2016-02-11 09:37:51 -05:00
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< title > Embracing Uncertainty< / title >
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Embracing Uncertainty & ODIn Lab Overview
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Exploring < u > < b > O< / b > < / u > nline < u > < b > D< / b > < / u > ata < u > < b > In< / b > < / u > teractions
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2017-08-31 17:18:47 -04:00
< a href = "https://odin.cse.buffalo.edu" target = "_blank" >
2016-02-11 09:37:51 -05:00
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2017-08-31 17:18:47 -04:00
< h5 > < a href = "https://odin.cse.buffalo.edu" > https://odin.cse.buffalo.edu< / a > < / h5 >
2016-02-11 09:37:51 -05:00
< img src = "graphics/qrcode.31361737.png" / >
< / section >
< section >
< h2 > Embracing Uncertainty< / h2 >
< div class = "headertext" style = "float: left; color: #041a9b; height: 3em;" > U @ Buffalo< / div >
< div class = "headertext" style = "color: #041a9b" >
Ying Yang, Niccolo Meneghetti, < br / >
Arindam Nandi, Vinayak Karuppasamy, < br / >
< u > Oliver Kennedy< / u > , Jan Chomicki< / div >
< div class = "headertext" style = "float: left; color: red;" > Oracle< / div >
< div class = "headertext" style = "color: red;" > Ronny Fehling, Zhen-Hua Liu, Dieter Gawlick< / div >
< / section >
< section >
< h2 > Before we begin...< / h2 >
< / section >
< section >
< h2 > Insider Threats< / h2 >
< ul >
< li > How do we identify < i > abnormal< / i > query behavior from users?< / li >
< li > What is < i > normal< / i > user behavior?< / li >
< li > Multiple gigs of query logs from M& T< / li >
< / ul >
< p > ...with < b > Gokhan Kul, Duc Thanh Anh Luong, Ting Xie< / b > , Shambhu, Varun, Hung< / p >
< / section >
< section >
< h2 > Pocket Data< / h2 >
< ul >
< li > Months of query logs from PhoneLab Phones (2 queries per phone per second)< / li >
< li > SQLite is inefficient< / li >
< li > SQLite is being used inefficiently< / li >
< li > Let's develop a benchmark to help shine a light on these inefficiencies< / li >
< / ul >
< p > ...with < b > Jerry Ajay< / b > , Geoff, Luke< / p >
< / section >
< section >
< h2 > Just-in-Time Datastructures< / h2 >
< ul >
< li > Decouple Physical Structure from Logical Interface.< / li >
< li > Express Datastructure Organization through Rewrite Rules.< / li >
< li > ...allows hybridized datastructures for intermediate tradeoffs.< / li >
< li > ...allows for semifunctional datastructures with all the benefits but fewer tradeoffs.< / li >
< / ul >
< p > ...with Luke< / p >
< / section >
< / section >
< section >
< section >
< h3 > A Big Data Fairy Tale< / h3 >
< / section >
< section >
< img src = "graphics/dagobert83-female-user-icon-800px.png" height = "300" / >
< h4 > Meet Alice< / h4 >
< attribution > (OpenClipArt.org)< / attribution >
< / section >
< section >
< img src = "graphics/dagobert83-female-user-icon-800px.png" height = "300" / >
< img src = "graphics/littlestorefront-800px.png" height = "300" / >
< h4 > Alice has a Store< / h4 >
< attribution > (OpenClipArt.org)< / attribution >
< / section >
< section >
< img src = "graphics/littlestorefront-800px.png" height = "300" style = " vertical-align: middle;" / >
< span style = "font-size: 3em; vertical-align: middle;" > →< / span >
< img src = "graphics/matt-icons_text-x-log-300px.png" height = "300" style = " vertical-align: middle;" / >
< h4 > Alice's store collects sales data< / h4 >
< attribution > (OpenClipArt.org)< / attribution >
< / section >
< section >
< img src = "graphics/dagobert83-female-user-icon-800px.png" height = "300" style = " vertical-align: middle;" / >
< span style = "font-size: 3em; vertical-align: middle;" > +< / span >
< img src = "graphics/matt-icons_text-x-log-300px.png" height = "300" style = " vertical-align: middle;" / >
< span style = "font-size: 3em; vertical-align: middle;" > =< / span >
< img src = "graphics/saco-800px.png" height = "300" style = " vertical-align: middle;" / >
< h4 > Alice wants to use her sales data to run a promotion< / h4 >
< attribution > (OpenClipArt.org)< / attribution >
< / section >
< section >
< img src = "graphics/matt-icons_text-x-log-300px.png" height = "300" style = " vertical-align: middle;" / >
< span style = "font-size: 3em; vertical-align: middle;" > →< / span >
< img src = "graphics/database-server-800px.png" height = "300" style = " vertical-align: middle;" / >
< h4 > So Alice loads up her sales data in her trusty database/hadoop/spark/etc... server.< / h4 >
< attribution > (OpenClipArt.org)< / attribution >
< / section >
< section >
< img src = "graphics/database-server-800px.png" height = "300" style = " vertical-align: middle;" / >
< span style = "font-size: 3em; vertical-align: middle;" > + ?< / span >
< h4 > ... asks her question ...< / h4 >
< attribution > (OpenClipArt.org)< / attribution >
< / section >
< section >
< img src = "graphics/database-server-800px.png" height = "300" style = " vertical-align: middle;" / >
< span style = "font-size: 3em; vertical-align: middle;" > + ? →< / span >
< img src = "graphics/crystalball-800px.png" height = "300" style = " vertical-align: middle;" / >
< h4 > ... and basks in the limitless possibilities of big data.< / h4 >
< attribution > (OpenClipArt.org)< / attribution >
< / section >
< / section >
< section >
< section >
< h2 > Why is this a fairy tale?< / h2 >
< / section >
< section >
< img src = "graphics/matt-icons_text-x-log-300px.png" height = "300" style = " vertical-align: middle;" / >
< span style = "font-size: 3em; vertical-align: middle;" > →< / span >
< img src = "graphics/database-server-800px.png" height = "300" style = " vertical-align: middle;" / >
< h4 > It's never this easy...< / h4 >
< / section >
< section >
< h2 > Loading Data< h2 >
< small >
< ul >
< li class = "fragment" > Validating and Fixing Outliers< / li >
< li class = "fragment" > Handling Missing Data< / li >
< li class = "fragment" > Matching Schemas< / li >
< li class = "fragment" > Fixing Schemas< / li >
< li class = "fragment" > Managing Stale Data< / li >
< li class = "fragment" > Deduplicating Records< / li >
< li class = "fragment" > ... and lots more< / li >
< / ul >
< / small >
< / section >
< / section >
< section >
< section >
< h2 > Data Cleaning is Hard!< / h2 >
< / section >
< section >
< h3 > State of the Art< / h3 >
< img src = "graphics/BI-Analyst.jpg" height = "400" / >
< attribution > (skilledup.com)< / attribution >
< p > Alice spends weeks cleaning her data before using it.< / p >
< / section >
< section >
< h3 > Newer State of the Art< / h3 >
< img src = "graphics/azure-data-lake.png" height = 500 / >
< attribution > (azure.microsoft.com)< / attribution >
< / section >
< section >
< img src = "graphics/data-lake-to-data-swamp.jpg" height = 500 / >
< attribution > (timoelliott.com)< / attribution >
< / section >
< / section >
< section >
< section >
< h2 > Making Cleaning Easier< / h2 >
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< text x = 0 y = 30 style = "font-size: 0.75em" > Scalability< / text >
< text x = 370 y = 30 style = "font-size: 0.75em" > Reliability< / text >
< text class = "fragment" x = -220 y = 400 style = "font-size: 0.75em" transform = "rotate(-90 20,20)" > Expert Analysis< / text >
< text class = "fragment" x = -220 y = 250 style = "font-size: 0.75em" transform = "rotate(-90 20,20)" > Crowdsourcing< / text >
< text class = "fragment" x = -180 y = 100 style = "font-size: 0.75em" transform = "rotate(-90 20,20)" > Automation< / text >
< / svg >
< p class = "fragment" > Can we start with automation and work our way up?< / p >
< / section >
< / section >
< section >
< h1 > Mimir< / h1 >
< / section >
< section >
< ul >
< li > Automate educated guesses for fast cleaning< ul >
< li > < b > Lenses< / b > : A family of simple data-cleaning operators< / li >
< div class = "fragment shrink fade-out" data-fragment-index = "5" >
< li class = "fragment" data-fragment-index = "1" > ... but what if the guesses are wrong?< / li >
< / div >
< / ul > < / li >
< div class = "fragment shrink fade-out" data-fragment-index = "5" >
< li class = "fragment" data-fragment-index = "2" > Annotate 'best guess' relations with the guesses< ul >
< li > < b > Virtual C-Tables< / b > : A lineage model based on views, labeled nulls, and lazy evaluation.< / li >
< li class = "fragment" data-fragment-index = "3" > ... so now the user needs to interpret your guesses?< / li >
< / ul > < / li >
< li class = "fragment" data-fragment-index = "4" > Rank guesses by their impact on result uncertainty< ul >
< li > < b > CPI< / b > : A greedy heuristic for ranking sources of uncertainty.< / li >
< / ul > < / li >
< / div >
< / ul >
< / section >
< section >
< section >
< h3 > Lenses< / h3 >
< p class = "fragment" data-fragment-index = "1" > Here's a problem with my data. < span class = "fragment" data-fragment-index = "2" > Fix it.< / span > < / p >
< ul >
< li class = "fragment" data-fragment-index = "3" > What types should columns in this table have?
< ul class = "fragment smalltext" data-fragment-index = "7" > < li > Majority Vote of All Castable Types< / li > < / ul > < / li >
< li class = "fragment" data-fragment-index = "4" > How do the columns of these relations line up?
< ul class = "fragment smalltext" data-fragment-index = "7" > < li > Paygo and Countless Other Papers/Systems< / li > < / ul > < / li >
< li class = "fragment" data-fragment-index = "5" > How do I query heterogeneous JSON/XML objects?
< ul class = "fragment smalltext" data-fragment-index = "7" > < li > XMorph and Many Others< / li > < / ul > < / li >
< li class = "fragment" data-fragment-index = "6" > What should these missing values be?
< ul class = "fragment smalltext" data-fragment-index = "7" > < li > Machine Learning + Interpolation< / li > < / ul > < / li >
< ul >
< / section >
< section >
< h3 > Lenses< / h3 >
< p > Each lens implements one automated data repair task with < b > minimal configuration or training< / b > .< / p >
< ul >
< li class = "fragment" > A "SQL" Expression< / li >
< li class = "fragment" > A Model that defines configuration parameters and best-guesses for data repairs.< / li >
< / ul >
< / section >
< section >
< pre > < code >
CREATE LENS PRODUCTS
AS SELECT * FROM PRODUCTS_RAW
USING DOMAIN_REPAIR(DEPARTMENT NOT NULL);
< / code > < / pre >
< ul >
< li > < code > AS< / code > clause defines source data.< / li >
< li > < code > USING< / code > clause requests repairs.< / li >
< / ul >
< / section >
< section >
< div >
< h4 > The Lens Query< / h4 >
< pre > < code >
CREATE VIEW PRODUCTS
AS SELECT ID, NAME, ...,
CASE WHEN DEPARTMENT IS NOT NULL THEN DEPARTMENT
ELSE VAR('PRODUCTS.DEPARTMENT', ROWID)
END AS DEPARTMENT
FROM PRODUCTS_RAW;
< / code > < / pre >
< / div >
< small class = "fragment" >
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< tr > < th > ID< / th > < th > Name< / th > < th > ...< / th > < th > Department< / th > < / tr >
< tr > < td > 123< / td > < td > Apple 6s, White< / td > < td > ...< / td > < td > Phone< / td > < / tr >
< tr > < td > 34234< / td > < td > Dell, Intel 4 core< / td > < td > ...< / td > < td > Computer< / td > < / tr >
< tr > < td > 34235< / td > < td > HP, AMD 2 core< / td > < td > ...< / td > < td class = "fragment" > $Prod.Dept_3$< / td > < / tr >
< tr > < td > ...< / td > < td > ...< / td > < td > ...< / td > < td > ...< / td > < / tr >
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< / section >
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< h4 > The Lens Model< / h4 >
< pre > < code >
SELECT * FROM PRODUCTS_RAW;
< / code > < / pre >
< / div >
< div class = "fragment" >
< div style = "font-size: 1em; vertical-align: middle;" > ↓< / div >
< div >
< img src = "graphics/weka.png" / >
< / div >
< / div >
< div class = "fragment" >
< div style = "font-size: 1em; vertical-align: middle;" > ↓< / div >
< div > < p > An estimator for each < small style = "vertical-align: baseline;" > $Prod.Dept_{ROWID}$< / small > < p > < / div >
< / div >
< / section >
< / section >
< section >
< section >
< h2 > The User's View< / h2 >
< pre > < code >
SELECT NAME, DEPARTMENT FROM PRODUCTS;
< / code > < / pre >
< table class = "fragment" data-fragment-index = "1" >
< tr > < th > Name< / th > < th > Department< / th > < / tr >
< tr > < td > Apple 6s, White< / td > < td > Phone< / td > < / tr >
< tr > < td > Dell, Intel 4 core< / td > < td > Computer< / td > < / tr >
< tr > < td > HP, AMD 2 core< / td > < td class = "fragment highlight-red" data-fragment-index = "2" > Computer< / td > < / tr >
< tr > < td > ...< / td > < td > ...< / td > < / tr >
< / table >
< p class = "fragment" data-fragment-index = "2" > < b > Simple UI:< / b > Highlight values (and rows) based on guesses.< / p >
< / section >
< section >
< pre > < code >
SELECT NAME, DEPARTMENT FROM PRODUCTS;
< / code > < / pre >
< small >
< table >
< tr > < th > Name< / th > < th > Department< / th > < / tr >
< tr > < td > Apple 6s, White< / td > < td > Phone< / td > < / tr >
< tr > < td > Dell, Intel 4 core< / td > < td > Computer< / td > < / tr >
< tr > < td > HP, AMD 2 core< / td > < td style = "color: red;" > Computer< / td > < / tr >
< tr > < td > ...< / td > < td > ...< / td > < / tr >
< / table >
< / small >
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< p class = "fragment" data-fragment-index = "1" > Allow users to < code > EXPLAIN< / code > uncertain outputs< / p >
< p class = "fragment" data-fragment-index = "3" > Explanations include reasons given in English< / p >
< / section >
< section >
< h3 > Other Lenses< / h3 >
< ul >
< li > Schema Matching (equivalently JSON/XML import)< / li >
< li > Archival (how stale is my data?)< / li >
< li > Type Inference< / li >
< li style = "color: grey;" > Deduplication / Entity Resolution< / li >
< li style = "color: grey;" > Schema Name Inference< / li >
< li > And more...< / li >
< / ul >
< / section >
< / section >
< section >
< section >
< h2 > Mimir Demo< / h2 >
2017-08-31 17:18:47 -04:00
< p > < a href = "http://demo.odin.cse.buffalo.edu" target = "_blank" > < img src = "https://odin.cse.buffalo.edu/wp-content/uploads/2015/08/Mimir_Screenshot.png" height = "400" / > < / a > < / p >
2016-02-11 09:37:51 -05:00
< / section >
< section >
< h2 > Intuitive Uncertainty< / h2 >
< p > < b > UB< / b > : Ying Yang, Niccolo Meneghetti, < br / > Arindam Nandi, Vinayak Karuppasamy, < br / > Oliver Kennedy, Jan Chomicki< / p >
< p > < b > Oracle< / b > : Ronny Fehling, Zhen-Hua Liu, Dieter Gawlick< / p >
< h4 > Thanks to Oracle for multiple gifts that make this research possible< / h4 >
< / section >
< / section >
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