Big Data in The Web. Agenda. Big Data Asking the Right Questions Wisdom of Crowds in the Web The Long Tail Issues and Examples Concluding Remarks

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1 Big Data in The Web Ricardo Baeza-Yates Yahoo! Labs Barcelona & Santiago de Chile Agenda Big Data Asking the Right Questions Wisdom of Crowds in the Web The Long Tail Issues and Examples Concluding Remarks - 3-1

2 Big Data Capture, transfer, store, search, share, analyze, and visualize large data in reasonable time Large volume and growth Petabytes to exabytes Growth is estimated in 3 exabytes per day Structured vs. non-structured data Diversity Types, formats, complexity, topics, etc. Best Public Data Example: The Web Content: text, multimedia Structure: graphs Usage: real time streams Big Data Focus on analytics Many storage technologies: DBs, DWs, distributed file systems, Many processing technologies: Cloud computing, map-reduce (Hadoop), Data mining, clustering, classification, Machine learning, A/B testing, NLP, Simulation Several technology providers Initial best practices (see TDWI report, 2011) Main challenges: scalability, online

3 Big Data: The Five V s Characteristic Data Issue Computing Issue Volume Variety Veracity Scale, Redundancy Heterogeneity, Complexity Completeness, Bias, Sparsity, Noise, Spam Scalability Adaptability, Extensibility Reliability, Trust Velocity Real time Online Value Usefulness, Privacy Business dependent Asking the Right Questions Problem Driven What data we need? How much? How we collect it? How we store and transfer it? Understanding the Data How sparse is the data? How much noise? There is redundancy? There are biases? There is spam? Any outliers? Analyzing the Data Any privacy issues? Do we need to anonymize? How well our algorithms scale? Can we visualize the results?

4 Too Much Data Available The Web is a database! Data does not imply information Many analyses for the sake of it (data driven) Analyzing data is not CS per se Publish in the right forum! Big Data or Right Data? The Different Facets of the Web

5 The Structure of the Web Big Data in the Web Wordnet Explicit Metadata RDF Wikipedia ODP Y! Answers Flickr UGC Blogs, Groups Implicit Text Anchors + links Logs (Clicks+Queries) Private Quality? Scale

6 What is in the Web? How Good it is? Quantity Usergenerated Traditional publishing Quality What else is in the Web?

7 Noise and Spam Noise may come from many places: Instruments that measure How we interpret the data (example later) Spam is everywhere Web Spam Deceiving text, links, clicks due to an economic incentive Depending on the goal and the data, spam is easier to generate Depending on the type & target data, spam is easier to fight Disincentives for spammers? Social Economical Web Spam is NOT Mail Spam

8 Content and Metadata Trends [Ramakrishnan and Tomkins 2007]

9 Web Data Trends User Generated Content Massive (quality vs. quantity) Social Networks Real time (people + physical sensors) Impact Fragmentation of ownership Fragmentation of access (longer heavy tail) Fragmentation of right to access Viability Business model based in advertising The Wisdom of Crowds James Surowiecki, a New Yorker columnist, published this book in 2004 Under the right circumstances, groups are remarkably intelligent Importance of diversity, independence and decentralization Aggregating data large groups of people are smarter than an elite few, no matter how brilliant they are better at solving problems, fostering innovation, coming to wise decisions, even predicting the future

10 6/28/13 Web Data Mining Content: text & multimedia mining Structure: link analysis, graph mining Usage: log analysis, query mining Relate all of the above Web characterization Particular applications Flickr: Clustering Pictures

11 6/28/13 Popularity Flickr: Geo-tagged pictures

12 Crowd Sourcing Web-based peer production has produced a number of successful products and communities Wikipedia, Y! Answers, YouTube, Flickr, Digg,... Can this form of production be harnessed for other ends? Existing successes are hard to replicate at will Amazon Mechanical Turk (AMT) Like outsourcing, but in a micro-distributed fashion Thousands of turkers working on hundreds of HITS (tasks) Rates are typically few cents per task Quality of their work is positively evaluated (e.g. in IR) The Wisdom of (Large) Crowds Crucial for Search Ranking Text: Web Writers & Editors not only for the Web! Links: Web Publishers Tags: Web Taggers Queries: All Web Users! The crowd implicitly knows the experts! (! action Queries and actions (or no

13 Scalability How to scale? Doubling the data in the best case will double the time Time complexity vs. result quality trade-off Example: entity detection in linear time at almost state of the art quality That implies that there exists a text size n* for which the linear algorithm will produce more correct entities Distributed parallel processing Map-reduce not always works Parallelism is problem dependent Online processing needs a different approach Redundancy and Bias There is any dependency in the data? There is any duplication? Lexical duplication in the Web is around 25% Semantic duplication is larger Are there any biases? Example 1: clicks in search engines Bias to the ranking and the interface There is a ranking bias in the Web content Example 2: tag recommendation

14 We can suggest tags: nice but Privacy Example: AOL Query Logs Release Incident A Face Is Exposed for AOL Searcher No , By MICHAEL BARBARO and TOM ZELLER Jr, The New York Times, Aug No conducted hundreds of searches over a three-month period on topics ranging from numb fingers to 60 single men. Other queries: landscapers in Lilburn, Ga, several people with the last name Arnold and homes sold in shadow lake subdivision gwinnett county georgia. Data trail led to Thelma Arnold, a 62-year-old widow who lives in Lilburn, Ga., frequently researches her friends medical ailments and loves her three dogs

15 Risks of Privacy (ZIP code, date of birth, gender) is enough to identify 87% of US citizens using public DB (Sweeney, 2001) K-anonymity Suppress or generalize attributes until each entry is identical to at least k-1 other entries Federal Trade Commission in US: Privacy policies should address the collection of data itself and not just how the data is used, Dec Data Protection Directive in EU Risks of Privacy: Query Logs Profile: [Jones, Kumar, Pang, Tompkins, CIKM 2007] Gender: 84% Age (±10): 79% Location (ZIP3): 35% Vanity Queries: [Jones et al, CIKM 2008] Partial name: 8.9% Complete: 1.2% More information: A Survey of query log privacy-enhancing techniques from a policy perspective [Cooper, ACM TWEB 2008] A good anonymization is still an open problem

16 Sparsity The Long Tail is always Sparse Why there is a long tail? When the crowd dominates Empowering the tail Example: Relations from Query Logs The Wisdom of Crowds Popularity Diversity Quality Coverage Long tail Heavy tail

17 The Long Tail Most measures in the Web follow a power law Heavy tail of user interests Many queries, each asked very few times, make up a large fraction of all queries Movies watched, blogs read, words used, One explanation Interests People Normal people 42 Weirdos

18 Heavy tail of user interests Many queries, each asked very few times, make up a large fraction of all queries Applies to word usage, web page access, We are all partially eclectic The reality Interests People Broder, Gabrilovich, Goel, Pang; WSDM Example: Click Distribution User interaction is a power law! (Zipf s principle of minimal effort)

19 When the crowd dominates Kills the long tail See (obsolete now) shwarzneger example Empowering the Tail The Filter Bubble, Eli Pariser Avoid the Poor get Poorer Syndrome Solutions: Diversity Novelty Serendipity Explore & Exploit

20 How to Circumvent Sparsity? Wisdom of ad-hoc crowds? Aggregate data in the right way When data is sparse Aggregate users around same intent, task, facet,. Change granularity ad hoc Middle age men Fans of Messi Example: Mining Geo/time Data Optimal Touristic Paths from Flickr Good for tourists and locals De Choudhury et al, HT

21 Aggregating in the Long Tail The long tail is important not only for e- commerce, but because we are all there Personalization vs. Contextualization User interaction is another long tail Interests People Epilogue l The Web is scientifically young l The Web is intellectually diverse l The technology mirrors the economic, legal and sociological reality l Data must be interesting! (Gerhard Weikum) l Problem driven l Plenty of challenges

22 Mirror of Society Exports/Imports vs. Domain Links Baeza-Yates & Castillo, WWW

23 Questions? ASIST 2012 Book of the Year Award Contact: Thanks to many people at Yahoo! Labs 23