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Brief Biography: Sidney Lumet (pronounced [luˈmɛt], loo-MET; born June 25, 1924) is an Academy Award winning American film director, with over 50 films to his name, including the critically acclaimed 12 Angry Men (1957), Serpico (1973), Dog Day Afternoon (1975), Network (1976) and The Verdict (1982), all of which, except for Serpico (1973), earned him Academy Award nominations for Best Director.
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Update on the Faroo website and API service interruption. - Both Faroo website ( and API are unavailable since 26/05/2016 03:26:05. We are working to fix it. The server is up, but not reachable from internet, even if booted from a fresh rescue OS with default configuration. Therefore we suspect there is a problem with the network, network adapter, hardware firewall or IP filtering. After hours on the phone with their support staff our hosting company 1und1 was not yet able to fix the problem. They agreed to reopen a ticket for their server admins and keep us updated. Currently it does not look like a quick fix, but setting up the whole system from scratch at a different hosting company. Visit for updates. We are aware that the API outage is affecting both you and your users and we are working to resolve the issue and bring the service back on as soon as possible.

Very fast Data cleaning of product names, company names & street names - The correction of product names, company names, street names & addresses is a frequent task of data cleaning and deduplication. Often those names are misspelled, either due to OCR errors or mistakes of the human data collectors. The difference is that those names often consist of multiple words, white space and punctuation. For large data or even Big data applications also speed is very important. Our algorithm supports both requirements and is up to 1 million times faster compared to conventional approaches (see benchmark). The C# source code is available as Open Source in another Blog post and GitHub). A simple modification of the original source code will add support of names with multiple words, white space and punctuation: Instead of 357 CreateDictionary("big.txt",""); which parses the a given text file into single words simply use CreateDictionaryEntry("company/street/product name", "") to add company, street & product names to the dictionary. Then with Correct("misspelled street",""); you will get the correct street name from the dictionary. In line 35..38 you may specify whether you want only the best match or all matches within a certain edit distance (number of character operations difference): 35 private static int verbose = 0; 36 //0: top suggestion 37 //1: all suggestions of smallest edit distance 38 //2: all suggestions <= editDistanceMax (slower, no early termination) For every similar term (or phrase) found in the dictionary the algorithm gives you the Damerau-Levenshtein edit distance to your input term (look for suggestion.distance in the source code). The edit distance describes how many characters have been added, deleted, altered or transposed between the input term and the dictionary term. This is a measure of similarity between the input term (or phrase) and similar terms (or phrases) found in the dictionary.

Fast approximate string matching with large edit distances in Big Data - 1 million times faster spelling correction for edit distance 3 After my blog post 1000x times faster spelling correction got more than 50.000 views I revisited both algorithm and implementation to see if it could be further improved. While the basic idea of Symmetric Delete spelling correction algorithm remains unchanged the implementation has been significantly improved to unleash the full potential of the algorithm. This results in a 10 times faster spelling correction and 5 times faster dictionary generation and 2…7 times less memory consumption in v3.0 compared to v1.6 . Compared to Peter Norvig’s algorithm it is now 1,000,000 times faster for edit distance=3 and 10,000 times faster for edit distance=2. In Norvig’s tests 76% of spelling errors had an edit distance 1. 98.9% of spelling errors got covered with edit distance 2. For simple spelling correction of natural language with edit distance 2 the accuracy is good enough and the performance Norvig’s algorithm is sufficient. The speed of our algorithm enables edit distance 3 for spell checking and thus improves the accuracy by 1%. Beyond the accuracy improvement the speed advantage of our algorithm is useful for automatic spelling correction in large corpora as well as in search engines, where many requests in parallel need to be processed. Billion times faster approximate string matching for edit distance > 4 But the true potential of the algorithm lies in edit distances > 3 and beyond spell checking. The many orders of magnitude faster algorithm opens up new application fields for approximate string matching and a scaling sufficient for big data and real-time. Our algorithm enables fast approximate string and pattern matching with long strings or feature vectors, huge alphabets, large edit distances, in very large data bases, with many concurrent processes and real time requirements. Application fields: Spelling correction in search engines, with many parallel requests Automatic Spelling correction in large corpora Genome data analysis, Matching DNA sequences Browser fingerprint analysis Realtime Image recognition (search by image, autonomous cars, medicine) Face recognition Iris recognition Speech recognition Voice recognition Feature recognition Fingerprint identification Signature Recognition Plagiarism detection (in music /in text) Optical character recognition Audio fingerprinting Fraud detection Address deduplication Misspelled names recognition Spectroscopy based chemical and biological material identification File revisioning Spam detection Similarity search, Similarity matching Approximate string matching, Fuzzy string matching, Fuzzy string comparison, Fuzzy string search, Pattern matching, Data cleaning and many more Edit distance metrics While we are using the Damerau-Levenshtein distance for spelling correction for other applications it could be easily exchanged with the Levenshtein distance or similar other edit distances by simply modifying the respective function. In our algorithm the speed of the edit distance calculation has only a very small influence on the overall lookup speed. That’s why we are using only a basic implementation rather than a more sophisticated variant. Benchmark Because of all the applications for approximate string matching beyond spell check we extended the benchmark to lookups with higher edit distances. That’s where the power of the symmetric delete algorithm truly shines and excels other solutions. With previous spell checking algorithms the required time explodes with larger edit distances. Below are the results of a benchmark of our Symmetric Delete algorithm and Peter Norvig’s algorithm for different edit distances, each with 1000 lookups: input term best correction edit distance maximum edit distance SymSpellms per 1000 lookups Peter Norvigms per 1000 lookups factor marsupilamimarsupilami no correction* >20 9 568,568,000 – – marsupilamimarsupilami no correction >20 8 161,275,000 – – marsupilamimarsupilami no correction >20 7 37,590,000 – – marsupilamimarsupilami no correction >20 6 5,528,000 – – marsupilamimarsupilami no correction >20 5 679,000 – – marsupilamimarsupilami no correction >20 4 46,592 – – marsupilami no correction >4 4 459 – – marsupilami no correction >4 3 159 159,421,000 1:1,000,000 marsupilami no correction >4 2 31 257,597 1:8,310 marsupilami no correction >4 1 4 359 1:90 hzjuwyzacamodation accomodation 10 10 7,598,000 – – otuwyzacamodation accomodation 9 9 1,727,000 – – tuwyzacamodation accomodation 8 8 316,023 – – uwyzacamodation accomodation 7 7 78,647 – – wyzacamodation accomodation 6 6 19,599 – – yzacamodation accomodation 5 5 2,963 – – zacamodation accomodation 4 4 727 – – acamodation accomodation 3 3 180 173,232,000 1:962,000 acomodation accomodation 2 2 33 397,271 1:12,038 hous hous 1 1 24 161 1:7 house house 0 1 1 3 1:3 *Correct or unknown word, which is not in the dictionary and there are also no suggestions within an edit distance of

How 1000 Apps are using the FAROO Search API -   During the last 9 months more than 1000 companies and developers subscribed to our API, with more than 100 new applications every month. Today we want to share what are the typical use cases for our search API: An interesting discovery is the fact that our search API is mainly used to data mine the big data of the web, instead of plain web search. We turn the whole web into a giant database which is queried and analyzed by AI services, Data mining and Business intelligence applications. Big data becomes accessible and can be queried within milliseconds. Apps save the lead time for crawling the vast amount of pages themselves.

FAROO Updates - Following the user feedback here are a couple of updates we made recently: New FAROO API function: Trending terms, sorted by number of sources. Rate limiting more relaxed: 24 hour penalty after exceeding the rate limit removed. Private search added to FAROO FAQ: XKeyscore, PRISM, Tempora et al. Image extraction improved.

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