Big Data Series Analytics Using TARDIS and its Exploitation in Geospatial Applications
Summary: TARDIS scales billions of data-series with iSAX-T signatures and a sigTree, reducing cardinality costs and improving proximity. Centralized and local indices support re-partitioning and GENET Big Data Series Approximate Retrieval in geospatial data. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Liang Zhang (Worcester Polytechnic Institute)
- 2. Noura Alghamdi (Worcester Polytechnic Institute)
- 3. Mohamed Y. Eltabakh (Worcester Polytechnic Institute)
- 4. Elke A. Rundensteiner (Worcester Polytechnic Institute)
BibTeX Citation
@inproceedings{zhang_sigmod20,
title = {{Big Data Series Analytics Using TARDIS and its Exploitation in Geospatial Applications}},
author = {Zhang, Liang and Alghamdi, Noura and Eltabakh, Mohamed Y. and Rundensteiner, Elke A.},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3384705},
url = {https://dl.acm.org/doi/10.1145/3318464.3384705},
year = {2020}
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