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Indexing for Interactive Exploration of Big Data Series
Summary: Adaptive indexing for big data series builds index parts on demand for regions touched by queries. Enables immediate exploration and large latency reductions, answering ~3×10^5 queries before conventional up-front indexing finishes indexing 1B series.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h3542d8b3f0aa3560
Venue
SIGMOD
Year
2014
Pagerank
6.3264608e-05
Overall Rank
4,986 | 66.48%
DOI
10.1145/2588555.2610498
Incoming Non-self Citations Over Time
BibTeX Citation
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@inproceedings{zoumpatianos_sigmod14,
title = {{Indexing for Interactive Exploration of Big Data Series}},
author = {Zoumpatianos, Kostas and Idreos, Stratos and Palpanas, Themis},
series = {{SIGMOD} '14},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2588555.2610498},
url = {https://dl.acm.org/doi/10.1145/2588555.2610498},
year = {2014}
}
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Showing 17 of 17 cited papers.
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Cited Paper
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Pagerank
2
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1984
SIGMOD
0.001992968
42
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1994
SIGMOD
0.00045773967
90
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1996
VLDB
0.00034860244
253
Database Cracking
2007
CIDR
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478
Fast Time Sequence Indexing for Arbitrary Lp Norms
2000
VLDB
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1997
SIGMOD
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2004
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2013
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2003
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