jm + probabilistic   14

_Optimal Probabilistic Cache Stampede Prevention_ [pdf]
'When a frequently-accessed cache item expires, multiple requests
to that item can trigger a cache miss and start regenerating
that same item at the same time. This phenomenon,
known as cache stampede, severely limits the performance
of databases and web servers. A natural countermeasure to
this issue is to let the processes that perform such requests
to randomly ask for a regeneration before the expiration
time of the item. In this paper we give optimal algorithms
for performing such probabilistic early expirations. Our algorithms
are theoretically optimal and have much better
performances than other solutions used in real-world applications.'

(via Marc Brooker)
via:marcbrooker  caching  caches  algorithm  probabilistic  expiration  vldb  papers  expiry  cache-miss  stampedes 
may 2017 by jm
Fast Forward Labs: Probabilistic Data Structure Showdown: Cuckoo Filters vs. Bloom Filters
Nice comparison of a counting Bloom filter and a Cuckoo Filter, implemented in Python:
This post provides an update by exploring Cuckoo filters, a new probabilistic data structure that improves upon the standard Bloom filter. The Cuckoo filter provides a few advantages: 1) it enables dynamic deletion and addition of items 2) it can be easily implemented compared to Bloom filter variants with similar capabilities, and 3) for similar space constraints, the Cuckoo filter provides lower false positives, particularly at lower capacities. We provide a python implementation of the Cuckoo filter here, and compare it to a counting Bloom filter (a Bloom filter variant).
algorithms  probabilistic  approximation  bloom-filters  cuckoo-filters  sets  estimation  python 
november 2016 by jm
"Cuckoo Filter: Practically Better Than Bloom"
'We propose a new data structure called the cuckoo filter that can replace Bloom filters for approximate set membership
tests. 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. Our experimental results also show that cuckoo filters outperform previous data structures that extend Bloom filters to support deletions substantially in both time and space.'
algorithms  paper  bloom-filters  cuckoo-filters  cuckoo-hashing  data-structures  false-positives  big-data  probabilistic  hashing  set-membership  approximation 
march 2015 by jm
"Invertible Bloom Lookup Tables" [paper]
'We present a version of the Bloom filter data structure that supports not only the insertion, deletion, and lookup of key-value pairs, but also allows a complete listing of the pairs it contains with high probability, as long the number of key- value pairs is below a designed threshold. Our structure allows the number of key-value pairs to greatly exceed this threshold during normal operation. Exceeding the threshold simply temporarily prevents content listing and reduces the probability of a successful lookup. If entries are later deleted to return the structure below the threshold, everything again functions appropriately. We also show that simple variations of our structure are robust to certain standard errors, such as the deletion of a key without a corresponding insertion or the insertion of two distinct values for a key. The properties of our structure make it suitable for several applications, including database and networking applications that we highlight.'
iblt  bloom-filters  data-structures  performance  algorithms  coding  papers  probabilistic 
september 2014 by jm
3 Rules of thumb for Bloom Filters
I often need to do rough back-of-the-envelope reasoning about things, and I find that doing a bit of work to develop an intuition for how a new technique performs is usually worthwhile. So, here are three broad rules of thumb to remember when discussing Bloom filters down the pub:

One byte per item in the input set gives about a 2% false positive rate.

The optimal number of hash functions is about 0.7 times the number of bits per item.

3 - The number of hashes dominates performance.

But see also http://stackoverflow.com/a/9554448 , http://www.eecs.harvard.edu/~kirsch/pubs/bbbf/esa06.pdf (thanks Tony Finch!)
bloom-filters  algorithm  probabilistic  rules  reasoning  via:norman-maurer  false-positives  hashing  coding 
august 2014 by jm
Probabalistic Scraping of Plain Text Tables
a nifty hack.
Recently I have been banging my head trying to import a ton of OCR acquired data expressed in tabular form. I think I have come up with a neat approach using probabilistic reasoning combined with mixed integer programming. The method is pretty robust to all sorts of real world issues. In particular, the method leverages topological understanding of tables, encodes it declaratively into a mixed integer/linear program, and integrates weak probabilistic signals to classify the whole table in one go (at sub second speeds). This method can be used for any kind of classification where you have strong logical constraints but noisy data.


(via proggit)
scraping  tables  ocr  probabilistic  linear-programming  optimization  machine-learning  via:proggit 
september 2013 by jm
Flower Filter
'A simple time-decaying approximate membership filter' -- like a Bloom filter with time decay. See also http://eng.42go.com/flower-filter-an-update/ for some notes on the non-independence of survival probabilities, and how that imposes negligible differences in practice.
bloom-filter  algorithms  coding  probabilistic  approximate  time  decay 
july 2013 by jm
Google Translate of "Lorem ipsum"
The perils of unsupervised machine learning... here's what GTranslate reckons "lorem ipsum" translates to:
We will be sure to post a comment. Add tomato sauce, no tank or a traditional or online. Until outdoor environment, and not just any competition, reduce overall pain. Cisco Security, they set up in the throat develop the market beds of Cura; Employment silently churn-class by our union, very beginner himenaeos. Monday gate information. How long before any meaningful development. Until mandatory functional requirements to developers. But across the country in the spotlight in the notebook. The show was shot. Funny lion always feasible, innovative policies hatred assured. Information that is no corporate Japan
lorem-ipsum  boilerplate  machine-learning  translation  google  translate  probabilistic  tomato-sauce  cisco  funny 
june 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
Probabilistic Data Structures for Web Analytics and Data Mining « Highly Scalable Blog
Stream summary, count-min sketches, loglog counting, linear counters. Some nifty algorithms for probabilistic estimation of element frequencies and data-set cardinality (via proggit)
via:proggit  algorithms  probability  probabilistic  count-min  stream-summary  loglog-counting  linear-counting  estimation  big-data 
may 2012 by jm
Golomb-coded sets
'a probabilistic data structure conceptually similar to a Bloom filter, but with a more compact in-memory representation, and a slower query time.' could come in handy
gcs  bloom-filters  probabilistic  data-structures  memory  algorithms 
september 2011 by jm

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