓▓▓▓▓▓▓▓▓▓▓▓ 12
▒▒▒▒▒▒▒▒▒▒▒▒▒▒ 14
░░░░░░░░░░░░░░░░░░░░░░░░ 24
▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫ 18 TOTAL 101 / 106 95.3%
2023 ██████████████████████████████████████████ 42
▓▓▓▓▓▓▓▓▓▓▓▓▓ 13
▒▒▒▒▒▒▒▒▒▒▒▒▒ 13
░░░░░░░░░░░░ 12
▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫ 22 TOTAL 102 / 110 92.7%
2022 █████████████████████████████████████████ 41
▓▓▓▓▓▓▓▓▓ 09
▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ 15
░ [NO ART-PAPER TRACK]
▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫ 24 TOTAL 089 / 095 93.7%
2021 ███████████████████████████████████████████ 43
▓▓▓▓▓▓▓▓▓▓▓▓▓▓ 14
▒▒▒▒▒▒▒▒▒▒▒ 11
░ [NO ART-PAPER TRACK]
▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫ 15 TOTAL 083 / 089 93.3%
2020 ████████████████████████████████████████████ 44
▓▓▓▓▓▓▓▓▓▓▓▓ 12
▒▒▒▒▒▒▒▒▒▒▒▒▒▒ 14
░ [NO ART-PAPER TRACK]
▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫▫ 66
TOTAL 136 / 144 94.4%[ FIGURE 02 ] SINGLE FEATURE COVERAGE BY YEAR
Feature coverage is not exclusive: one work may contain several features. The pattern shows which phase of practice most often carries computation.
I = Inspiration / C = Collection / O = Operation / E = Exhibition
2025 I ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 100/127 78.7%
C ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 079/127 62.2%
O ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 120/127 94.5%
E ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 087/127 68.5%
2024 I ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 081/106 76.4%
C ☐☐☐☐☐☐☐☐☐☐☐☐☐ 061/106 57.5%
O ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 096/106 90.6%
E ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 073/106 68.9%
2023 I ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 074/110 67.3%
C ☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 065/110 59.1%
O ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 095/110 86.4%
E ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 070/110 63.6%
2022 I ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 060/095 63.2%
C ☐☐☐☐☐☐☐☐☐☐☐☐☐ 051/095 53.7%
O ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 087/095 91.6%
E ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 069/095 72.6%
2021 I ☐☐☐☐☐☐☐☐☐☐☐☐☐ 050/089 56.2%
C ☐☐☐☐☐☐☐☐☐☐☐☐ 047/089 52.8%
O ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 077/089 86.5%
E ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 061/089 68.5%
2020 I ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 102/144 70.8%
C ☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 081/144 56.2%
O ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 127/144 88.2%
E ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ 093/144 64.6%[ FIGURE 03 ] FEATURE COUNT DISTRIBUTION BY YEAR
This figure reads feature depth. A higher count means computation appears across more phases of the same work, rather than only as a single isolated device or topic.
LEGEND: · 0 features ░ 1 feature ▒ 2 features ▓ 3 features █ 4 features SCALE : one mark is approximately four artworks/projects 2025 | ·░░▒▒▒▒▒▒▓▓▓▓▓▓▓▓▓▓▓▓▓████████████ | 127 2024 | ·░▒▒▒▒▒▒▓▓▓▓▓▓▓▓▓██████████ | 106 2023 | ··░░▒▒▒▒▒▒▒▒▓▓▓▓▓▓▓██████████ | 110 2022 | ··░░▒▒▒▒▒▓▓▓▓▓▓▓█████████ | 095 2021 | ··░░▒▒▒▒▒▓▓▓▓▓▓▓▓██████ | 089 2020 | ··░░░▒▒▒▒▒▒▒▒▓▓▓▓▓▓▓▓▓▓▓████████████ | 144
SSD is an interpretive framework, not a clinical instrument. Outputs are situated readings shaped by sources, analysts, and context.
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