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Effective Temporal Dependence Discovery in Time Series Data

Summary: Generalizes cohort analysis to temporal dependence discovery via recurrent cohort analysis for time-series behavior. Introduces recurrent-cohort operators and tailored access methods for single-node and distributed engines; experiments show up to 1e6× speedup over DB-layer and ~100× over Spark SQL. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11792
Venue
VLDB
Year
2018
Pagerank
4.1945683e-05
Overall Rank
11,753 | 18.24%
DOI
10.14778/3204028.3204033

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
13,318 Cohort Analysis with Ease 2018 SIGMOD -
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Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

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

Rank Cited Paper Year Venue Pagerank
21 C-Store: A Column-oriented DBMS 2005 VLDB 0.00086087497
35 MonetDB/X100: Hyper-Pipelining Query Execution 2005 CIDR 0.00076197749
52 Database Architecture Optimized for the new Bottleneck: Memory Access 1999 VLDB 0.00066474881
66 Spark SQL: Relational Data Processing in Spark 2015 SIGMOD 0.00061639801
131 Integrating Compression and Execution in Column-Oriented Database Systems 2006 SIGMOD 0.0004370331
1,270 BitWeaving: Fast Scans for Main Memory Data Processing 2013 SIGMOD 0.00012926086
2,061 Optimizing Queries On Compressed Bitmaps 2000 VLDB 9.6529345e-05
2,572 Efficient Distributed Memory Management with RDMA and Caching 2018 VLDB 8.519943e-05
8,189 Cohort Query Processing 2017 VLDB 4.5642142e-05
13,318 Cohort Analysis with Ease 2018 SIGMOD -
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