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Optimal aggregation algorithms for middleware
Abstract
Assume that each object in a database has m grades, or scores, one for each of m attributes. For example, an object can have a color grade, that tells how red it is, and a shape grade, that tells how round it is. For each attribute, there is a sorted list, which lists each object and its grade under that attribute, sorted by grade (highest grade first). There is some monotone aggregation function, or combining rule, such as min or average, that combines the individual grades to obtain an overall grade.
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Citations
Cited by
Efficient query evaluation on probabilistic databases
Dalvi, N., & Suciu, D. (2007) · The VLDB Journal · doi:10.1007/s00778-006-0004-3 · Cited by 962
LCARS: a location-content-aware recommender system
Yin, H., Sun, Y., Cui, B., Hu, Z., & Chen, L. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487608 · Cited by 331
Worst-case optimal join algorithms: [extended abstract]
Ngo, H. Q., Porat, E., Ré, C., & Rudra, A. (2012) · Proceedings of the 31st ACM SIGMOD-SIGACT-SIGAI symposium on Principles of Database Systems · doi:10.1145/2213556.2213565 · Cited by 324
Optimal multimodal fusion for multimedia data analysis
Wu, Y., Chang, E. Y., Chang, K. C.-C., & Smith, J. R. (2004) · Proceedings of the 12th annual ACM international conference on Multimedia · doi:10.1145/1027527.1027665 · Cited by 229
Metadata
- Authors
- Fagin, R., Lotem, A., & Naor, M.
- Venue
- Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
- Publisher
- ACM
- Published
- 2001-05
- Pages
- 102-113
- OpenAlex
- W2134206624
- Cited by
- 1,086
- References
- 0
- FWCI
- 10.84
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Optimal aggregation algorithms for middleware. https://app.cochat.ai/papers/10.1145/375551.375567
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- Fagin, R., Lotem, A., & Naor, M. (2001). Optimal aggregation algorithms for middleware. Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. https://doi.org/10.1145/375551.375567
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
