a (corny) ending

2 Course Outcomes After this course, you should be able to answer: –How search engines work and why are some better than others –Can web be seen as a collection of (semi)structured databases? If so, can we adapt database technology to Web? –Can useful patterns be mined from the pages/data of the web? What did you think these were going to be?? REVIEW

3 Main Topics Approximately three halves plus a bit: –Information retrieval –Information integration/Aggregation –Information mining –other topics as permitted by time REVIEW

4 Adapting old disciplines for Web-age Information (text) retrieval –Scale of the web –Hyper text/ Link structure –Authority/hub computations Databases –Multiple databases Heterogeneous, access limited, partially overlapping –Network (un)reliability Datamining [Machine Learning/Statistics/Databases] –Learning patterns from large scale data Social Networks REVIEW

Topics Covered 1.Introduction 2.Text retrieval; vectorspace ranking 3.Indexing/Retrieval issues 4.Correlation analysis & Latent Semantic Indexing 5.Search engine technology 6.Social Networks  7.Anatomy of Google etc 8.Clustering 9.Text Classification  (m) 10.Filtering/Personalization 10.Web & Databases: Why do we even care? 11.XML and handling semi- structured data 12.Semantic web and its standards (RDF/RDF- S/OWL...) 13.Information Extraction  14.Data/Information Integration/aggregation 15.Query Processing in Data Integration: Gathering and Using Source Statistics

Topics Covered Clustering (2) Text Classification (2) Filtering/Recommender Systems (1.5) Computational Advertising (1) XML and handling semi- structured data + Semantic Web standards (3) Information Extraction (2.5) Information/data Integration (1.5) Introduction (2) Text retrieval; vectorspace ranking (3) Indexing; tolerant retrieval; (1) Correlation analysis & Latent Semantic Indexing (3) Link Analysis in Web Search (A/H; Pagerank) (4) Crawling; Map/Reduce (2) Social Networks (3)

Finding“Sweet Spots” in computer-mediated cooperative work It is possible to get by with techniques blythely ignorant of semantics, when you have humans in the loop –All you need is to find the right sweet spot, where the computer plays a pre-processing role and presents “potential solutions” –…and the human very gratefully does the in- depth analysis on those few potential solutions Examples: –The incredible success of “Bag of Words” model! Bag of letters would be a disaster ;-) Bag of sentences and/or NLP would be good –..but only to your discriminating and irascible searchers ;-)  Giving “pointers” where results can be found and letting users do the “reading” is okay for simple queries But for aggregate queries, it becomes tiresome  Here read these 700 employee files to figure out the average employee salary.

Collaborative Computing AKA Brain Cycle Stealing AKA Computizing Eyeballs A lot of exciting research related to web currently involves “co-opting” the masses to help with large-scale tasks –It is like “cycle stealing”—except we are stealing “human brain cycles” (the most idle of the computers if there is ever one ;-) Remember the mice in the Hitch Hikers Guide to the Galaxy? (..who were running a mass-scale experiment on the humans to figure out the question..) –Collaborative knowledge compilation (wikipedia!) –Collaborative Curation –Collaborative tagging –Paid collaboration/contracting Many big open issues –How do you pose the problem such that it can be solved using collaborative computing? –How do you “incentivize” people into letting you steal their brain cycles?

Tapping into the Collective Unconscious AKA “Wisdom of the Crowds” Another thread of exciting research is driven by the realization that WEB is not random at all! –It is written by humans –…so analyzing its structure and content allows us to tap into the collective unconscious.. Meaning can emerge from syntactic notions such as “co-occurrences” and “connectedness” Examples: –Analyzing term co-occurrences in the web-scale corpora to capture semantic information (today’s paper) –Analyzing the link-structure of the web graph to discover communities DoD and NSA are very much into this as a way of breaking terrorist cells –Analyzing the transaction patterns of customers (collaborative filtering)

If you don’t take Autonomous/Adversarial Nature of the Web into account, then it is gonna getcha.. Most “first-generation” ideas of web make too generous an assumption of the “good intentions” of the source/page/ creators. The reasonableness of this assumption is increasingly going to be called into question as Web evolves in an uncontrolled manner… Controlling creation rights removes the very essence of scalability of the web. Instead we have to factor in adversarial nature.. –Links can be manipulated to change page importance So we need “trust rank” –Fake annotations can be added to pages and images So we need ESP-game like self-correcting annotations.. –Fake/spam mails can be sent (and the nature of the spam mails can be altered to defeat simple spam classifiers…) So we need adversarial classification techniques –Sources may export untrustworthy/made-up data So we need SourceRank?

Interactive Review

Anatomy may be likened to a harvest-field. First come the reapers, who, entering upon untrodden ground, cut down great store of corn from all sides of them. These are the early anatomists of Europe Then come the gleaners, who gather up ears enough from the bare ridges to make a few loaves of bread. Such were the anatomists of last. Last of all come the geese, who still contrive to pick up a few grains scattered here and there among the stubble, and waddle home in the evening, poor things, cackling with joy because of their success. Gentlemen, we are the geese. --John Barclay English Anatomist

Information Integration on Web still rife with uncut corn Unlike anatomy of Barclay’s day, Web is still young. We are just figuring out how to tap its potential …You have great stores of uncut corn in front of you. ……