Multi-column Substring Matching. Schema Translation (And other wild thoughts while shaving)
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1 For Dtbse Schem Trnsltion (And other wild thoughts while shving) Robert H. Wrren 1 Dr. Frnk Wm Tomp 1 1 rhwrren,fwtomp@uwterloo.c Dvid R. Cheriton School of Computer Science University of Wterloo Wterloo, Cnd Bertinoro PhD School on Dt nd Service Integrtion, 2006
2 Where in the world m I from? (Se Monsters here.) Wterloo, Cnd You re here.
3 Dtbse Integrtion But! seen s lrge, monolithic, one-off project. solved by dtbse nd domin experts with the time nd motivtion. The number, size nd complexity of dtbses keeps growing. (+10,000 tbles, +1,600 columns) Integrtion is n every dy issue. (Semntic web, opportunistic dt sources) Multiple representtion stndrds in use. (22 Locles) Stndrdized dtbse ccess (JDBC, ODBC) possible. End user knows the dt is vilble, cn t ccess it nd wnts it right NOW!
4 Dtbse Integrtion But! seen s lrge, monolithic, one-off project. solved by dtbse nd domin experts with the time nd motivtion. Need utomtion to del with this problem. The number, size nd complexity of dtbses keeps growing. (+10,000 tbles, +1,600 columns) Integrtion is n every dy issue. (Semntic web, opportunistic dt sources) Multiple representtion stndrds in use. (22 Locles) Stndrdized dtbse ccess (JDBC, ODBC) possible. End user knows the dt is vilble, cn t ccess it nd wnts it right NOW!
5 Dtbse schem mtching nd trnsltion Objective A generlisble method cpble of resolving complex schem mtches nd the trnsltion required to convert the instnce dt using substrings conctention. Exmple Nme(Wrren, Rob) in dtbse D First(Rob) + Lst(Wrren) in D 2005/05/29 in dtbse D in dtbse D LstNme(wrner) + Birthdte(980102) in D Userid(wrn98) in D. PrtNumber( ) in D Number(0435) +PlntId(03) + Yer(2006) in D.
6 Problem formliztion Definition For given trget dtbse tble T 2 with trget column A nd source tble T 1 with set of likely source columns (B 1, B 2,, B n ) Find trnsformtion such tht: A = ω 1 + ω ω ν Where ω i represents substring of column B i Trnsltion model t = t [β x 1y β x 2y β xνyν ν ] (chrs x ν y ν of col B ν )
7 Bsic exmple (Source) Tble 1 (Trget) Tble 2 first robert kyle norm my josh john middle h s l l j lst kerry norm wisemn cse ldermn mlton j??? Login nwisem jlmlton rhkerry lcse ksokmon ksnormnj
8 Bsic exmple (Source) Tble 1 How to infer trnsltion? (Trget) Tble 2 select substring(first from 1 for 1) first middle lst??? Login substring(middle from 1 for 1) lst s login robert h kerry into trget_tble from kyle s norm norm wisemn Solution: my Itertively l select substrings cse from best-fit lcse josh columns while performing ldermn simple form of john record linkge. l j mlton j nwisem jlmlton rhkerry ksokmon ksnormnj
9 Bsic exmple - Find initil column. (1) (Source) Tble 1 (Trget) Tble 2 first robert kyle norm my josh john middle h s l l j lst kerry norm wisemn cse ldermn mlton j??? Login nwisem jlmlton rhkerry lcse ksokmon ksnormnj
10 Bsic exmple - Find prtil trnsltion. (1) (Source) Tble Generte cndidte trnsltions 1 mlton + wisemn %[1-2]% mlton first+ jlmlton middle %[1-EOL] lst??? mlton robert + lcse h %[2-3]% kerry kyle s norm kerry norm + rhkerry %[1-EOL] wisemn Highest myoccurrence: l %lst[1-eol] cse josh ldermn john l j mlton j (Trget) Tble 2 Login nwisem jlmlton rhkerry lcse ksokmon ksnormnj Use string edit distnce to crete cndidte trnsltion.
11 Bsic exmple - Serch for dditionl columns. (1) (Source) Tble 1 (Trget) Tble 2 first robert kyle norm my josh john middle h s l l j lst kerry norm wisemn cse ldermn mlton j??? Login nwisem jlmlton rhkerry lcse ksokmon ksnormnj Smple the tuples formed from trnsltion formul.
12 Bsic exmple - Serch for dditionl columns. (1) (Source) Tble Generte cndidte trnsltions 1 (Trget) Tble 2 robert + rhkerry first[1-1]%lst[1-eol] first robert middle h lst kerry??? Login nwisem (Keep kyle trck of s ll cndidtes normnd their frequencies.) jlmlton norm my josh john l l j wisemn cse ldermn mlton j rhkerry lcse ksokmon ksnormnj Smple the tuples formed from trnsltion formul.
13 Bsic exmple - Serch for dditionl columns. (2) (Source) Tble 1 (Trget) Tble 2 first robert kyle norm my josh john middle h s l l j lst kerry norm wisemn cse ldermn mlton j??? Login nwisem jlmlton rhkerry lcse ksokmon ksnormnj Smple the tuples formed from current trnsltion formul
14 Bsic exmple - Serch for dditionl columns. (2) (Source) Tble Generte cndidte trnsltions 1 (Trget) Tble 2 h + rhkerry first[1-1]middle[1-1]lst[1-eol] first robert middle h lst kerry??? Login nwisem (Keep kyle trck of sll cndidtes norm nd their frequencies.) jlmlton norm my josh john l l j wisemn cse ldermn mlton j rhkerry lcse ksokmon ksnormnj Smple the tuples formed from current trnsltion formul
15 Bsic exmple - Serch for dditionl columns. (2) (Source) Ending condition Tble 1 (Trget) Tble 2 No unknowns remin within: first middle lst??? Login robertt = ht [β x 1y 1 kerry 1 + β x 2y βν xνyν nwisem ] kyle s norm jlmlton norm Login = first[1-1] wisemn + middle[1-1] + lst[1-eol] rhkerry my josh john l l j cse ldermn mlton j lcse ksokmon ksnormnj Smple the tuples formed from current trnsltion formul
16 Experimentl setup - Noise column Add nd populte the following noise columns: A rndom RFC-2822 timestmp. A rndom street ddress. A rndom long integer. A rndom vlue, vrible length string. Wr nd pece by Leo Tolstoy. Definition Simulte noisy mtching environment nd ensure proper lgorithmic behvior.
17 Citeseer & DBLP Dtset Citeseer Extrcted 526,000 records from OAI dump. Creted Title, Yer nd Author (15) columns. Creted Cittion column from Title, Yer nd First Author. (Successfully mtched t 1% smpling.) DBLP Extrcted 233,000 records from web dump. Creted Title, Yer nd Author (15) columns. Creted Cittion column from Title, Yer nd First Author. (Successfully mtched t 1% smpling.)
18 Cross Citeseer nd DBLP Dtset trnsltion Expected result Mtch Citeseer Cittion column to DBLP source tble. Only 714 records mtch cross Title, Yer nd First Author. Actul result Citeseer Cittion = DBLP Title + DBLP Yer + DBLP Second Author. 378 cittions hve their First nd Second uthors reversed! Returned mpping is correct ccording to the dt.
19 Incrementl wll-clock performnce Step 1 10 Step st Itertion Mins. 2nd itertion Percentge of Citeseer dt processed Estimted complexity O(w n s 1 s 2 )
20 Overll Reserch Motivtion Previous pproches required specilized domin specific mtchers to form both the mtch nd the trnsltion. This lgorithm is generlized lgorithm for string-bsed conctentions mtches. Ment to function s prt of lrger dtbse integrtion frmework. It is un-supervised, does not need exmples or known record overlp nd cn be implemented using bsic SQL sttement.
21 Overll Reserch Motivtion Previous This ispproches ll old stuff!! required Now wht? specilized domin specific mtcherssmpling to form both extremely the mtch lrgend tbles. the trnsltion. This lgorithm Dt-driven, is generlized mchine redble lgorithm for string-bsed conctentions descriptions mtches. of extremely lrge dtbse. Ment toquesting function for s prt liner oftime lrger dt dtbse mtching integrtion of frmework. ttributes. It is un-supervised, Using ontologies does not s integrtion need exmples negotition or known record overlp documents. nd cn be implemented using bsic SQL sttement.
22 Smpling extremely lrge tbles or MY dtbse is bigger tht YOUR dtbse. Currently two pproches: equidistnt nd rndom smpling. As the size of tble grows, trversl cn become lmost impossible. Behvior is like tht of dt strem or tpe drive. Disk ccess cn lso be more efficient if we use it s liner device. Cn we use clever sttistics to guess how deep into the tble to go?
23 Dt-driven, mchine redble descriptions of extremely lrge dtbse. Q: How re we going to dvertise nd describe dt in formt tht utomted integrtion system cn ctully use. (And mybe not lie bout?)
24 Questing for liner (or better) time dt mtching of ttributes. Currently two methods: q-grm or KL-divergence. Wht hppens when we cn t red the entire tble? Are informtion theory methods useful? nd fster? Cn we use ny clever smpling techniques?
25 Using ontologies s integrtion negotition documents. mostly used for hierrchy of concepts now. Ontologicl stndrds good wy to document the dt exchnge process (e.g.: constrints, dependencies, ) in mchine redble wy. Wht bout pushing dtbse informtion rules to the outside world? (e.g.: Records re only ccepted is they contin ttributes (First, Middle nd Lst) or if the ttributes (Nme nd DOB) re vilble.
26 THE END
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