© daniel s. weld, planet 2003 tutorial on data integration planning for the web ii execution &...

© Daniel S. Weld, PLANET 2003 Tutorial on Data Integration

Planning for the Web IIExecution & Service

IntegrationDan Weld

University of WashingtonJune, 2003

2 © Daniel S. Weld, PLANET 2003 Tutorial on Data Integration

Acknowledgements

• Oren Etzioni• Yolanda Gil• Keith Golden• Alon Halevy• Zack Ives• Tal Shaked

Caveat


Outline• Execution for Data Integration

Coping with incomplete statistics, latency Interleaved planning & execution Convergent query processing

• Service Integration Web service composition

• Background• Representational issues• Planning algorithms

Automated data analysis


Optimization and Execution

• Problem: Few and unreliable statistics about the data. Unexpected (possibly bursty) network

transfer rates. Generally, unpredictable environment.

• General solution: (research area) Adaptive query processing. Interleave optimization and execution. As you

get to know more about your data, you can improve your plan.


Adaptivity & Incremental Processing Query Performance

QueryTranslation

User's

Query

Query overSources

Que

ry R

esul

ts

Tukwila Network-BasedQuery Processor

Evaluated within the Tukwila system

[Ives PhD]


Query Optimization: Model Query Plans’ Execution &

Choose the Best

op

op

op

op

Restock (R) 100 tuples

Orders (O)50 tuples

Shipping (S)90 tuples

Restock (R)100 tuples

Orders (O)50 tuples

Shipping (S)90 tuples

From source sizes, stats, estimate result sizes, costs

RO~30 tuples

ROS~270 tuples

50 sec

ROS~270 tuples

30 sec

OS~15 tuples

Estimates, assumptions introduce error: Exponential increase in estimation error with

each join [Ioannidis & Christodoulakis 91] [Antoshekov 93,96]

Worse if no detailed statistics


Why Does Data Integration Make Optimization Harder?

Query optimization estimates costs using knowledge about environment and data:

Data source sizes (“cardinalities”)Often unavailable or not meaningful in data

integration Histograms

Too expensive to maintain in data integration I/O costs

Network I/O costs fluctuate

Need a way to gain this sort of knowledge!


Some Solutions

1. Adaptive operators2. Mid query reoptimization3. Convergent query processing4. Query scrambling [Franklin et al.]5. Eddies [Hellerstein et al.]


Optim izer

(Re-)Optim izer

Mem Alloc-Fragm enter

ExecutionEngine

Tem p Store

EventHandler

QueryOperators

Reform ulator

Catalog

source mappings

querylogical

planexecplan

answ er

data

execresults

Tukwila Data Integration System

Novel components: Event handler Optimization-execution loop Adaptive operators


Hybrid Hash Join No output until build relation

read Asymmetric (build vs. probe)

— optimization requires source behavior knowledge

Double Pipelined Hash Join Outputs data immediately Symmetric — requires less

source knowledge to optimize Threads overlap I/O,

computation

Double Pipelined Join


Tim

e (

sec)

Tuples Output (1000s)

Join of 3 tables sent via JDBC over 10Mb Ethernet: TPC-H Lineitem Supplier Order

Performance on Networked Data


Benefits:Easier to optimize (symmetric)Sub-operations scheduled flexiblyAllows overlap of I/O and computation

Incurs some overhead: Threading, queues Required extensions to intelligently handle

overflow:• Same hash function, number of buckets for each side• Approaches: flush buckets on left side or flush

symmetrically

Double Pipelined Join in Summary


Some Solutions

1. Adaptive operators2. Mid-query reoptimization

• Interleaved planning and execution

3. Convergent query processing4. Query scrambling5. Eddies


Mid-query reoptimization

BA

C

D C D

AB

Materialization Point: write AB to disk

If actual predicted statistics replan[Kabra & DeWitt]


Some Solutions

1. Adaptive operators2. Mid query reoptimization3. Convergent query processing4. Query scrambling5. Eddies


Convergent Query Processing

• Instead of adapting remainder of plan after executing all data on plan prefix

• Adapt whole plan after executing whole plan on part of data

• Can better gather information this way…


Convergent Query Processing in Action: Changing Join Plans

in Mid-Stream(R O S)

“Cleanup” query plan

Join Restock, Orders, Shipping

ROS

RS

R1

O1 S1

O1S1

R1 O1S1

R2 O2S2

R2O2

R0 O0S0

R0 S0


Breaking a Join into Phases: One Subset per Table, Each

PhaseRestock (R) Orders (O)

R1 O1

Phase 1

R0 O0Phase 0

O1

O0

CleanupPhase

ncnc

cm

cm

m

mTTTT

1,...,1

11

1

1)...(...


The Cleanup Plan Reuses Previous

Work Where PossibleRestock Orders Shipping

R0 O0S0

R1

O1 S1

O1S1

R1 O1S1

R2O2

R0 S0

R2

S2

O2R1

S1

O1

S0

R0 O0

R2

S2

O2

R2O2

R1

S1

O1

S0

R0 O0

Exclude R2O2

Exclude R0S0O0, R1S1O1, R2S2O2,

R2 O2S2


CQP on a 100Mbps LAN: Nearly “Optimal”

Performance 866MHz P-III, 256MB buffer pool, re-optimization every 10sec

cost to parse XML


Slow WAN, Faster CPU: CQP Reduces Work

1GHz P-III, 256MB, re-optimization every 10sec. 1Mbps network, RTT ~50msec





•Background•Representational issues•Planning algorithms



What is a Web Service• A web service is a network accessible interface to application

functionality, built using standard Internet protocols (TCP/IP, XML, SOAP, WSDL… Clients of a web service do NOT need to know how it is implemented.

• Why interesting? Increased automation

Application

client

Application

codeNetwork Web

Service


Case Study: Amazon

• Services Exported Product details (short, long, images, samples) Purchase functionality Ratings, reviews, collaborative filtering data, lists, …

• Examples Store builder tools Amazon Browser – visualization tool Windows desktop interfaces – drag-n-drop… MP3 Piranha Games Automatic review writer??


Case Study: Google

• Services Exported Search interface Limits on items returned, queries / day

• Examples Metacrawler functionality Geosearch ‘nearby thai restaurants’

• TIGER, FIPs -> lat,long of pages

Robust hyperlinks• Creates a signature for destination pages & tracks with query


Case Study: Fed Express• Shipment tracking• Proof of delivery• Invoice reviewed, adjusted, settled• Schedule pickup time, location

Outgoing or returns• Order supplies (airbills, envelopes, boxes)• Review shipping history• Rate requests

Location, package size• International trade

Required documents, duties, taxes


Case Study: Hailstorm / MyServices

• Web Services MyDocuments MyAddressbook MyWallet MyNotifications ….

• Scenario Wallet keeps receipts, arranges product return Expedia uses notifications to warn of canceled flight

• Reality Ebay, AmEx, Groove, …


Case Study: OAA

• Common schema for travel industry• Reservations

Flights, trains, rental cars, hotels

• Time & distances• Payment, deposits, vouchers• Vacation Packages


Web Service Technology Stack

DiscoveryDiscovery

DescriptionDescription

PackagingPackaging

TransportTransport

NetworkNetwork

shopping web service?

WSDL URIsWeb ServiceClient

Web Service

UDDI

Proxy

WSDL

SOAP pkg requestWSDL

SOAP pkg response


SOAP (Simple Object Access Protocol)

• SOAP Messages XML Payload

• Using SOAP as RPC (Remote Procedure Call) Messages

SOAP client SOAP serverRequest message

Response message


If a WS were a Phone Call…

• XML represents the conversation,

• SOAP describes the rules for how to call someone

• UDDI is the phone book.

• WSDL describes what the phone call is about and how

you can participate.


WSDL <types> <schema targetNamespace="http://tempuri.org/xsd" xmlns="http://www.w3.org/2001/XMLSchema" xmlns:SOAP-ENC="http://schemas.xmlsoap.org/soap/encoding/" xmlns:wsdl="http://schemas...l/" elementFormDefault="qualified" > </schema> </types> <message name="Simple.foo"> <part name="arg" type="xsd:int"/> </message> <message name="Simple.fooResponse"> <part name="result" type="xsd:int"/> </message> <portType name="SimplePortType"> <operation name="foo" parameterOrder="arg" > <input message="wsdlns:Simple.foo"/> <output message="wsdlns:Simple.fooResponse"/> </operation> </portType>

for int foo(int arg);


DISCO

• If you know the URL for a service• DISCO lets you query them• And get back a WSDL description

• But what if you don’t know the right URL?


UDDI• Hosted Registries

Microsoft, IBM, HP, SAP, NTT, BEA• Entries defined with

Business information• Name, contacts, descriptions, identifier, yellow pages category

Service information• Entities, each of which describes a family of related services

which together implement a business process Binding information

• How to invoke: URI, required parameters, options, & Tmodel Service specifications (Tmodel)

• As a symbol – fingerprint to recognize a known service• Decomposable to find WSDL description


Acronyms (W3C, MSFT, IBM)• UDDI

Discover, describe, register services SOAP-based service for locating WSDL-formatted service descriptions

• DISCO Discover / retrieve SCL+SDL descrips

• SDL / NASSL SOAP description lang –get params / types

• SCL SOAP contract lang – extends SDL – orchestration of msgs

• WSDL Describe abstract interface and protocol bindings of arbitrary

network services (extends scl)

• XLANG / WSFL / BPEL4WS lang for biz processes used in BizTalk Biz process execution language for web services

• MSFT, IBM, BEA proposal NASSL

SCL

SDL

WSDL

WSFL

XLANG

BPEL4WS


The Layer Cake [TBL,XML2000]


RDF (Resource Description Framework)

Way to describe resources via metadata Makes no assumptions about a particular application domainBased on XMLAnother one?Standard for semantic web

Restricts resource descriptions to triplets (subject,predicate,object)

Provides a lightweight ontology systemSubproperty, Subclass, Domain & Range


DAML+OIL (www.daml.org)

• DAML extends RDF and RDFS with richer modeling primitives. disjointWith, intersectionOf, oneOf,

cardinality

• Able to provide properties of properties uniqueness, transitivity, etc.


(mapping to WSDL)

DAML-SDAML+OIL ontology describing Web

ServicesComplements low level descriptions like

WSDL Describes what and why a service operates, Not just how to communicate with it.

Goals: Discovery, Invocation, Composition, Verification, Execution Monitoring





• Background• Representational issues• Planning algorithms



Partial Survey of Planners• UW Internet Softbot

Planners: SENSp / XII / PUCCINI Repr. languages: UWL / SADL ; LCW

• PKS Planning at the knowledge level

• McDermott Forward-chaining search w/ GRG guidance

• McIlraith et al. ConGolog (procs, loops, conditionals, w/ nondet

• Papazoglou, Traverso et al. Stratified service arch; XSRL language; MBP

• Finin; Srivastava; Knoblock; Ambite; Nau…


•

Inputs Filters Models Visualization

False Color

PhenologyNPP

Mean wind

MODISFPAR

MODISLAI

RUC2

Mosaic

Re-project

Drill-down

Mosaic

Re-project

LandSurfaceModels

Daily

GRIBStatistics

LAZEA

Daily LAZEA

Mean Precip.

Soil

Topography

FP

AR

L

AI

Min, Max Temp

Stream flowSnow coverSoil Moisture

GOESRadiation

WGRIB bin

Com-posit

Com-posit

8-day

8-day

Planning for image processing tasks• Many fielded systems

Lansky’s COLLAGE , Chien et al. MVP/ASIP, Golden ADLIM, Blythe GRID…

• Spatial representations important


Motivating ScenariosPlanning a trip

Yahoo maps -> driving time -> travel prefsAutomatic expense form filing

Purchasing a group of itemsAggregation from multiple vendorsSelect for: payment types, stock level, delivLocal & 3rd party reputation services (BBB)

Monitoring marketplaceAuction sitesEvents (check calendar / notification service


UW Internet Softbot

• Software robot• Effectors mv, ftp, chmod, cd, lpr, rm, ...• Sensors ls, finger, INSPEC, netfind, wc, ...• Say whatwhat we want, not howhow to do it

Find phone numbers, fetch/print online papers, …

• Integrate multiple resources


Motivation/Contributions

• Represent actions like ls, finger• Represent goals such as

“Rename paper.tex to kr.tex” “Print all files in directory papers.”

(even with incomplete information)

• No previous system could express


The Middle Ground

Tractability Expressiveness Complete I nf o STRI PS ADL Situation Calculus I ncomplete UWL Moore et al

1. Action Representation

Tractability Expressiveness Complete I nf o Closed World Assumption (CWA) I ncomplete OWA Circumscription

2. Knowledge Representation


Softbot Architecture

Sensors

TaskManager

Effectors

UNIX shell & WWW

SADLActions

PUCCINIPlanner

LCWKnowledge


SADL Family Tree

STRIPS

UWL ADL

SADL

Incomplete info,Noise-free sensors

ConditionalEffects

[Pednault, 89][Etzioni et al, 92]

[Fikes & Nilsson, 71]

Represents ls, “Rename”, finger...

[Golden & Weld, 96]


SADL/UWL AnnotationsGoal annotations: satisfy = achieve by any means hands-off = don’t change (maintenance)Effect annotations cause = change world observe = change agent’s knowledge

“Delete the file named junk” satisfy (name (ƒ, junk)) satisfy(deleted (ƒ))


Information Goals are Temporal

• Two time points When proposition sampledWhen proposition sampled When reply givenWhen reply given

• “Tell me nownow who was President in 1883in 1883”• “Tell me tomorrowtomorrow who is President nownow”• “Identify (ASAPASAP) the file nownow named `junk’”


Information Goals are Temporal

“Rename paper.tex to kr.tex” designator (name) changes UWL can’t express

SADL solution initiallyinitially = time goal was posed = time goal was posed

initially (name (ƒ, paper.tex)) satisfy (name (ƒ, kr.tex))

initially (name (ƒ, core)) satisfy (deleted (ƒ ))

Compare to more general temporal representation


“Print paper, but don’t leave it uncompressed.”

initially (compressed (paper), tv) satisfy (printed (paper)) satisfy (compressed (paper), tv)

State of paper.ps may change temporarilybut must be restored

Compare to more general goal lang, e.g. LTL C C B B

Tidiness Goals


Unbounded Information Gain

action ls (d ) precondition: satisfy(current.shell(csh)) satisfy(readable(d )) effect: ff when in.dir(ff, d) l,n,d observeobserve(length(ff, l ))

observeobserve(name(ff, n )) observeobserve(in.dir(ff, d ))


Compare PKS Representation

Initial State:Kf = {(= (pwd) root), (indir papers root), (indir planner root), (dir root), (dir papers), (dir planner), (file paper_tex)}Kx = {((indir paper_tex planner) | (indir paper_tex papers))}Goal:K(indir paper_tex (pwd))


The Internet Softbot

Sensors

TaskManager

Effectors

UNIX shell & WWW

SADLActions

PUCCINIPlanner

LCWKnowledge


Knowledge Representation

• Closed World Assumption (CWA) Made by classical planners Anything not recorded as true is falsefalse

• Open World Assumption (OWA) Anything not recorded true or false is

unknownunknown Sensor abuse Can’t handle goals


Sensor Abuse

• OWA: Don’t know when to stop sensing Many ways to find same information Many plans containing same action

• After executing find / -name foo, should know ls bin won’t reveal more files named foo

ls tex won’t reveal more files named foo Google may reveal more files named foo


CBA

How Classical Planners Handle

blockblock ((xx)) OnTable (xx) replaced with:OnTable (AA) OnTable (BB) OnTable (CC)

• Relies on CWA Must know all blocks OWA can never be sure

AA

C C

B B


Local Closed World Knowledge

• Complete info over restricted domain All blocks on table, all products at Amazon

• Local Closed World Knowledge (LCW) Restricted form of circumscription Provides fast closed world inference Allows fast updates Suited to planner action representations.


LCW Semantics

““I know all files in directory I know all files in directory binbin””LCW(in.dir(f, bin))

LCW(in.dir(ff, bin)) ff ⊨in.dir(ff, bin) ⊨ in.dir(ff, bin)


LCW Representation• M: Ground literals in agent’s model

in.dir(icaps03, papers) in.dir(junk, papers)executable(core)

• L: LCW formulas in agent’s model LCW(in.dir(ff, papers))

• If P M, and L ⊢LCW(P), then P Conclude: in.dir(foofoo, papers)


LCW Reasoning•Inference

If I know all files in tex, and I know the size of every file, then do I know the size of every file in tex?

•Updates If I know the size of every file in the size of every file in textex,

and I removeremove a file from tex, do I still know the size of every file in the size of every file in textex?

What if I addadd a file to tex?


LCW Reasoning is HardTheorem:

If LCW formulas can contain and then answering an LCW query is NP-hard.

But we need fast inference!But we need fast inference!

• Solution: restrict representation• Positive first-order conjunctions• Fast polynomial time inference/updates

[Etzioni et al. AIJ][Levy VLDB96][Friedman & Weld IJCAI97]


LCW Updates

• L must be updated when M changes.• All changes to M fall into one of four

categories: Information loss: Δ(φ{TF} U) Information gain: Δ(φU{TF}) Domain Growth: Δ(φFT) Domain contraction: Δ(φTF)


Domain GrowthAdding core to bin invalidate

LCW(in.dir(f, bin) size(f,c)) unless the size of core is known!Theorem:

If Δ(φFT)thenL’ L - MREL(φ)

MREL(φ) {ΦREL(φ)⊬LCW(ΦX)θ}REL(φ) {ΦL(XΦθα)Xθφα⊬(ΦX)θ}

BBBB

A A C C


LCW Updates

I nf ormation loss

T F U L’ L -

REL() compress

I nformation gain

U T F L’ L LCW() ls, wc

Domain growth

F T L’ L - MREL()

cp

Domain contraction

T F L’ L rm


Pruning Redundant Sensing

Experience (problems attempted)

Tim

e (

CPU

seco

nd

s)


The Internet Softbot

Sensors

TaskManager

Effectors

UNIX shell & WWW

SADLActions

PUCCINIPlanner

LCWKnowledge


XII / Puccini Planner• Based on UCPOP

Generative, Partial-Order, Causal-Link I.e. much like Gerevini’s LPG

• Efficient sensing (LCW control)• Lifted support of goals

[Golden et al. 94, Golden Phd]


Satisfying GoalsLink Directly to Effect

Subgoal on LCW; Then Expand to Ground Form

Partition

rm * f Satisfy(Deleted(f))

ls LCWlpr foo, lpr bar f Satisfy(Printed(f))


ls -l /tex goal

Threats to LCW,

LCW(in.dir(f, /tex) & size(f, l))

compress /tex/paper cause(length(paper), U)

Threat = “Information Loss”PromoteDemoteConfrontShrink

mv junk /tex/ cause(in.dir(junk, /tex), T)

Threat = “Domain Growth”Promote, Demote, ConfrontShrinkEnlarge


Softbot Status• Fully Implemented (1997)• Hundreds of Unix, Internet Actions• Daunting Combinatorics

Declarative Search Control Laborious, Brittle

• Hence... ? Improved Declarative Control ? Reactive Control ? Less Expressive Language

Rodney

Simon

Info Manifold

MetaCrawler

BargainFinder

ShopBot

Occam

ILASIMS

Ahoy


PG-based Heuristics / Sensing

(own PlayGo)

(subject PlayGo go)

(subject MySystem chess)

(search amazon chess)

(atStore *b amazon)

(subject *b chess)

(LCW((atStore !b amazon)(subject !b chess)))

(trade PlayGo *b amazon)

(order MySystem amazon)

(not (own PlayGo))

(own *b)

(subject *b chess)

(atStore MySystem amazon)

(own PlayGo) (own PlayGo)

(subject PlayGo go) (subject PlayGo go)

(subject MySystem chess) (subject MySystem chess)

(LCW((atStore !b amazon)(subject !b chess)))

(atStore *b amazon)

0 1 2

?

?

[Shaked03]


Using the Graph

• LPG-like search (local search on POP)• Propagating sensing action links• Executing to reach ‘better’ states• Sophisticated heuristics!


Conclusion

,

• Planning for the web is ripe for progress• Data integration

Modeling sources: GAV, LAV, … Answering queries using views Interleaved planning and execution, eddies, cqp

• Service integration Web service composition Representing unbounded information gain Latest heuristic search techniques => fast!


PKS

• Contingent, forward-chaining planner Constructs a complete, correct plan Separates plan-time and execution-time effects

• Less Expressive No universal quantification

• Still needs search control heuristics

[Pettrick & Bacchus KR00, AIPS02]

© daniel s. weld, planet 2003 tutorial on data integration planning for the web ii execution &...

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