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TraceHub - A Platform to Bridge the Gap between State-of-the-Art Time-Series Analytics and Datasets

Authors :
Sohini Upadhyay
Shubham Agarwal
Mayank Agarwal
Zeng Zhongshen
Zilu Tang
Christian Muise
Yasaman Khazaeni
Source :
AAAI
Publication Year :
2020
Publisher :
Association for the Advancement of Artificial Intelligence (AAAI), 2020.

Abstract

In this paper, we present TraceHub - a platform that connects new non-trivial state-of-the-art time-series analytics with datasets from different domains. Analytics owners can run their insights on new datasets in an automated setting to find insight's potential and improve it. Dataset owners can find all possible types of non-trivial insights based on latest research. We provide a plug-n-play system as a set of Dataset, Transformer pipeline, and Analytics APIs for both kinds of users. We show a usefulness measure of generated insights across various types of analytics in the system. We believe that this platform can be used to bridge the gap between time-series analytics and datasets by significantly reducing the time to find the true potential of budding time-series research and improving on it faster.

Details

ISSN :
23743468 and 21595399
Volume :
34
Database :
OpenAIRE
Journal :
Proceedings of the AAAI Conference on Artificial Intelligence
Accession number :
edsair.doi...........2ed8f9f871a558961b4305e1e1431659
Full Text :
https://doi.org/10.1609/aaai.v34i09.7087