Small summaries for big data:
The 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...
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| Main Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cambridge
Cambridge University Press
2020
|
| Subjects: | |
| Online Access: | BSB01 EUV01 FHN01 Volltext |
| Summary: | The 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 |
| Physical Description: | 1 Online-Ressource (viii, 270 Seiten) |
| ISBN: | 9781108769938 |
| DOI: | 10.1017/9781108769938 |
Staff View
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| isbn | 9781108769938 |
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| spelling | Cormode, Graham 1977- Verfasser (DE-588)140958916 aut Small summaries for big data Graham Cormode, University of Warwick, Ke Yi, Hong Kong University of Science and Technology Cambridge Cambridge University Press 2020 1 Online-Ressource (viii, 270 Seiten) txt rdacontent c rdamedia cr rdacarrier The 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 Big data Big Data (DE-588)4802620-7 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Big Data (DE-588)4802620-7 s Datenanalyse (DE-588)4123037-1 s DE-604 Yi, Ke 1979- Verfasser (DE-588)1221683055 aut Erscheint auch als Druck-Ausgabe 978-1-108-47744-4 https://doi.org/10.1017/9781108769938 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Cormode, Graham 1977- Yi, Ke 1979- Small summaries for big data Big data Big Data (DE-588)4802620-7 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4802620-7 (DE-588)4123037-1 |
| title | Small summaries for big data |
| title_auth | Small summaries for big data |
| title_exact_search | Small summaries for big data |
| title_exact_search_txtP | Small summaries for big data |
| title_full | Small summaries for big data Graham Cormode, University of Warwick, Ke Yi, Hong Kong University of Science and Technology |
| title_fullStr | Small summaries for big data Graham Cormode, University of Warwick, Ke Yi, Hong Kong University of Science and Technology |
| title_full_unstemmed | Small summaries for big data Graham Cormode, University of Warwick, Ke Yi, Hong Kong University of Science and Technology |
| title_short | Small summaries for big data |
| title_sort | small summaries for big data |
| topic | Big data Big Data (DE-588)4802620-7 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Big data Big Data Datenanalyse |
| url | https://doi.org/10.1017/9781108769938 |
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