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AutoSuggest

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Presented by Ralph LeVan, Senior Research Scientist, OCLC Research, as a lightning talk at Code4LibMidWest at the University of Chicago Regenstein Library on 14 July 2016.

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AutoSuggest

  1. 1. This is for ELM Ralph LeVan Sr. Research Scientist 7/14/2016 Code4Lib Midwest AutoSuggest
  2. 2. Goals • Return records at keystroke speeds • Run on an underpowered Unix box 2
  3. 3. Result • Precalculate a response record for every possible legitimate keystroke combination • Load those records into a Pears database and expose via SRW • Client javascript takes keystrokes and turns them into queries to an AutoSuggest servlet • The thin gateway servlet takes queries, turns them into SRW requests and passes through the record returned 3
  4. 4. How are the records precalculated? • For each source record, a relevance score is calculated – For VIAF, that’s a value in the record • Names are extracted from the record. – The names are ranked – The best name gets the score of the record and subsequent names get a reduced score – For each name, a tuple is generated containing the name, the recordID of the source record, the score for the name and any other data extracted from the record 4
  5. 5. How are the records precalculated? • The tuples are sorted • A process reads in all the names that start with the same letter. • The first two terms are compared and a top-10 list is started for each set of letters in common – E.g. Andrew and Anthony each go into the top-10 list for A and AN. – AutoSuggest records are generated for the singletons Andrew and Anthony. The full name is the key for these records. 5
  6. 6. How are the records precalculated? • The next term is compared to the one that preceeded it – E.g. Anthony and Astrid are compared – Astrid is added to the top-10 list for A – An AutoSuggest record is written for the AN list • The key for the record is AN • Each of the names (and associated data) are included in the record – An AutoSuggest record is generated for the singleton Astrid 6
  7. 7. Top-10 is complicated • The naïve assumption is that the 10 names with the highest score would be in the list • But, all the variations on Shakespeare that start with S would be in the S record. • So, a candidate name for the top-10 list is checked to see if there is a higher ranking name with the same recordID before it is added 7
  8. 8. It’s not really that easy • All the names that start with A won’t fit into memory. • We do all of this work in Hadoop • We partition the tuple input on the first 5 letters in common • Process as described before, but write the shorter fragments (less than 5 letters) to a separate directory • Combine those lists to produce unified lists (and records) 8
  9. 9. Loaded into Pears • All these generated records are loaded into Pears • Lots and lots of records – The latest AutoSuggest database for VIAF has 341 million records in it. – VIAF itself only has 31M records 9
  10. 10. Thank You! ©2014 OCLC. This work is licensed under a Creative Commons Attribution 3.0 Unported License. Suggested attribution: “This work uses content from [presentation title] © OCLC, used under a Creative Commons Attribution license: http://creativecommons.org/licenses/by/3.0/” Ralph LeVan levan@oclc.org 10

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