1 / 42

TI: An Efficient Indexing Mechanism for Real-Time Search on Tweets SIGMOD ‘11 C. Chen et al

TI: An Efficient Indexing Mechanism for Real-Time Search on Tweets SIGMOD ‘11 C. Chen et al. Pete Bohman Adam Kunk. Outline. Introduction Related Work System Overview Indexing Scheme Ranking Evaluation Conclusion. Real-Time Search. R equirements

lidia
Télécharger la présentation

TI: An Efficient Indexing Mechanism for Real-Time Search on Tweets SIGMOD ‘11 C. Chen et al

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. TI: An Efficient Indexing Mechanism for Real-Time Search on TweetsSIGMOD ‘11C. Chen et al Pete Bohman Adam Kunk

  2. Outline • Introduction • Related Work • System Overview • Indexing Scheme • Ranking • Evaluation • Conclusion

  3. Real-Time Search • Requirements • Contents searchable immediately following creation • Scale to thousands of updates/sec OBL Death 5,000 tweets/sec • Results relevant to query via cost efficient ranking

  4. Real-Time Search Vs. Time Rank TI Rank

  5. TI Concepts • Real-time search of microblogging applications is provided via two components: • Indexing Mechanism – for pruning tweets, only looking at a subset of all tweets (allows for speed) • Ranking Mechanism – for looking at relevant tweets (weeding out tweets that are not deemed important enough) • Main idea: look at important tweets only

  6. Outline • Introduction • Related Work • System Overview • Indexing Scheme • Ranking • Evaluation • Conclusion

  7. Partial Indexing • The Case for Partial Indexes • Stonebreaker, 1989 • Index only a portion of a column • User specified index predicates (where salary > 500) • Build index as a side-effect of query processing • Incremental index building

  8. View Materialization • An application of materialized views is to use cost models to automatically select which views to materialize. • Materialized views can be thought of as snapshots of a database, in which the results of a query are stored in an object. • The concept of only indexing essential tweets in real-time was borrowed from the idea of view materialization.

  9. Microblog Search • Google and Twitter have both released real-time search engines. • Google’s engine adaptively crawls the microblog • Twitter’s engine relies on Apache’s Lucene (high-performance, full-featured text search engine library) • But, both the Google and Twitter engines only utilize time in their ranking algorithms. • TI’s ranking algorithm takes much more than just time into account.

  10. Outline • Introduction • Related Work • System Overview • Indexing Scheme • Ranking • Evaluation • Conclusion

  11. TI Architecture

  12. In-memory structures • Certain structures are kept in-memory to support indexing and ranking • Keyword threshold – records statistics of recent popular queries • Candidate topic list – information about recent topics • Popular topic list – information about highly discussed topics

  13. User Graph • Twitter users have links to other friends • A User Graph is utilized to demonstrate this relationship • Gu = (U, E) • U is the set of users in the system • E is the friend links between them

  14. Tweet Tree Structure Nodes represent tweets Directed edges indicate replies or retweets Implemented by assigning tweets a tree encoding ID

  15. TI Design • Search is handled via an inverted index for tweets • Given a keyword, the inverted index returns a tweet list, T • T contains set of tweets sorted by timestamp

  16. TI Inverted Index TID = Tweet ID U-PageRank = Used for ranking TF = Term Frequency tree = TID of root node of tweet tree time = timestamp

  17. Ranking Support • In order to help ranking, TI keeps a table of metadata for each tweet • TID = tweet ID • RID = ID of replied tweet (to find parent) • tree = TID of root node of tweet tree • time = timestamp • count = number of tweets replying to this tweet

  18. Outline • Introduction • Related Work • System Overview • Indexing Scheme • Ranking • Evaluation • Conclusion

  19. TI Indexing Overview

  20. Tweet Classification • Observation • Users are only interested in top-K results for a query • Given a tweet t and a user’s query set Q, • ∃qi∈ Q and t is a top-K result for qi based on the ranking function F t is a distinguished tweet • Maintenance cost for query set Q?

  21. Query Set • Observation • 20% of queries represent 80% of user requests (Zipf’s dist.) • Suppose the nth query appears with a prob. of (Zipf’s distribution) • Let s be the # of queries submitted /sec. Expected time interval of the nth query is • Batch processing occurs every t’ sec We will keep the n-th query in Q, only if t(n) < t’

  22. Naïve Classifier • Dominant set ds(qi,t) • The tweets that have higher ranks than tfor query qi • Performance problems • Full scan of tweet set required for dominant set • Test each tweet against every query

  23. Optimization 1 • Observation • The rank of the lower results are stable • Replace dominant set with a comparison to the score of Q’s Kth result.

  24. Optimization 2 • Compare a tweet to similar queries • Given tweet t = <k1, k4>, compare t to Q1, Q3, Q4

  25. Real-Time Indexing • New tweets categorized as being distinguished (index these immediately) • If tweet belongs to existing tweet tree, retrieve its parent tweet to get root ID and generate encoding. Update count number in parent. • Tweet is inserted into tweet data table. • Tweet is inserted into inverted index. • Main cost is updating the inverted index (due to each keyword in the tweet).

  26. Batch Indexing • New tweets categorized as being noisy (index these at a later time) • Instead of indexing in inverted index, append tweet to log file. • Batch indexing process periodically scans the log file and indexes the tweets there.

  27. Outline • Introduction • Related Work • System Overview • Indexing Scheme • Ranking • Evaluation • Conclusion

  28. Ranking Overview • Ranking functions are completely separate from the indexing mechanism • New ranking functions could be used • TI’s proposed ranking function is based on: • User’s PageRank • Popularity of the topic • Timestamp (self-explanatory) • Similarity between tweet and the query

  29. User’s PageRank • Twitter has two types of links between users • f(u): the set of users who follow user u • f-1(u): the set of users who user u follows • A matrix, Mf[i][j], is used to record the following links between users • A weight factor is given for each user • V = (w1, w2, ….. wn)

  30. User’s PageRank Formula • PageRank formula is given as: Pu = VMfx • So, the user’s PageRank is a combination of their user weight and how many followers they have • The more popular the user, the higher the PageRank

  31. Popularity of Topics • Users can retweet or reply to tweets. • Popularity can be determined by looking at the largest tweet trees. • Popularity of tree is equal to the sum of the U-PageRank values of all tweets in the tree

  32. Similarity between query and tweet • The similarity of a query and the tweet t can be computed as follows: sim(q,t) = (q x t) / (|q||t|) • q and t are turned into bags of words, then viewed as vectors

  33. Ranking Function • q.timestamp = query submittal time • tree.timstamp = timestamp of tree t belongs to (timestamp of root node) • w1, w2, w3 are weight factors for each component (all set to 1)

  34. Adaptive Indexing • The size of the inverted index limits the performance of the search for tweets • The size of the inverted index grows with the number of tweets • To alleviate this problem, adaptive indexing is proposed:

  35. Adaptive Indexing Cont. • The main idea: • Iteratively read a block of the inverted index (rather than the entire thing) • Stop iterating blocks when the timestamp value gives a score low enough to throw out the results • Stop because the rest of the tweets in the inverted index will also have a lower score

  36. Outline • Introduction • Related Work • System Overview • Indexing Scheme • Ranking • Evaluation • Conclusion

  37. Evaluation • Evaluation performed on real dataset • Dataset collected for 3 years (October 2006 to November 2009) • 500 random users picked as seeds (from which other users are integrated into the social graphs) • 465,000 total users • 25,000,000 total tweets • Experiments typically 10 days long • 5 days training, 5 days measuring performance

  38. Evaluation Cont. • Queries lengths are distributed as follows: • ~60% are 1 word • ~30% are 2 words • ~10% are more than 2 words • Queries submitted at random, tweets are inserted into system based on original timestamps (from dataset)

  39. Indexing Cost (per 10k tweets)

  40. Performance of Query Processing • TimeBased represents using only tweet timestamp (like Google)

  41. Outline • Introduction • Related Work • System Overview • Indexing Scheme • Ranking • Evaluation • Conclusion

  42. Conclusion • Current search engines unable to index social networking data • Adaptive indexing mechanism to reduce update cost • Cost efficient and effective ranking function • Successful evaluation using real data set from twitter

More Related