Identifying Representative Trends in Massive Time Series Data Sets Using Sketches
Summary: Formalizes identifying representative trends as interval-based, distance-driven patterns in massive time series. Sketch-based interval sketches from precomputed polynomial convolutions enable efficient, probabilistic detection over arbitrary windows. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Piotr Indyk
- 2. Nick Koudas
- 3. S. Muthukrishnan
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 481 | Mining Database Structure; Or, How to Build a Data Quality Browser | 2002 | SIGMOD | 0.000221538 |
| 2,429 | Optimal Multi-scale Patterns in Time Series Streams | 2006 | SIGMOD | 8.8285306e-05 |
| 2,448 | Multi-Dimensional Regression Analysis of Time-Series Data Streams | 2002 | VLDB | 8.7935134e-05 |
| 3,123 | Comparing Data Streams Using Hamming Norms (How to Zero In) | 2002 | VLDB | 7.5270618e-05 |
| 4,828 | Managing Massive Time Series Streams with Multi-Scale Compressed Trickles | 2009 | VLDB | 5.8890363e-05 |
| 5,489 | Adaptive, Hands-Off Stream Mining | 2003 | VLDB | 5.4790681e-05 |
| 6,339 | A Regression-Based Temporal Pattern Mining Scheme for Data Streams | 2003 | VLDB | 5.0989397e-05 |
| 6,388 | Similarity Search and Locality Sensitive Hashing using Ternary Content Addressable Memories | 2010 | SIGMOD | 5.079988e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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|---|
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