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 Bing Social Search

Real-Time Search Input Data  Example of what kind of input data is considered for real-time search systems:  twittervision 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 Ranking Relevance Authority  Real-Time Search Ranking Relevance Temporal immediacy Popularity

Real-Time vs. Conventional Search  Conventional search input Crawl the web periodically and update index ○ Web documents evolve Incapable 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 ConventionalReal-Time ShoppingCommerce EntertainmentTravel AdultEconomy

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 Million Value derived from types of queries entered in real-time search systems Utilized 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) Indexing Non structured data If 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  Challenge Processing 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 queries Distinguished tweets indexed in real-time Noisy tweets indexed by batch process

TI Tweet Classification  Observation Users 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 tweet Those tweets not appearing in the top-K results for any of the systems previous queries

TI Indexing  Must limit the size of the query set 1.6 Billion twitter queries per day

Query set optimization  Observation 20% of queries represent 80% of user requests  Therefore Zipf’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 systems In RDBMS system data is strictly defined including algebraic operators Results 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 Comparison TI Rank Vs. Time Rank

What are others doing?

 Facebook Real-Time Feed

Implications  New type of data not currently searchable through existing search engines New 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  Real Time Search User Behavior  TwitterRank: Finding Topic-Sensitive Influential Twitterers  Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors  TwitterStand: News in Tweets  Learning Effective Ranking Functions for Newsgroup Search  TwitterSearch: A Comparison of Microblog Search and Web Search  TwitterVision  Bing Social  Reak tune search on the web: Queries, topics, and economic value

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?