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Dwarfs in the Rearview Mirror: How Big are they Really?

Summary: Re-implements Dwarf for OLAP workloads; reproduces key experiments but uncovers orders-of-magnitude differences from prior results. Provides missing comparisons to baseline strategies and explains when Dwarf is practically useful, toward guidelines for practitioners. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9807
Venue
VLDB
Year
2008
Pagerank
4.1905499e-05
Overall Rank
12,418 | 13.70%
DOI
-

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

Rank Cited Paper Year Venue Pagerank
123 A Decomposition Storage Model 1985 SIGMOD 0.00045235743
145 Quickly Generating Billion-Record Synthetic Databases 1994 SIGMOD 0.00041403894
456 "One Size Fits All" Database Architectures Do Not Work For DSS 1995 SIGMOD 0.00022703892
475 Bottom-Up Computation of Sparse and Iceberg CUBEs 1999 SIGMOD 0.00022238407
1,193 Fast Computation of Sparse Datacubes 1997 VLDB 0.00013412915
2,665 Dwarf: Shrinking the PetaCube 2002 SIGMOD 8.3505169e-05
4,004 QC-Trees: An Efficient Summary Structure for Semantic OLAP 2003 SIGMOD 6.5401186e-05
6,131 Data Mining with the SAP NetWeaver BI Accelerator 2006 VLDB 5.1930075e-05
7,077 The Polynomial Complexity of Fully Materialized Coalesced Cubes 2004 VLDB 4.8367109e-05
12,558 Bridging the Gap between OLAP and SQL 2005 VLDB 4.1905499e-05
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