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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
6.0000861e-05
Overall Rank
4,673 | 67.53%
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
2,204 Continuous Analytics Over Discontinuous Streams 2010 SIGMOD 9.2986554e-05
3,818 High-Performance Dynamic Pattern Matching over Disordered Streams 2010 VLDB 6.7279318e-05
4,789 Consistency in a Stream Warehouse 2011 CIDR 5.9144227e-05
6,180 MonetDB/DataCell: Online Analytics in a Streaming Column-Store 2012 VLDB 5.1643914e-05
6,738 Analytics for the Real-Time Web 2011 VLDB 4.9413518e-05
9,806 Railgun: managing large streaming windows under MAD requirements 2021 VLDB 4.2766788e-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
143 TelegraphCQ: Continuous Dataflow Processing for an Uncertain World 2003 CIDR 0.00041688468
1,222 Querying and Mining Data Streams: You Only Get One Look 2002 SIGMOD 0.00013205769
1,557 Resource Sharing in Continuous Sliding-Window Aggregates 2004 VLDB 0.00011381807
1,791 On-the-Fly Sharing for Streamed Aggregation 2006 SIGMOD 0.0001052731
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