Introduces algorithms for data summarization including simple sums, counts, Bloom filters, sketches, and quantiles for versatile data analysis
Explains the mathematical foundations and behavior of each algorithm to support deep understanding
Provides detailed pseudocode and practical examples for key algorithms used in major tech companies
Covers specific data types such as geometric data, graphs, vectors, and matrices for specialized applications
Designed for both practitioners and students seeking efficient data summarization solutions
Summarized by Shop
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 comprehensi