Document Type

Conference Paper

Publication Date

2022

DOI

10.1145/3529372.3530936

Publication Title

JCDL '22: Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries

Pages

15 (1-10)

Conference Name

JCDL '22: The ACM/IEEE Joint Conference on Digital Libraries in 2022. June 20-24, 2022, Cologne, Germany

Abstract

Reusable data/code and reproducible analyses are foundational to quality research. This aspect, however, is often overlooked when designing interactive stream analysis workflows for time-series data (e.g., eye-tracking data). A mechanism to transmit informative metadata alongside data may allow such workflows to intelligently consume data, propagate metadata to downstream tasks, and thereby auto-generate reusable, reproducible analytic outputs with zero supervision. Moreover, a visual programming interface to design, develop, and execute such workflows may allow rapid prototyping for interdisciplinary research. Capitalizing on these ideas, we propose StreamingHub, a framework to build metadata propagating, interactive stream analysis workflows using visual programming. We conduct two case studies to evaluate the generalizability of our framework. Simultaneously, we use two heuristics to evaluate their computational fluidity and data growth. Results show that our framework generalizes to multiple tasks with a minimal performance overhead.

Comments

© 2022 The owners and authors.

This work is licensed under a Creative Commons Attribution International 4.0 (CC BY 4.0) License.

Original Publication Citation

Jayawardana, Y., Ashok, V. G., & Jayarathna, S. (2022). StreamingHub: Interactive stream analysis workflows. In JCDL '22: Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries, June 20-24, 2022, Cologne, Germany. (1-10) Article 15. Association for Computing Machinery. https://doi.org/10.1145/3529372.3530936

ORCID

0000-0001-5992-6818 (Jayawardana), 0000-0002-4772-1265 (Ashok), 0000-0002-4879-7309 (Jayarathna)

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