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ARCube: Supporting Ranking Aggregate Queries in Partially Materialized Data Cubes

Summary: ARCube introduces a unified partial-cube structure for ranking aggregates in partially materialized data cubes. A candidate generation/verification framework uses guiding cells with a pruning rule (prune a guiding cell, prune descendants) and a chunked, memory-bounded execution to verify many candidates; yields order-of-magnitude speedups and broader ranking-aggregate support over prior work. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h491e85b0928f330a
Venue
SIGMOD
Year
2008
Pagerank
5.2710039e-05
Overall Rank
8,814 | 40.74%
DOI
10.1145/1376616.1376627

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wu_sigmod08,
        title = {{ARCube: Supporting Ranking Aggregate Queries in Partially Materialized Data Cubes}},
        author = {Wu, Tianyi and Xin, Dong and Han, Jiawei},
        series = {{SIGMOD} '08},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1376616.1376627},
        url = {https://dl.acm.org/doi/10.1145/1376616.1376627},
        year = {2008}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
3,188 Extracting Top-K Insights from Multi-dimensional Data 2017 SIGMOD 7.5535445e-05
3,557 Promotion Analysis in Multi-Dimensional Space 2009 VLDB 7.2079402e-05
3,984 Data Canopy: Accelerating Exploratory Statistical Analysis 2017 SIGMOD 6.8741188e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 23 of 23 cited papers.

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

Rank Cited Paper Year Venue Pagerank
5 Optimal Aggregation Algorithms for Middleware [Extended Abstract] 2001 PODS 0.0010679641
11 Implementing Data Cubes Efficiently 1996 SIGMOD 0.00071084324
323 An Array-Based Algorithm for Simultaneous Multidimensional Aggregates 1997 SIGMOD 0.0002100085
396 On Saying "Enough Already!" in SQL 1997 SIGMOD 0.00019162218
403 Bottom-Up Computation of Sparse and Iceberg CUBEs 1999 SIGMOD 0.0001910396
588 Computing Iceberg Queries Efficiently 1998 VLDB 0.00015906635
838 Minimal Probing: Supporting Expensive Predicates for Top-k Queries 2002 SIGMOD 0.00013550235
942 SPARK: Top-k Keyword Query in Relational Databases 2007 SIGMOD 0.00012956137
957 Materialized View Selection for Multidimensional Datasets 1998 VLDB 0.00012861484
961 RankSQL: Query Algebra and Optimization for Relational Top-k Queries 2005 SIGMOD 0.0001282305
1,764 Caching Multidimensional Queries Using Chunks 1998 SIGMOD 9.6965217e-05
1,773 Rank-aware Query Optimization 2004 SIGMOD 9.6719067e-05
1,874 Efficient Computation of Iceberg Cubes with Complex Measures 2001 SIGMOD 9.4579757e-05
2,006 IO-Top-k: Index-access Optimized Top-k Query Processing 2006 VLDB 9.199795e-05
2,256 Dwarf: Shrinking the PetaCube 2002 SIGMOD 8.7438759e-05
2,544 Star-Cubing: Computing Iceberg Cubes by Top-Down and Bottom-Up Integration 2003 VLDB 8.3179863e-05
2,561 Answering Top-k Queries Using Views 2006 VLDB 8.2977193e-05
3,460 Quotient Cube: How to Summarize the Semantics of a Data Cube 2002 VLDB 7.2845441e-05
3,782 Supporting Ad-hoc Ranking Aggregates 2006 SIGMOD 7.0230959e-05
3,841 High-Dimensional OLAP: A Minimal Cubing Approach 2004 VLDB 6.9904505e-05
4,408 Efficiently Answering Top-k Typicality Queries on Large Databases 2007 VLDB 6.6122752e-05
4,648 Answering Top-k Queries with Multi-Dimensional Selections: The Ranking Cube Approach 2006 VLDB 6.4867043e-05
4,705 Context-Sensitive Ranking 2006 SIGMOD 6.4591302e-05
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