Modelling Dynamic Web Data

Sergio Maffeis Joint work with Philippa Gardner Modelling Dynamic Web Data APPSEM'04 Tallinn, 15 April 2004

Motivation

What is dynamic Web data? We model large scale, peer-to-peer systems for sharing dynamic data over the Web. • Distribution is on large scale • Each site provides and consumes data using a (standardised) set of functionalities • Data is dynamic and intensional: can contain calls to Web services, forms, scripted code, etc... • Data is interlinked

Modelling dynamic Web data Existing models: • query languages for semistructured data (XML) • Describe data manipulation • Do not include a distribution layer • process calculi for the distributed infrastructure • Are good at orchestrating data exchanges between peers • Tend to abstract from the actual data Modelling dynamic Web data requires merging these approaches.

A unified framework Reasoning about data and the distributed infrastructure in the same framework provides means to • understand the system behaviour • give schema/types to documents containing scripts • control access to resources • propose new optimisations

The Xd project • Dynamic Web data in Xd • syntax, sematics, examples • Observational equivalences • spectrum of network and process equivalences • Proof techniques • domain bisimilarity • Types and security • access control, data validation • Implementation • declarative platform for intensional data and WS-coordination

Dynamic Web data in Xd

L2 L1 L3 Xd Processes Trees L4 • A flat space of locations • Locations contain (XML) trees and coordination processes

Representing data in Xd a a a a c c b Processes c b Processes T3 @L1:a/c P T’ T2 T1 L2 L1 • Unordered, edge-labelled trees • Scripted processes (no reflection) • Pointers (links)

T’ P’ L1 Data manipulation: cut = cuta/e(X).pastea/c<X> a a c P b c b e Q T L2 • select some subtrees with a path expression

T’ P’ L1 Data manipulation: cut X= = cuta/e(X).pastea/c<X> T a a c P b c b e Q T L2 • select some subtrees with a path expression • match the subtrees against a binding pattern

T’ P’ L1 Data manipulation: cut X= = cuta/e(X).pastea/c<> T T a a c P b c b e Q T L2 • select some subtrees with a path expression • match the subtrees against a binding pattern • cut away the subtrees

T’ P’ L1 Data manipulation: paste = cuta/e(X).pastea/c<> T a a c P b c b e T Q L2 • select some paths for pasting

T’ P’ L1 Data manipulation: paste = cuta/e(X).pastea/c< > T a a c P b c b e Q T L2 • select some paths for pasting • paste the subtrees in place

T’ P’ L1 Data manipulation = cuta/e(X).pastea/c< > T a a c P b c b e Q T L2 • Encode cut, copy, paste by general update command • Simple path expressions as query language (multiple selection) • Other choices possible

a a c b c b e T Process selection = runc a a c P R b c b e Q L1 L2 runactivates scripts: click on a hyperlink, system event, ...

a a c b c b e T • = intuitive specification of a hyperlink Process migration = go L2.copya/c(X).go L1.pastea/e<X> a a c P R b c b e Q L1 L2

= intuitive specification of a hyperlink Process migration = go L2.copya/c(X).go L1.pastea/e< > T a a a a c c P R b b c b c b e T e Q T L1 L2

= intuitive specification of a hyperlink • over-simplified Process migration = go L2.copya/c(X).go L1.pastea/e<X> a a a a c c P R b b c b c b e e Q T T L1 L2

Communication = web service call = web service = result handling code = service instance a a a a c c P R b b c b c b e e Q T L1 L2

Communication = go L2.get<a/c,L1> = !get(x,y).copyx(z).go y.put<z> = put(x).pastea/e<x> a a a a c c P R b b c b c b e e Q T L1 L2

Communication = go L2.get<a/c,L1> = !get(x,y).copyx(z).go y.put<z> = put(x).pastea/e<x> = copya/c(z).go L1.put<z> a a a a c c P R b b c b c b e e Q T L1 L2

Communication = go L2.get<a/c,L1> = !get(x,y).copyx(z).go y.put<z> = put(x).pastea/e<x> T = copya/c(z).go L1.put< > a a a a c c P R b b c b c b e e T Q T L1 L2

Communication = go L2.get<a/c,L1> = !get(x,y).copyx(z).go y.put<z> = put(x).pastea/e<x> T = copya/c(z).go L1.put< > a a a a c c P R b b c b c b e T e Q T L1 L2

Communication = go L2.get<a/c,L1> = !get(x,y).copyx(z).go y.put<z> = put(x).pastea/e< > T = copya/c(z).go L1.put< > T a a a a c c P R b b c b c b e T e Q T L1 L2

Communication = go L2.get<a/c,L1> = !get(x,y).copyx(z).go y.put<z> = put(x).pastea/e< > T = copya/c(z).go L1.put< > T a a a a c c P R b b c b c b e e Q T T L1 L2 Potential problem: who can return a result to put?

Restriction = go L2.get<a/c,L1> = !get(x,y).copyx(z).go y.put<z> = put(x).pastea/e<x> a a a a c c P R b b c b c b e e Q T L1 L2

Restriction = go L2.get<a/c,L1,put> = !get(x,y,w).copyx(z).go y.w<z> = put(x).pastea/e<x> a a a a c c P R b b c b c b e e Q T L1 L2 • Add the return channel put as a parameter to get

Restriction = go L2.get<a/c,L1,put> = !get(x,y,w).copyx(z).go y.w<z> = put(x).pastea/e<x> = (new put)( | ) a a a a c c P R b b c b c b e e Q T L1 L2 • Add the return channel put as a parameter to get • Make put an unforgeable secret with restriction binder (new put)(-)

Restriction = go L2.get<a/c,L1,put> = !get(x,y,w).copyx(z).go y.w<z> = put(x).pastea/e<x> = (new put)( | ) a a a a c c P R b b c b c b e e Q T L1 L2

Restriction = go L2.get<a/c,L1,put> = !get(x,y,w).copyx(z).go y.w<z> = put(x).pastea/e<x> = (new put)( | ) a a a a c c P R b b c b c b e e T Q T L1 L2

Restriction = go L2.get<a/c,L1,put> = !get(x,y,w).copyx(z).go y.w<z> = put(x).pastea/e<x> = (new put)( | ) a a a a c c P R b b c b c b e T e Q T L1 L2

Restriction = go L2.get<a/c,L1,put> = !get(x,y,w).copyx(z).go y.w<z> = put(x).pastea/e<x> = (new put)( | ) a a a a c c P R b b c b c b e e Q T T L1 L2 • Nothing can interefere with put now • Interference with get at L2: same technique, types, …

Function repository Data Repository Data warehouse Example: rendez-vous Client L2 L1 L3 L4

Function repository Data Repository Data warehouse Example: rendez-vous Client L2 L1 L3 L4 • Reduce network traffic • Comply to access control policies • Forward streams of results

Modelling Dynamic Web Data

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