Probabilistic Data Structures and Algorithms for Big Data ApplicationsBoD – Books on Demand, 05.08.2022 - 222 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. |
Inhalt
Membership | 21 |
Bibliography | 59 |
Cardinality | 61 |
1 | 76 |
4 | 82 |
Bibliography | 93 |
Majority algorithm | 99 |
Count Sketch | 106 |
CountMin Sketch | 116 |
Bibliography | 127 |
Bibliography | 163 |
| 209 | |
| 211 | |
Häufige Begriffe und Wortgruppen
according algorithm already applications approach approximate array become binary bits blocks Bloom filter bucket buffer build calculate called candidate cardinality centroid cluster collisions compression compute Consider contains correction corresponding Count Sketch counters Counting data stream data structure dataset determine distribution documents empty equal error estimation exact Example expected final fingerprint formula frequency given hash functions hash table hash values heavy hitters HyperLogLog important increase increment Input instance integers least length Linear Majority meaning memory merged MinHash number of elements Output pairs perform permutations possible practice probabilistic probability problem processing provides quantile random range rank remainder representation represented requires result sampling sequence signature signature matrix SimHash similarity simple single sorted space t-digest t-digest data structure threshold update vector zero