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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
9806
Venue
VLDB
Year
2008
Pagerank
4.1945683e-05
Overall Rank
12,409 | 13.68%
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 0.00045255007
145 Quickly Generating Billion-Record Synthetic Databases 1994 SIGMOD 0.0004138408
457 "One Size Fits All" Database Architectures Do Not Work For DSS 1995 SIGMOD 0.00022713791
472 Bottom-Up Computation of Sparse and Iceberg CUBEs 1999 SIGMOD 0.00022346384
1,191 Fast Computation of Sparse Datacubes 1997 VLDB 0.00013434201
2,662 Dwarf: Shrinking the PetaCube 2002 SIGMOD 8.3532302e-05
4,005 QC-Trees: An Efficient Summary Structure for Semantic OLAP 2003 SIGMOD 6.5414308e-05
6,128 Data Mining with the SAP NetWeaver BI Accelerator 2006 VLDB 5.1979556e-05
7,081 The Polynomial Complexity of Fully Materialized Coalesced Cubes 2004 VLDB 4.8413336e-05
12,549 Bridging the Gap between OLAP and SQL 2005 VLDB 4.1945683e-05
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