I understand that a fundamental aspect of full-text search is the use of inverted indexes. So, with an inverted index a one-word query becomes trivial to answer. Assuming the index is structured like
I read somewhere that when you have an inverted index (for instance,开发者_运维问答 you have a sorted list of pages of brutus, a sorted list of pages for caesar, and a sorted list of pages for calpurn
The question: What solution or tips would you have to deal with a very large (multi terabytes) database indexed on strong hashes with high redundancy?
I\'m writing an inverted index for a search engine on a collection of documents. Right now, I\'m storing the index as a dictionary of dictionaries. That is, each keyword maps to a dictionary of docIDs
I consider using Sphinx search in one of my projects so I have a few questions related to it. When using SphinxSE and RT index, every UPDATE or INSERT in the SphinxSE table will update the index, ri
I have a full inverted index in form of nested python dictionary. Its structure is : {word : { doc_name : [location_list] } }
I have a full inverted index in form of nested python dictionary. Its structure is : {word : { doc_name : [location_list] } }
I am making a inverted index using hadoop and python. I want to know how can I include the byte offset of a line/word in python.
If we want to search a query like this \"t1 t2 t3\" (t1,t2 ,t3 must be queued) in an inverted index structure ,
It\'s part of an information retrieval thing I\'m doing for school. The plan is to create a hashmap of words using the the first two letters of the word as a key and any words with the two letters sav
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