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Continuous Analytics: Rethinking Query Processing in a Network-Effect World

Summary: Proposes Continuous Analytics: run SQL continuously and incrementally over incoming data (before persistent storage) to meet network-effect-driven scale and low-latency needs. Integrated into a full DBMS to blend tables and streams, replacing store-first-query-later and claiming orders-of-magnitude efficiency gains. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
113
Venue
CIDR
Year
2009
Pagerank
6.0058653e-05
Overall Rank
4,673 | 67.50%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank Citing Paper Year Venue Pagerank
2,198 Continuous Analytics Over Discontinuous Streams 2010 SIGMOD 9.308495e-05
3,815 High-Performance Dynamic Pattern Matching over Disordered Streams 2010 VLDB 6.7333316e-05
4,788 Consistency in a Stream Warehouse 2011 CIDR 5.9200462e-05
6,176 MonetDB/DataCell: Online Analytics in a Streaming Column-Store 2012 VLDB 5.169318e-05
6,734 Analytics for the Real-Time Web 2011 VLDB 4.9461128e-05
9,803 Railgun: managing large streaming windows under MAD requirements 2021 VLDB 4.2807806e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank Cited Paper Year Venue Pagerank
142 TelegraphCQ: Continuous Dataflow Processing for an Uncertain World 2003 CIDR 0.00041725802
1,222 Querying and Mining Data Streams: You Only Get One Look 2002 SIGMOD 0.00013213129
1,554 Resource Sharing in Continuous Sliding-Window Aggregates 2004 VLDB 0.00011400581
1,788 On-the-Fly Sharing for Streamed Aggregation 2006 SIGMOD 0.00010555742
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