On-Device Interaction Mining MHCI024

Interaction mining is a popular technique for capturing design and interaction data while a mobile app is being used. Over the years, researchers have leveraged interaction mining systems to build large repositories of interaction data, enabling novel, ML-based tools for UX practitioners, designers, and programmers. Existing interaction mining systems range from simple screen recorders — which are easy to use but capture sparse, unstructured data — to complex installations requiring dedicated hardware and custom OS forks — which yield rich, high-fidelity traces but are difficult to deploy outside of a lab environment. This paper presents ODIM, an on-device framework for mobile interaction mining that produces detailed trace metadata. The framework is reified in an Android implementation based on a simple APK that users can install on their personal devices. The paper outlines ODIM’s design principles, describes its implementation, evaluates the system on traces collected from 100 popular apps on the Google Play Store, and discusses future avenues for scaling the utility and impact of interaction mining systems. The ODIM software, source code, and online trace repository are all freely available at interactionmining.org, for anyone to use and contribute to.

Authors

Institutions

Publication Details

Journal
Proceedings of the ACM on Human-Computer Interaction
Published
2025-09-09
DOI
https://doi.org/10.1145/3743726
Primary Topic
Mobile Crowdsensing and Crowdsourcing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

On-Device Interaction Mining MHCI024

Jerry O. Talton, Ranjitha Kumar, Deniz Arsan, Eun-Gu Ji et al.
Proceedings of the ACM on Human-Computer Interaction
Mobile Crowdsensing and Crowdsourcing
article

On-Device Interaction Mining MHCI024

Jerry O. Talton, Ranjitha Kumar, Deniz Arsan, Eun-Gu Ji, Muhammad Rizky Wellyanto, Cuiting Guo
article en

Abstract

Interaction mining is a popular technique for capturing design and interaction data while a mobile app is being used. Over the years, researchers have leveraged interaction mining systems to build large repositories of interaction data, enabling novel, ML-based tools for UX practitioners, designers, and programmers. Existing interaction mining systems range from simple screen recorders — which are easy to use but capture sparse, unstructured data — to complex installations requiring dedicated hardware and custom OS forks — which yield rich, high-fidelity traces but are difficult to deploy outside of a lab environment. This paper presents ODIM, an on-device framework for mobile interaction mining that produces detailed trace metadata. The framework is reified in an Android implementation based on a simple APK that users can install on their personal devices. The paper outlines ODIM’s design principles, describes its implementation, evaluates the system on traces collected from 100 popular apps on the Google Play Store, and discusses future avenues for scaling the utility and impact of interaction mining systems. The ODIM software, source code, and online trace repository are all freely available at interactionmining.org, for anyone to use and contribute to.

Proceedings of the ACM on Human-Computer InteractionVol. 9(5)
University of Illinois Urbana-Champaign (US), Computing Center (RU)
Openalex Percentile: Top 72%
Mobile Crowdsensing and Crowdsourcing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.