inverted-index

Ways to create a huge inverted index

I want to create a big inverted index of around 106 terms. What method would you suggest? I'm thinking in fast binary key store DBs like Tokyo cabinet, voldemort, etc. Edit: I've tried MySQL in the past for storing a table of two integers to represent the inverted index, but even with the first column having a db index, queries were very...

Inverted index in a search engine

Hello there, I'm trying to write some code to make a small application for searching text from files. Files should be crawled, and I need to put an inverted index to boost searches. My problem is that I kind of have ideas about how the parser would be, I'm willing to implement the AND, NOT, OR in the query. Whereas, I couldn't figure...

How do search engines merge results from an inverted index?

How do search engines merge results from an inverted index? For example, if I searched for the inverted indexes of the words "dog" and "bat", there would be two huge lists of every document which contained one of the two words. I doubt that a search engine walks through these lists, one document at a time, and tries to find matches wit...

what is the best way to build inverted index ?

I'm building a small web search engine for searching about 1 million web pages and I want to know What is the best way to build the inverted index ? using the DBMS or What …? from many different views like storage cost, performance, speed of indexing and query? and I don't want to use any open source project for that I want to make my ow...

Assistance with building an inverted-index

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 saved as a string value. So, hashmap["ba"] = "bad barley base" Once I'm done tokenizing a line I take that hashmap, serialize it, and append i...

How to search phrase queries in inverted index structure?

If we want to search a query like this "t1 t2 t3" (t1,t2 ,t3 must be queued) in an inverted index structure , which ways should we do ? 1-First we search the "t1" term and find all documents that contains "t1" , then do this work for "t2" and then "t3" . Then find documents that positions of "t1" , "t2" and "t3" are next to each other ...

I have created inverted index for a website but where to store that? Database for a search engine?

What can be the database for a search engine? I mean after creating inverted index for a site, where one could store it so that program can create indices for other sites and save them too. Later on indexer can query them also. Because indices can range in thousands of billions. Thanksyou ...

How to get byte offset in a file in python

hello, 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. I need something like this hello hello.txt@1124 I need the locations for making a full inverted index. Please help. ...

Storing an inverted index

Hello, I am working on a project on Info Retrieval. I have made a Full Inverted Index using Hadoop/Python. Hadoop outputs the index as (word,documentlist) pairs which are written on the file. For a quick access, I have created a dictionary(hashtable) using the above file. My question is, how do I store such an index on disk that also ha...

Searching a normal query in an inverted index

I have a full inverted index in form of nested python dictionary. Its structure is : {word : { doc_name : [location_list] } } For example let the dictionary be called index, then for a word " spam ", entry would look like : { spam : { doc1.txt : [102,300,399], doc5.txt : [200,587] } } so that, the documents containing...

Loading a large dictionary using python pickle

I have a full inverted index in form of nested python dictionary. Its structure is : {word : { doc_name : [location_list] } } For example let the dictionary be called index, then for a word " spam ", entry would look like : { spam : { doc1.txt : [102,300,399], doc5.txt : [200,587] } } I used this structure as python dict are pretty o...