Enabling Signal Processing over Data Streams
Summary: Proposes deep integration of DSP and relational query processing for online/offline analysis of grouped sensor signals via a query language. TRILL DSP enables incremental computation and easy UDF integration, delivering up to two orders of magnitude speedups over loosely-coupled systems. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Milos Nikolic (University of Oxford)
- 2. Badrish Chandramouli (Microsoft)
- 3. Jonathan Goldstein (Microsoft)
BibTeX Citation
@inproceedings{nikolic_sigmod17,
title = {{Enabling Signal Processing over Data Streams}},
author = {Nikolic, Milos and Chandramouli, Badrish and Goldstein, Jonathan},
series = {{SIGMOD} '17},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3035918.3035935},
url = {https://dl.acm.org/doi/10.1145/3035918.3035935},
year = {2017}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,352 | Raising the Level of Abstraction for Time-State Analytics With the Timeline Framework | 2023 | CIDR | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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