000			02179nam a2200337 i 4500
001			CR9781108769938
003			UkCbUP
005			20240920173526.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			190314s2021||||enk o ||1 0|eng|d
020			_a9781108769938 (ebook)
020			_z9781108477444 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA76.9.B45 _bC67 2021
082	0	0	_a005.7 _223
100	1		_aCormode, Graham, _d1977- _eauthor.
245	1	0	_aSmall summaries for big data / _cGraham Cormode, University of Warwick, Ke Yi, Hong Kong University of Science and Technology.
264		1	_aCambridge : _bCambridge University Press, _c2021.
300			_a1 online resource (viii, 270 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 29 Oct 2020).
520			_aThe massive volume of data generated in modern applications can overwhelm our ability to conveniently transmit, store, and index it. For many scenarios, building a compact summary of a dataset that is vastly smaller enables flexibility and efficiency in a range of queries over the data, in exchange for some approximation. This comprehensive introduction to data summarization, aimed at practitioners and students, showcases the algorithms, their behavior, and the mathematical underpinnings of their operation. The coverage starts with simple sums and approximate counts, building to more advanced probabilistic structures such as the Bloom Filter, distinct value summaries, sketches, and quantile summaries. Summaries are described for specific types of data, such as geometric data, graphs, and vectors and matrices. The authors offer detailed descriptions of and pseudocode for key algorithms that have been incorporated in systems from companies such as Google, Apple, Microsoft, Netflix and Twitter.
650		0	_aBig data.
700	1		_aYi, Ke, _d1979- _eauthor.
776	0	8	_iPrint version: _z9781108477444
856	4	0	_uhttps://doi.org/10.1017/9781108769938
942			_2ddc _cEB
999			_c9841 _d9841