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Combining Databases and Signal Processing in Plato

Summary: Plato: an extensible DBMS that uses signal-processing to infer statistical models of spatiotemporal sensor measurements as noisy, incomplete samples of an underlying ground truth. Queries run over models (not raw readings), yielding strong compression, faster execution, and higher-quality results via explicit signal/noise separation. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
269
Venue
CIDR
Year
2015
Pagerank
6.3164509e-05
Overall Rank
4,257 | 70.39%
DOI
-

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
14 Online Aggregation 1997 SIGMOD 0.0010801504
140 The MADlib Analytics Library or MAD Skills, the SQL 2012 VLDB 0.00042270404
469 MauveDB: Supporting Model-based User Views in Database Systems 2006 SIGMOD 0.00022406923
477 Model-Driven Data Acquisition in Sensor Networks 2004 VLDB 0.00022221803
1,569 Querying Continuous Functions in a Database System 2008 SIGMOD 0.0001132337
2,118 Using Probabilistic Models for Data Management in Acquisitional Environments 2005 CIDR 9.5100739e-05
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