Continually Evaluating Similarity-Based Pattern Queries on a Streaming Time Series
Summary: Continual similarity queries for streaming time series, using FFT-based cross-correlation to batch-distance the inflow stream against database patterns across future positions. Predicts future values to precompute distances and uses arrival-time errors to prune, delivering fast responses. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Like Gao (George Mason University)
- 2. X. Sean Wang (George Mason University)
BibTeX Citation
@inproceedings{gao_sigmod02,
title = {{Continually Evaluating Similarity-Based Pattern Queries on a Streaming Time Series}},
author = {Gao, Like and Wang, X. Sean},
series = {{SIGMOD} '02},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/564691.564734},
url = {https://dl.acm.org/doi/10.1145/564691.564734},
year = {2002}
}
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,886 | Approximate NN Queries on Streams with Guaranteed Error/performance Bounds | 2004 | VLDB | 7.9957585e-05 |
| 5,162 | Online Event-driven Subsequence Matching over Financial Data Streams | 2004 | SIGMOD | 6.339805e-05 |
| 6,189 | Subsequence Matching on Structured Time Series Data | 2005 | SIGMOD | 5.9483531e-05 |
| 9,638 | Streaming Similarity Self-Join | 2016 | VLDB | 5.2434488e-05 |
| 11,416 | Correlation Joins over Time Series Data Streams Utilizing Complementary Dimension Reduction and Transformation | 2023 | SIGMOD | 5.093636e-05 |
| 12,787 | StreamMiner: A Classifier Ensemble-based Engine to Mine Concept-drifting Data Streams | 2004 | VLDB | 5.093636e-05 |
| 12,803 | AIMS: An Immersidata Management System | 2003 | CIDR | 5.093636e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 25 | NiagaraCQ: A Scalable Continuous Query System for Internet Databases | 2000 | SIGMOD | 0.00054667018 |
| 41 | Fast Subsequence Matching in Time-Series Databases | 1994 | SIGMOD | 0.00046675394 |
| 190 | Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases | 2001 | SIGMOD | 0.00026105472 |
| 368 | Continuous Queries over Append-Only Databases | 1992 | SIGMOD | 0.00019968151 |
| 371 | Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases | 1995 | VLDB | 0.00019869565 |
| 810 | Similarity-Based Queries for Time Series Data | 1997 | SIGMOD | 0.00013874464 |
| 3,252 | Fast Time-Series Searching with Scaling and Shifting | 1999 | PODS | 7.5951443e-05 |
| 8,359 | High-Dimensional Index Structures: Database Support for Next Decade's Applications | 1998 | SIGMOD | 5.4444916e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 11,416 | Correlation Joins over Time Series Data Streams Utilizing Complementary Dimension Reduction and Transformation | 2023 | SIGMOD |
| 2 | 5,162 | Online Event-driven Subsequence Matching over Financial Data Streams | 2004 | SIGMOD |
| 3 | 3,252 | Fast Time-Series Searching with Scaling and Shifting | 1999 | PODS |
| 4 | 13,318 | Fully Automated Correlated Time Series Forecasting in Minutes | 2025 | VLDB |
| 5 | 8,107 | Anticipatory DTW for Efficient Similarity Search in Time Series Databases | 2009 | VLDB |
| 6 | 668 | StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time | 2002 | VLDB |
| 7 | 5,043 | Efficient Temporal Pattern Mining in Big Time Series Using Mutual Information | 2022 | VLDB |
| 8 | 2,441 | Optimal Multi-scale Patterns in Time Series Streams | 2006 | SIGMOD |
| 9 | 810 | Similarity-Based Queries for Time Series Data | 1997 | SIGMOD |
| 10 | 1,764 | Fast Approximate Correlation for Massive Time-series Data | 2010 | SIGMOD |