The DOI matches its Crossref record; 126 of the 126 references it cites were found in a registry or an index.
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
Abstract
Methods for Approximate Query Processing (AQP) are essential for dealing with massive data. They are often the only means of providing interactive response times when exploring massive datasets, and are also needed to handle high speed data streams. These methods proceed by computing a lossy, compact synopsis of the data, and then executing the query of interest against the synopsis rather than the entire dataset. We describe basic principles and recent developments in AQP. We focus on four key synopses: random samples, histograms, wavelets, and sketches. We consider issues such as accuracy, space and time efficiency, optimality, practicality, range of applicability, error bounds on query answers, and incremental maintenance. We also discuss the tradeoffs between the different synopsis types.
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Cites
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BlinkDB: queries with bounded errors and bounded response times on very large data
Agarwal, S., Mozafari, B., Panda, A., Milner, H., Madden, S., & Stoica, I. (2013) · Proceedings of the 8th ACM European Conference on Computer Systems · doi:10.1145/2465351.2465355 · Cited by 694
VideoEdge: Processing Camera Streams using Hierarchical Clusters
Hung, C.-C., Ananthanarayanan, G., Bodik, P., Golubchik, L., Yu, M., Bahl, P., & Philipose, M. (2018) · 2018 IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec.2018.00016 · Cited by 287
Profiling relational data: a survey
Abedjan, Z., Golab, L., & Naumann, F. (2015) · The VLDB Journal · doi:10.1007/s00778-015-0389-y · Cited by 283
A survey of data partitioning and sampling methods to support big data analysis
Mahmud, M. S., Huang, J. Z., Salloum, S., Emara, T. Z., & Sadatdiynov, K. (2020) · Big Data Mining and Analytics · doi:10.26599/bdma.2019.9020015 · Cited by 282
Yang, Z., Moczulski, M., Denil, M., De Freitas, N., Song, L., & Wang, Z. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.173 · Cited by 215
Metadata
- Authors
- Cormode, G., Garofalakis, M., Haas, P. J., & Jermaine, C.
- Venue
- Foundations and Trends in Databases
- Publisher
- Emerald
- Published
- 2011-12-31
- Volume
- 4
- Issue
- 1-3
- Pages
- 1-294
- ISSN
- 1931-7883 (ISSN-L)
- 1931-7891
- OpenAlex
- W2022858489
- Cited by
- 468
- References
- 126
- FWCI
- 15.61
- Type
- journal article
- Language
- en
- Open access
- closed
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