Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
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| URI: | http://purl.tuc.gr/dl/dias/E41FE882-3778-4C6D-9D73-2D48ED4F8FF1 | ||
| Έτος | 2012 | ||
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | ||
| Άδεια Χρήσης |
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| 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 https://doi.org/10.1561/1900000004 | ||
| Εμφανίζεται στις Συλλογές |
Methods for Approximate Query Processing (AQP) are essential fordealing with massive data. They are often the only means of providinginteractive response times when exploring massive datasets, and arealso needed to handle high speed data streams. These methods proceedby computing a lossy, compact synopsis of the data, and then executingthe query of interest against the synopsis rather than the entiredataset. 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 onquery answers, and incremental maintenance. We also discuss the tradeoffsbetween the different synopsis types.
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