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Proof of work based on SHA256 and Bloom filter.Timestamp MUST be equal to number of milliseconds since 1970-01-01T00:00:00.000Z in UTC time.

https://github.com/indutny/proof-of-work#readme Dependencies:

hash.js : ^1.1.0

minimalistic-assert : ^1.0.0

Tags | bloom-filter proof of work sha256 bloom |

Implementation | Javascript |

License | MIT |

Platform | OS-Independent |

A Bloom filter is a representation of a set of n items, where the main requirement is to make membership queries; i.e., whether an item is a member of a set.A Bloom filter has two parameters: m, a maximum size (typically a reasonably large multiple of the cardinality of the set to represent) and k, the number of hashing functions on elements of the set. (The actual hashing functions are important, too, but this is not a parameter for this implementation). A Bloom filter is backed by a BitSet; a key is represented in the filter by setting the bits at each value of the hashing functions (modulo m). Set membership is done by testing whether the bits at each value of the hashing functions (again, modulo m) are set. If so, the item is in the set. If the item is actually in the set, a Bloom filter will never fail (the true positive rate is 1.0); but it is susceptible to false positives. The art is to choose k and m correctly.

bloom bloom-filters bloom-filter data-structure collections go-collectionBoom Filters are probabilistic data structures for processing continuous, unbounded streams. This includes Stable Bloom Filters, Scalable Bloom Filters, Counting Bloom Filters, Inverse Bloom Filters, Cuckoo Filters, several variants of traditional Bloom filters, HyperLogLog, Count-Min Sketch, and MinHash.Classic Bloom filters generally require a priori knowledge of the data set in order to allocate an appropriately sized bit array. This works well for offline processing, but online processing typically involves unbounded data streams. With enough data, a traditional Bloom filter "fills up", after which it has a false-positive probability of 1.

bloom-filter stable-bloom-filters cuckoo-filter probabilistic-programming counting-bloom-filters scalable-bloom-filters count-min-sketch data-stream filter data-structure collections go-collectionCuckoo Cycle is the first graph-theoretic proof-of-work, and the most memory bound, yet with instant verification. Unlike Hashcash, Cuckoo Cycle is immune from quantum speedup by Grover's search algorithm. With a 42-line complete specification, Cuckoo Cycle is less than half the size of either SHA256, Blake2b, or SHA3 (Keccak) as used in Bitcoin, Equihash and ethash. Simplicity matters.

cuckoo-cycle bounties proof-of-work graph solverCuckoo filter is a Bloom filter replacement for approximated set-membership queries. Cuckoo filters support adding and removing items dynamically while achieving even higher performance than Bloom filters. For applications that store many items and target moderately low false positive rates, cuckoo filters have lower space overhead than space-optimized Bloom filters. Some possible use-cases that depend on approximated set-membership queries would be databases, caches, routers, and storage systems where it is used to decide if a given item is in a (usually large) set, with some small false positive probability. Alternatively, given it is designed to be a viable replacement to Bloom filters, it can also be used to reduce the space required in probabilistic routing tables, speed longest-prefix matching for IP addresses, improve network state management and monitoring, and encode multicast forwarding information in packets, among many other applications. Cuckoo filters provide the flexibility to add and remove items dynamically. A cuckoo filter is based on cuckoo hashing (and therefore named as cuckoo filter). It is essentially a cuckoo hash table storing each key's fingerprint. Cuckoo hash tables can be highly compact, thus a cuckoo filter could use less space than conventional Bloom filters, for applications that require low false positive rates (< 3%).

cuckoo-filter bloom-filter filterP. Almeida, C.Baquero, N. Preguiça, D. Hutchison, Scalable Bloom Filters, (GLOBECOM 2007), IEEE, 2007. Bloom filters are great if you understand what amount of bits you need to set aside early to store your entire set. Scalable Bloom Filters allow your bloom filter bits to grow as a function of false positive probability and size.

"A Bloom filter is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set. False positive matches are possible, but false negatives are not. In other words, a query returns either "possibly in set" or "definitely not in set". Elements can be added to the set, but not removed," says Wikipedia. Warning: These are synthetic benchmarks in isolated environment. Usually the difference in throughput and latency is bigger in production system because it will stress the GC, lead to slow allocation paths and higher latencies, trigger the GC, etc.

bloom-filter probabilistic high-performance datastructuresA Bloom filter is a data structure that may report it contains an item that it does not (a false positive), but is guaranteed to report correctly if it contains the item ("no false negatives"). The opposite of a Bloom filter is a data structure that may report a false negative, but can never report a false positive. That is, it may claim that it has not seen an item when it has, but will never claim to have seen an item it has not.This repository contains thread-safe implementations of "the opposite of a Bloom filter" in Java and Go.

Cuckoo filter is a Bloom filter replacement for approximated set-membership queries. While Bloom filters are well-known space-efficient data structures to serve queries like "if item x is in a set?", they do not support deletion. Their variances to enable deletion (like counting Bloom filters) usually require much more space. Cuckoo ﬁlters provide the ﬂexibility to add and remove items dynamically. A cuckoo filter is based on cuckoo hashing (and therefore named as cuckoo filter). It is essentially a cuckoo hash table storing each key's fingerprint. Cuckoo hash tables can be highly compact, thus a cuckoo filter could use less space than conventional Bloom ﬁlters, for applications that require low false positive rates (< 3%).

inbloom - a cross language Bloom filter implementation (https://en.wikipedia.org/wiki/Bloom_filter). A Bloom filter is a probabalistic data structure which provides an extremely space-efficient method of representing large sets. It can have false positives but never false negatives which means a query returns either "possibly in set" or "definitely not in set".

C++ Bloom Filter Library

algorithm associative bloom bloom-filter bloomier boostThe goal of pybloomfiltermmap is simple: to provide a fast, simple, scalable, correct library for Bloom Filters in Python.

bloom-filterFast bloom filter in JavaScript.

bloom-filter probabilistic-data-structureNote: this project has been mostly unmaintained for a while. This project aims to demonstrate a novel Bloom filter implementation that can scale, and provide not only the addition of new members, but reliable removal of existing members.

Redisson - distributed Java objects and services (Set, Multimap, SortedSet, Map, List, Queue, BlockingQueue, Deque, BlockingDeque, Semaphore, Lock, AtomicLong, Map Reduce, Publish / Subscribe, Bloom filter, Spring Cache, Executor service, Tomcat Session Manager, Scheduler service, JCache API) on top of Redis server. Rich Redis client.

cache distributed-caching distributed-locks redis-client redis-cluster collections java-collections hashmap set queueBloomd is a high-performance C server which is used to expose bloom filters and operations over them to networked clients. It uses a simple ASCII protocol which is human readable, and similar to memcached. Bloom filters are a type of sketching or approximate data structure. They trade exactness for efficiency of representation. Bloom filters provide a set-like abstraction, with the important caveat that they may contain false-positives, meaning they may claim an element is part of the set when it was never in fact added. The rate of false positives can be tuned to meet application demands, but reducing the error rate rapidly increases the amount of memory required for the representation.

Dogecoin is a cryptocurrency like Bitcoin, although it does not use SHA256 as its proof of work (POW). Taking development cues from Tenebrix and Litecoin, Dogecoin currently employs a simplified variant of scrypt. Dogecoin is released under the terms of the MIT license. See COPYING for more information or see http://opensource.org/licenses/MIT.

Bloom is a great rule-based development framework for websites, web applications and CMSs. Bloom (web-loom) rules promote consistent look and feel and integration of HTML, CSS, JavaScript, data model, etc. Bloom applications are single HTML pages (SPADE).

A stand-alone Bloom filter implementation written in Java

JavaScript bloom filter using FNV for fast hashing

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