Generic Model Management: Experiences and Open Questions

Transcription

1 Geeric Model Maagemet: Experieces ad Ope Questios Sergey Melik Leipzig Uiversity / Staford Uiversity Supervisor: Erhard Rahm Goal: Reduce amout of programmig for buildig metadata-drive applicatios Model Maagemet: Geeral-purpose system for maagig complex models Algebraic operatios to maipulate metadata i large chuks Thesis questios: Ca model maagemet be doe i a geeric fashio? Does geeric model maagemet offer practical beefits?

2 ORDERS PRODUCTS Brad Sample sceario: Data Traslatio Source schemas (relatioal): Quatity UitPrice Discout ORDERS ShipDate FreightCharge Rebate PRODUCTS Quatity Price Export schemas (XML schema): PurchaseOrder Product Brad Quatity UitPrice Discout PurchaseOrder Product Quatity UitPrice ShipDate FreightCharge Rebate maually automatically! operator PropagateChages(rdb1, rdb2, xsd1, xsd1_xsd1 ) rdb1_rdb2 = Match(rdb1, rdb2, NGram(rdb1, rdb2)) (xsd1, rdb1_xsd1) = SQLDDL2XSD(rdb1) (xsd2, rdb2_xsd2) = SQLDDL2XSD(rdb2) xsd1_xsd2 = Match(xsd1, xsd2, Ivert(rdb1_xsd1) rdb1_rdb2 rdb2_xsd2) xsd1 = Delete(xsd1, Rage(RestrictDomai(xsd1_xsd1 ), All(xsd1) Domai(xsd1_xsd2))) xsd2_add = All(xsd2) Rage(xsd1_xsd2) xsd2 = Select(xsd2, xsd2_add) xsd2 = Delete(xsd2, xsd2_add) xsd2 _xsd1 = RestrictDomai(Ivert(xsd1_xsd2) xsd1_xsd1 ), All(xsd2 )) xsd3 = Merge(xsd2, xsd1, xsd2 _xsd1 ) xsd2_xsd3 = (Ivert(xsd1_xsd2) xsd1_xsd1 ) Id(xsd2_add) retur (xsd3, xsd2_xsd3) rdb1 rdb2 xsd1 xsd2 xsd2 xsd2 xsd1 xsd1 xsd3 2

3 Operators ad Data Structures Data structures: Model: directed labeled graph w/ OIDs ad literals Selector: set of OIDs Mappig: (weighted) biary relatio o OIDs Primitive operators: CREATE TABLE Persoel ( Po it, Pame strig, Dept strig,...) Derived operators: Table type &1 ame Persoel colum colum colum... Colum ColumType type ame Po type &2 &3 SQLtype ame type type ame Pame &4 &5 SQLtype ame type ame Dept &6 SQLtype it strig Domai(map): set of OIDs that are i the domai of map RestrictDomai(map, selector): mappig w/ domai restricted by selector Id(selector): idetity mappig Ivert(map): iverts a mappig Compose(map1, map2): compositio of mappigs TrasitiveClosure(map): returs the trasitive closure of map All(M): set of all OIDs used i model M,, : set operators Subgraph(M, selector): subgraph of M iduced by the odes i selector operator Reachable(selector, map) retur Rage(RestrictDomai(TrasitiveClosure(map), selector)) operator Select(M, selector, depedecies) retur Subgraph(M, selector Reachable(selector, depedecies)) operator DeleteSoft(M, selector, depedecies) caotbedeleted = Reachable(All(M) selector, depedecies) todelete = selector caotbedeleted tokeep = All(M) todelete retur Select(M, tokeep, depedecies) operator DeleteHard(M, selector, depedecies) todelete = selector Reachable(selector, Ivert(depedecies)) tokeep = All(M) todelete retur Select(M, tokeep, depedecies)... Semiautomatic operators: Laguage-specific operators: operator Match(M1, M2, map12): correspodeces betwee elemets operator Merge(M1, M2, map12): use map12 for glueig M1 ad M2 DepedeciesSQL(M), DepedeciesXSD(M): model elemets required for cosistecy, e.g., field table 3

4 Matchig: Similarity Floodig Algorithm Sample graphs: Fixpoit computatio o propagatio graph: 0.33 wags wags wags logertha logertha logertha 0.69 Ituitio: similar objects have similar cotext Basic formula: σ i+1 =ormalize(σ i +ϕ(σ i )), with similarity vector σ i, iteratio i Correspods to eigevector computatio σ i+1 = λ i Μσ i Filterig of results exploits stable marriage property Accuracy metric: 1 wrog + missig 1 = Recall (2 ) correct Precisio S. Melik, H. Garcia-Molia, E. Rahm: Similarity Floodig: A Versatile Graph Matchig Algorithm ad its Applicatio to Schema Matchig, ICDE 2002 (best studet paper award) 4

5 GEMMYS: A Geeric Model Maagemet System Browser Import/export Scriptig Editors Catalogs Model Maager Cachig ad Distributio Match Compose Merge... Otologet s product oly Model Likage Model Persistece graphs SQL tables graphs files SQL DBMS File System 5

6 Idustrial settig: Otologet,Ic. Goal: Study deploymet aspects of GMM Product: automated provisioig ad support of telecommuicatio devices ad services Challeges: Multitude of equipmet vedors Devices with icompatible iterfaces ad varyig capabilities Approach: Represet device specificatios i machie-readable form Maage all metadata uiformly Uses idustry-tailored variat of GEMMYS Verificatio & aalysis tools Compiler Browser Editors Provisioig system (10, 10, blue) (5, 20, gree) Presetatio metadata UML diagram Device Switch Router TCP/IP Author: Versio: Date: Catalog metadata Digital sigature: Pricipal: Security metadata 6

7 Ogoig work Model Shuffler : Simulates evolutioary chages of models Helps clarify sematics of operators Used for quatifyig beefits of GMM i typical scearios Scearios: Model evolutio Schema itegratio Data traslatio 3-way merge (reitegratio) Reverse egieerig Ope questios: rdb Maipulatio of complex mappigs (SQL views, XSLT, scripts) Ordered relatioships i models Istace data trasformatio DB backed operator executio, optimizatio Impedace mismatch, GUI support rdb rdb ORDERS PRODUCTS Brad rdb Quatity UitPrice Discout xsd 1 rdb 1 rdb 2 rdb shuffle! rdb xsd Quatity ItemCost Rebate Cliet rdb xsd 7