Upload
pamela-hill
View
221
Download
0
Tags:
Embed Size (px)
Citation preview
TI: AN EFFICIENT INDEXING MECHANISM FOR REAL-TIME
SEARCH ON TWEETSSIGMOD ‘11
C. CHEN ET AL
Pete Bohman
Adam Kunk
Real-Time Search
Definition: A search mechanism capable of finding information in an online fashion as it is produced.Technology belonging to real-time web that
enables users to receive information as soon as it is published
Real-Time Search
In terms of real-time search, what does “online” mean?Online means that a constant stream of
input data is handled as it enters the system, contrary to batch processing
Bing Social Search
Real-Time Search Input Data Example of what kind of input data is
considered for real-time search systems:
twittervision
Real-Time Content Microblogging - Entirely new type of data
1. Short temporal life span
2. Little to no context
3. Simple ideas, fast reporting of events
4. Metadata: time, location, social links
5. Less factual, more opinionated
6. Static posts
7. Furious input rate
8. Often no hyperlink structure, few traditional ranking factors
Current search engines don’t take full advantage of this new data type
Real-Time vs. Conventional Search
Conventional Search RankingRelevance Authority
Real-Time Search RankingRelevanceTemporal immediacy Popularity
Real-Time vs. Conventional Search
Conventional search input Crawl the web periodically and update index
○ Web documents evolveIncapable of crawling and indexing the entire web in
real-time
Real-time search input Stream of data.No need to poll since the posts are static
What can we do with real-time search engines?
User Query Analysis
Collecta real-time search engine Analyzed ~1 Million queries
Continuous Queries○ Monitor events by frequently resubmitting the
same query Different query categories
Conventional Real-Time
Shopping Commerce
Entertainment Travel
Adult Economy
Crowdsourcing Real-Time Data
Crowd sourcing of first hand reports
Value of Real-Time Search The estimated value of real-time search
is around $33 MillionValue derived from types of queries entered
in real-time search systemsUtilized adwords to determine worth of
keywords appearing in queries
Applications of Real-Time Search TwitterStand: Real-time news reports
Example: Coverage of MJ’s death
Applications of Real-Time Search Real-time alert systems
Leverages tweet metadata (time, location) to raise alerts
Earthquake localization based on tweets
Twitter Real-Time Alerts
USGS Twitter Earthquake Detector
Difficulties of Real-Time Search
Two factors:Efficient indexing in order to provide for fast
results
Effective ranking in order to return relevant results
Indexing: RDBMS RDBMS Indexing
Indexes built on columns commonly used in queries
Improves the speed of retrieval operations
Indexing: Conventional Search
Conventional Search (Inverted) IndexingNon structured dataIf a document does not exist in the index, it will not
appear in query results
Indexing: Real-Time Search
Index stream of data Map keywords to tweets containing those
keywords
ChallengeProcessing the stream in a timely manor
○ 5,000 tweets per second
TI Indexing
Not feasible to index every incoming tweet immediately
Selective indexing based on results that are most likely to appear in queriesDistinguished tweets indexed in real-timeNoisy tweets indexed by batch process
TI Tweet Classification
ObservationUsers are only interested in top-K results for
a query Distinguished tweets
Tweet that belongs in the top-K result set of previous query
Noisy tweetThose tweets not appearing in the top-K
results for any of the systems previous queries
TI Indexing
Must limit the size of the query set1.6 Billion twitter queries per day
Query set optimization
Observation20% of queries represent 80% of user
requests
ThereforeZipf’s distribution used statistically limit the
number of queries tweets were compared against
Real-Time Search Ranking How does ranking differ from traditional
web ranking?Typical web search engines rank based on
links to a site, and links from a site (PageRank)
Microblogging data contains social networking links ○ Followers○ Friends○ Re-tweets
Real-Time Search Ranking Ranking is not necessary in RDBMS
systemsIn RDBMS system data is strictly defined
including algebraic operatorsResults are complete not subjective
TI Ranking
Ranking function comprised of:1) User’s PageRank
○ Combination of user weight (defaulted to 1) and how many followers
they have (popularity)
2) Timestamp (self-explanatory)
3) Similarity between tweet and the query
TI Ranking Ranking function also
comprised of:4) Popularity of the topic
Determined by large tweet trees
Popularity of tree is equal to the sum of the U-PageRank values of all tweets in the tree
Tweet Tree Structure
TI Ranking ComparisonTI Rank Vs. Time Rank
What are others doing?
What are others doing?
FacebookReal-Time Feed
Implications
New type of data not currently searchable through existing search enginesNew search tools developed for new data New user search behavior
○ Continuous search results (non-static) Advertisers
○ Chance for more targeted advertisements
Conclusion
TI makes use of two concepts in their real-time search of Twitter:Selective Indexing
○ Form of partial indexing, can’t afford to index every incoming tweet due to large volume of input
Ranking○ Ranking is a known technique, but
microblogging applications provide new ranking algorithms
Conclusion
Real-time search engines must provide:Online algorithms to handle constant input Relevant search results
References TI: An Efficient Indexing Mechanism for Real-Time Search on Tweets
http://www.comp.nus.edu.sg/~ooibc/sigmod11ti.pdf Real Time Search User Behavior
http://faculty.ist.psu.edu/jjansen/academic/jansen_real_time_search.pdf TwitterRank: Finding Topic-Sensitive Influential Twitterers
http://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1503&context=sis_research Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors
http://ymatsuo.com/papers/www2010.pdf TwitterStand: News in Tweets
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.148.1477&rep=rep1&type=pdf Learning Effective Ranking Functions for Newsgroup Search
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.92.5556&rep=rep1&type=pdf TwitterSearch: A Comparison of Microblog Search and Web Search
http://www.stanford.edu/~dramage/papers/twitter-wsdm11.pdf TwitterVision
http://twittervision.com/ Bing Social
http://www.bing.com/social Reak tune search on the web: Queries, topics, and economic value
http://collecta.com/RealTimeSearch.pdf
Discussion Questions
1) What do you think is the most innovative technique in the TI approach that led to real-time microblog search results?
Discussion Questions
2) Given the partial indexing optimization provided in the paper, how do you think Google could optimize their indexing algorithm in order to capture the newest content on the web?
Discussion Questions
3) TI makes use of a ranking function in order to select tweets based on various user characteristics. What would you change about the ranking function, if anything?