jm + cardinality   11

jomsdev notes:

'Last year, in the AofA’16 conference Robert Sedgewick proposed a new algorithm for cardinality estimation. Robert Sedgwick is a professor at Princeton with a long track of publications on combinatorial/randomized algorithms. He was a good friend of Philippe Flajolet (creator of Hyperloglog) and HyperBitBit it's based on the same ideas. However, it uses less memory than Hyperloglog and can provide the same results. On practical data, HyperBitBit, for N < 2^64 estimates cardinality within 10% using only 128 + 6 bits.'
algorithms  programming  cs  hyperloglog  estimation  cardinality  counting  hyperbitbit 
march 2017 by jm
A probabilistic data structure for frequency/k-occurrence cardinality estimation of multisets. Sample implementation

(via Patrick McFadin)
via:patrickmcfadin  hyperloglog  cardinality  data-structures  algorithms  hyperlogsandwich  counting  estimation  lossy  multisets 
may 2015 by jm
a new, and interesting, sketching algorithm, with a Java implementation:
Recordinality is unique in that it provides cardinality estimation like HLL, but also offers "distinct value sampling." This means that Recordinality can allow us to fetch a random sample of distinct elements in a stream, invariant to cardinality. Put more succinctly, given a stream of elements containing 1,000,000 occurrences of 'A' and one occurrence each of 'B' - 'Z', the probability of any letter appearing in our sample is equal. Moreover, we can also efficiently store the number of times elements in our distinct sample have been observed. This can help us to understand the distribution of occurrences of elements in our stream. With it, we can answer questions like "do the elements we've sampled present in a power law-like pattern, or is the distribution of occurrences relatively even across the set?"
sketching  coding  algorithms  recordinality  cardinality  estimation  hll  hashing  murmurhash  java 
august 2013 by jm
Sketch of the Day: K-Minimum Values
Another sketching algorithm -- this one supports set union and intersection operations more easily than HyperLogLog when there are more than 2 sets
algorithms  coding  space-saving  cardinality  streams  stream-processing  estimation  sets  sketching 
june 2013 by jm
a high-performance C server which is used to expose HyperLogLog sets and operations over them to networked clients. It uses a simple ASCII protocol which is human readable, and similar to memcached.

HyperLogLog's are a relatively new sketching data structure. They are used to estimate cardinality, i.e. the unique number of items in a set. They are based on the observation that any bit in a "good" hash function is indepedenent of any other bit and that the probability of getting a string of N bits all set to the same value is 1/(2^N). There is a lot more in the math, but that is the basic intuition. What is even more incredible is that the storage required to do the counting is log(log(N)). So with a 6 bit register, we can count well into the trillions. For more information, its best to read the papers referenced at the end. TL;DR: HyperLogLogs enable you to have a set with about 1.6% variance, using 3280 bytes, and estimate sizes in the trillions.

hyper-log-log  hlld  hll  data-structures  memcached  daemons  sketching  estimation  big-data  cardinality  algorithms  via:cscotta 
june 2013 by jm
Approximate Heavy Hitters -The SpaceSaving Algorithm
nice, readable intro to SpaceSaving (which I've linked to before) -- a simple stream-processing cardinality top-K estimation algorithm with bounded error.
algorithms  coding  space-saving  cardinality  streams  stream-processing  estimation 
may 2013 by jm
HyperLogLog++: Google’s Take On Engineering HLL
Google and AggregateKnowledge's improvements to the HyperLogLog cardinality estimation algorithm
hyperloglog  cardinality  estimation  streaming  stream-processing  cep 
february 2013 by jm
clearspring / stream-lib
ASL-licensed open source library of stream-processing/approximation algorithms: count-min sketch, space-saving top-k, cardinality estimation, LogLog, HyperLogLog, MurmurHash, lookup3 hash, Bloom filters, q-digest, stochastic top-k
algorithms  coding  streams  cep  stream-processing  approximation  probabilistic  space-saving  top-k  cardinality  estimation  bloom-filters  q-digest  loglog  hyperloglog  murmurhash  lookup3 
february 2013 by jm
aaw/hyperloglog-redis - GitHub
'This gem is a pure Ruby implementation of the HyperLogLog algorithm for estimating cardinalities of sets observed via a stream of events. A Redis instance is used for storing the counters.'
cardinality  sets  redis  algorithms  ruby  gems  hyperloglog 
january 2013 by jm

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