@article{Agarwal_Muise_Agarwal_Upadhyay_Tang_Zeng_Khazaeni_2020, title={TraceHub - A Platform to Bridge the Gap between State-of-the-Art Time-Series Analytics and Datasets}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/7087}, DOI={10.1609/aaai.v34i09.7087}, abstractNote={<p>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 <em>Dataset, Transformer pipeline</em>, and <em>Analytics APIs</em> 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.</p>}, number={09}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Agarwal, Shubham and Muise, Christian and Agarwal, Mayank and Upadhyay, Sohini and Tang, Zilu and Zeng, Zhongshen and Khazaeni, Yasaman}, year={2020}, month={Apr.}, pages={13600-13601} }