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Ιδρυματικό Αποθετήριο [SANDBOX]
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| URI | http://purl.tuc.gr/dl/dias/E41FE882-3778-4C6D-9D73-2D48ED4F8FF1 | - |
| Αναγνωριστικό | https://doi.org/10.1561/1900000004 | - |
| Αναγνωριστικό | http://db.ucsd.edu/static/Synopses.pdf | - |
| Γλώσσα | en | - |
| Μέγεθος | 296 pages | en |
| Τίτλος | Synopses for massive data: samples, histograms, wavelets, sketches | en |
| Δημιουργός | Cormode, Graham, 1977- | en |
| Δημιουργός | Garofalakis Minos | en |
| Δημιουργός | Γαροφαλακης Μινως | el |
| Δημιουργός | Haas Peter J. | en |
| Δημιουργός | Jermaine Chris | en |
| Εκδότης | Now Publishers | en |
| Περίληψη | 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 effi- ciency, optimality, practicality, range of applicability, error bounds on query answers, and incremental maintenance. We also discuss the tradeoffs between the different synopsis types. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2015-11-30 | - |
| Ημερομηνία Δημοσίευσης | 2012 | - |
| Θεματική Κατηγορία | Histograms | en |
| Θεματική Κατηγορία | Approximate query processing | en |
| Βιβλιογραφική Αναφορά | G. Cormode, M. Garofalakis, P. Haas and C. Jermaine, "Synopses for massive data: samples, histograms, wavelets, sketches", Foundations and Trends in Databases, vol. 4, no. 1-3, pp. 1-294, 2012. doi: 10.1561/1900000004 | en |
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