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Internet crawls reveal similar statistics In the Enron email dataset, 61% of messages are in threads. 31% quote other messages verbatim@#Nave Solution 1 : Index Everything|Pros: Simple to implement Semantics are preserved Cons: Index size blows up Performance penalty (big index + post filtering)N,E,E A%Nave Solution 2: Index Just One CopyPros: Best performance Not too difficult to implement Cons: Only applies to the duplicates scenario Semantics are changed, and relevant results may not be returned for a queryN0t0t B-The Web Duplicate Case: Meta Data Vs. Content<Removal of web duplicates changes the semantics of the query==D Our SolutionContent is split to shared and private parts Shared content is indexed only once Private content (such as metadata in the Web duplicates case) is indexed for each document Index provides virtual cursors that simulate having all content indexedZE AdvantagesYIndex size, build time, and query efficiency Precise semantics No need for post-filteringFInverted IndicesIndex is sorted by term For each term, a sorted list of documents in which it appears is maintained (postings list) Each occurrence (posting) contains additional payloadG Document Sharing Model&Each document is partitioned into private and shared content. The two types are differentiated by posting payload Documents exist in a tree  shared content is shared with all descendents Document IDs (and hence index order) are dictated by a DFS traversal of document treesZThe Document Tree/Content is shared from ancestor to descendants:00J Example: MQuerying Inverted IndexesRQueries contain mandatory terms, forbidden terms, and optional terms (such as +term1  term2) Typically a zigzag algorithm is used Uses cursors on postings list. 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