Probabilistic Data Structures and Algorithms for Big Data Applications
BoD – Books on Demand, 11.02.2019 - 220 Seiten
A technical book about popular space-efficient data structures and fast algorithms that are extremely useful in modern Big Data applications. The purpose of this book is to introduce technology practitioners, including software architects and developers, as well as technology decision makers to probabilistic data structures and algorithms. Reading this book, you will get a theoretical and practical understanding of probabilistic data structures and learn about their common uses.
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approximate Big Data applications binary bit array buffer candidate buckets candidate cluster canonical bucket cardinality estimation centroid compression compute Consider corresponding bits cosine similarity Count Sketch Count–Min Sketch Counter[j Counting Bloom filter cryptographic hash cryptographic hash functions Cuckoo filter Cuckoo hashing data stream decrement deletion digest property document d3 empty false positive fingerprint formula frequencies of elements frequency estimation Hamming distance hash collisions hash functions hash table hash values HyperLogLog algorithm increment insert integers Jaccard similarity length Linear probing LogLog algorithm memory merged MinHashSig monitored element MurmurHash3 nearest neighbor nearest neighbor search number of elements p-bit permutation probabilistic data structures probability processing q-quantile QF[i quantile value Quotient filter random rank remainder requires rstart sequence signature matrix SimHash signature similarity threshold simple counter space t-digest data structure total number update vector zero