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

Authors

  • Shubham Agarwal MIT-IBM Watson AI Lab
  • Christian Muise MIT-IBM Watson AI Lab
  • Mayank Agarwal MIT-IBM Watson AI Lab
  • Sohini Upadhyay IBM Research
  • Zilu Tang IBM Cloud and Cognitive Software
  • Zhongshen Zeng IBM Research
  • Yasaman Khazaeni IBM Research

DOI:

https://doi.org/10.1609/aaai.v34i09.7087

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.

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Published

2020-04-03

How to Cite

Agarwal, S., Muise, C., Agarwal, M., Upadhyay, S., Tang, Z., Zeng, Z., & Khazaeni, Y. (2020). TraceHub - A Platform to Bridge the Gap between State-of-the-Art Time-Series Analytics and Datasets. Proceedings of the AAAI Conference on Artificial Intelligence, 34(09), 13600-13601. https://doi.org/10.1609/aaai.v34i09.7087