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 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.

Author: Graham Cormode, Ke Yi
Publisher: Cambridge University Press
Published: 11/12/2020
Pages: 278
Binding Type: Hardcover
Weight: 1.20lbs
Size: 9.00h x 6.00w x 0.69d
ISBN13: 9781108477444
ISBN10: 1108477445
BISAC Categories:
- Computers | Database Administration & Management
- Computers | Data Science | General