Presentation is loading. Please wait.

Presentation is loading. Please wait.

Potter’s Wheel: An Interactive Data Cleaning System Vijayshankar Raman Joseph M. Hellerstein.

Similar presentations


Presentation on theme: "Potter’s Wheel: An Interactive Data Cleaning System Vijayshankar Raman Joseph M. Hellerstein."— Presentation transcript:

1 Potter’s Wheel: An Interactive Data Cleaning System Vijayshankar Raman Joseph M. Hellerstein

2 Outline Background Potter’s Wheel architecture Discrepancy detection Interactive transformation Conclusions and Future Work

3 Motivation Dirty data common E.g., in content integration, e-catalogs Inter-organizational differences in data representation Home Depot: 60,000 suppliers! Data often scraped off web pages, etc. E.g. in centralized systems Data entry “errors”, poor integrity constraints Cleansing a prereq for analysis, xactions Cleansing done by “content managers” Ease of use critical! Standards can help a bit (e.g. UDDI) But graphical tools are the name of the game

4 Current solutions Detect errors in data “eyeball” data in a spreadsheet data auditing tools domain-specific algorithms Code up transforms to fix errors “ETL” (extract/transform/load) tools from warehousing world string together domain-specific cleansing rules scripting languages, custom code, etc. Apply transforms on data Iterate special cases nested discrepancies, e.g /10/31 Code Apply Detect

5 Problems Slow, batch tasks Significant human effort! Specification of transforms regular expressions, grammars, custom scripts, etc. Discrepancy detection notion of discrepancy domain-dependent want a mix of custom and standard techniques want to apply on parts of the data values Rebeccaby Daphne du Maurier (Mass Market Paperback)$6.29**** Sonnet 19.Craig W.J., ed The Oxford Shakespeare The Big FourAgatha Christie, Mass market paperback5.3910% (from bartleby.com, bn.com)

6 Outline Background Potter’s Wheel architecture Discrepancy detection Interactive Transformation Conclusions and Future Work

7 Potter’s Wheel: Design Goals Eliminate wait time during each step Even on big data! Use Online Reordering (VLDB ‘99), sampling Ensure transform results can be seen/undone instantly Compile/optimize sequence of transforms when happy Eliminate programming, but keep user “in the loop” Semi-automatic, “direct manipulation” GUI Support & leverage “eyeball” detection, verification (human input) Point-and-click transformation “by example” Unify detection and transformation Detection always runs online in the background Detection always runs on transformed “view” of data Extensibility Domain experts (vendors) should be able to plug in detectors/transforms A mixed (“Systems!”) design challenge: Query Processing, HCI, Learning Limited appreciation for this kind of systems work

8 Potter’s Wheel UI Data read so far

9 Dataflow in Potter’s Wheel Transformation engine Spreadsheet display Optimized program Online reorderer Data source Discrepancy detector compile get page  scrollbar pos.n specify/undo transforms scroll check for errors

10 Outline Background Potter’s Wheel architecture Discrepancy detection Domains in Potter’s Wheel Structure inference Interactive Transformation Conclusions and Future Work

11 Discrepancy Detection Challenge: find discrepancies in a column Structure inference: Given: A set of (possibly composite) data items, including discrepancies A set of user-defined “domains” (atomic types) Choose a “structure” for the set A string of domains (w/repetition) that best fits the data E.g. for “March 17, 2000”:  * alpha* digit*, digit* [Machr]* 17, int Report rows that do not fit chosen domain PS: Must be an online algorithm!

12 Extensible Domains As in Object-Relational, keep domains opaque. class Domain { // Required inclusion function boolean match(char *value); // Helps in structure extraction int cardinality(int length); // For probabilistic discrepancy checking float matchWithConfidence(char *value, int dataSetSize); void updateState(char *value); // Helps in parsing boolean isRedundantAfter(Domain d); } e.g. integer, ispell word, money, standard part names

13 Evaluating Structure Fit Three desired characteristics Recall match as many values as possible Precision flag as many real discrepancies as possible e.g. Month day, day over alpha* digit*, digit* Conciseness avoid over-fitting examples, make use of the domains e.g. alpha* digit*, digit* over March 17, 2000

14 Evaluating Structure Fit, cont. Given structure S = d 1 d 2 …d p, string v i, how good is S? Minimum Description Length (MDL) principle Rissanen, ‘78, etc. DL(v i,S) = length of theory for S + length to encode string v i with S Computing DL(v,S) 1) Length of theory = p log (number of domains known) 2) If v i doesn’t match S, encode it explicitly 3) Else encode v i = w i,1 w i,2 …w i,p where w i,j  d j Encode length of each w i,j Encode each w i,j among all d j ’s of length j use cardinality function DL = AVG i ((1) + (2) + (3)) = AVG i (UnConciseness + UnPrecision + UnRecall) Choose structure with minimum DL(v,S) Hard search problem; heuristics in paper

15 Potter’s Wheel UI

16 Outline Background Potter’s Wheel architecture Discrepancy detection Interactive Transformation transforms split-by-example Conclusions and Future Work

17 Interactive transformation Sequence of simple visual transforms rather than a single complex program Each transform must be easy to specify immediately applicable on screen rows Must be able to undo transforms compensatory transforms not always possible everything REDO-oriented at display-time no need for UNDO!

18 Transforms in Potter’s Wheel Value translation Format(value) – reg. expr. substitution, arithmetic ops, … One-to-one row mappings Add/Drop/Copy columns Merge,Split columns Divide column by predicate One-to-many row mappings Fold columns adapted from Fold of SchemaSQL[LSS’96] Resolve some higher-order differences

19 Example (1) 2 Merges Format '(.*), (.*)' to '\2 \1' Stewart,Bob Dole,Jerry Davis Marsh Anna Joan Stewart AnnaDavis Dole JoanMarsh Jerry Bob Jerry Stewart Dole Anna Joan Davis Marsh Split at ' ' Anna Joan Davis Marsh Bob Stewart Jerry Dole

20 Example (2) Divide (like ’.*,.*’) AnnaDavis JoanMarsh Stewart,Bob Dole,Jerry Stewart,Bob Anna Dole,Jerry Joan Davis Marsh

21 Example (3) Split Fold 2 Formats (demotes) Ann Bob Math:43 Math:96 Bio:78 Bio:54 NameMath Ann Bob Bio Ann Bob Ann Name Math:96 Bio:54 Math:43 Bio:78 Math Bio Math Bio Ann Bob Name Name

22 Power all one-to-{one,many} row mappings interactive many-to-{one,many} mappings hard to do interactively must find/display companion rows for each row to transform higher-order transforms Specification click on appropriate columns and choose transform but, Split is hard important transform in screen-scraping/wrapping need to enter regular expressions not always unambiguous e.g. want to leverage domains Taylor, Jane, $52,072 Tony Smith, 1,00,533 Transforms summary

23 Split by Example User marks split positions on examples System infers structure, then parses rest Parsing must identify matching substrings for structures multiple alternate parses could work search heuristics explored in paper DecreasingSpecificity seems good Taylor, Jane |, $52,072 Tony Smith |, 1,00,533 infer structures,

24 Related Work Transformation languages -- e.g. SchemaSQL, YATL Data cleaning tools commercial -- ETL and auditing tools research -- e.g. AJAX, Lee/Lu/Ling/Ko ’99 Custom auditing algorithms de-duplication (e.g. Hernandez/Stolfo ’97) outlier detection (e.g. Ramaswamy/Rastogi/Shim ’00) dependency inference (e.g. Kivinen/Manilla ’95) Structure extraction techniques e.g. XTRACT, DataMold, Brazma ‘94 Transformation tools text-processing tools – e.g. perl/awk/sed, LAPIS screen-scraping -- e.g. NoDoSE, XWRAP, OnDisplay, Cohera Connect, Telegraph Screen Scraper (TeSS) Middleware, schema mapping

25 Conclusions Interactive data cleaning Couple transformation and discrepancy detection Perform both interactively short, immediately applied steps specify visually, undo if needed contrast with declarative language Parse values before discrepancy detection user-defined domains helpful Software online (http://control.cs.berkeley.edu/abc)http://control.cs.berkeley.edu/abc

26 Looking Ahead Generalizing transform by example Transforming nested data (XML, HTML) More complex domain-expressions Extend to generalized query processor client in Telegraph specify initial query refine by specifying transforms as results stream in dynamically choose transforms to be pushed into server See Shankar’s upcoming thesis, Telegraph papers

27 Backup Slides

28 Optimization of Transform Sequences In Potter’s Wheel system generates program at end hence opportunities for optimization remove redundant operations avoid expensive memory copies/allocations/deallocations by careful pipelining materialize intermediate strings only when necessary up to 110% speedup for C programs C programs 10x faster than Perl scripts

29 Example vs


Download ppt "Potter’s Wheel: An Interactive Data Cleaning System Vijayshankar Raman Joseph M. Hellerstein."

Similar presentations


Ads by Google