Job Shop Scheduling - homes.cs.washington.edu

Job Shop Scheduling - homes.cs.washington.edu

Planning for the Web I Data Integration Dan Weld University of Washington June, 2003 Acknowledgements Oren Etzioni Alon Halevy Zachary Ives Rao Kambhampati Caveat Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 2 My Two Talks for Today Data Integration Providing uniform access to disparate data srcs AI meets DB Answering queries using views

Execution in the face of uncertainty, latency Service Integration Invoking and composing web services Query and update Planning with incomplete information Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 3 Overview: Data Integration Motivation / intro Wrappers / information extraction Database review Integrating data sources Content, completeness, capabilities Reformulation algorithms: Bucket Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 4

What is Data Integration? A system providing: Uniform (same query interface to all sources) Access to (queries; eventually updates too) Multiple (we want many, but 2 is hard too) Autonomous (DBA doesnt report to you) Heterogeneous (data models are different) Structured (or at least semi-structured) Data Sources (not only databases). Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 5 User enters query Formulate queries Remove duplicates ... Post-process + rank

Lycos Excite Collate results Download? Present to user Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 6

Meta-? Web Search Shopping Product Reviews Chat Finder Columnists (e.g. jokes, sports, .) Email Lookup Event Finder People Finder Restaurant Reviews Job Listings Classifieds Apartment + Real Estate Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 7 Intuition: Info Integration

Info aggregation on Steroids! Want agent such that User says what she wants Agent decides how & when to achieve it Example: Show me all reviews of movies starring Matt Damon that are currently playing in Seattle Sidewalk IMDB Daniel S. Weld, PLANET 2003 Tutorial on Data Integration Ebert o g n a d n Fa

Info. Aggregation vs. Integration movies prices of laptop with in Seattle starring IMDB store1 store2 storeN sidewalk join rev1

rev2 revN sort Join, sort aggregate More complex queries Dynamic generation/optimization of execution plan Applicable to wider range of problems Much harder to implement efficiently Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 9

Challenges User must know which sites have relevant info User must go to each one in turn Slow: Sequential access takes time Confusing: Each site has a different interface User must manually integrate information Daniel S. Weld, PLANET 2003 Tutorial on Data Integration Practical Motivation Enterprise Business dashboard; web-site construction. WWW Comparison shopping Portals integrating data from multiple sources B2B, electronic marketplaces Science and culture: Medical genetics: integrating genomic data

Astrophysics: monitoring events in the sky. Environment: Puget Sound Regional Synthesis Model Culture: uniform access to all cultural databases produced by countries in Europe. Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 11 The Problem: Data Integration m y b o o k s .c o m M e d ia te d S c h e m a B ooks I n te rn e t In v e n t o r y O rd e rs W A N M o rg a n K a u fm a n

P re n tic e H a ll ... S h ip p in g I n te rn e t East O rd e rs W est R e v ie w s I n te rn e t FedE x C u s to m e r R e v ie w s UPS

N Y T im e s ... a lt . b o o k s . r e v ie w s Uniform query capability across autonomous, heterogeneous data sources on LAN, WAN, or Internet Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 12 Current Solutions Mostly ad-hoc programming: create a special solution for every case; pay consultants a lot of money. Data warehousing: load all the data periodically into a warehouse. 6-18 months lead time

Separates operational DBMS from decision support DBMS. (not only a solution to data integration). Performance is good; data may not be fresh. Need to clean, scrub you data. Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 13 Data Warehouse Architecture User queries OLAP / Decision support/ Data cubes/ data mining Relational database (warehouse) Data extraction, cleaning/ scrubbing Data extraction programs

Data source Data source Daniel S. Weld, PLANET 2003 Tutorial on Data Integration Data source 14 Warehouse Summary Pro Relatively simple Good performance (OLAP support) Mature technology (DB, ETL industries) Con Expensive Stale data

Risky most warehouse projects fail Rigid architecture Fixed schema Must know all queries ahead of time Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 15 Architecture for Virtual Integration Leave the data in the sources. When a query comes in: 1) Determine which sources are relevant to query. 2) Break query into sub-queries for each source. 3) Get answers from sources 4) Combine. Data is fresh. Challenge: performance. Daniel S. Weld, PLANET 2003 Tutorial on Data Integration

16 Virtual Integration Architecture User queries Mediated schema Reformulator Mediator: Optimizer Execution engine Data source catalog wrapper wrapper wrapper Data

source Data source Data source Sources can be: relational, hierarchical (IMS), structured files, web sites. Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 17 Research Projects

Garlic (IBM), Information Manifold (AT&T) Tsimmis, InfoMaster (Stanford) Internet Softbot/Razor/Tukwila (U Wash.) Hermes (Maryland) Telegraph / Eddies (UC Berkeley) Niagara (Univ Wisconsin) DISCO, Agora (INRIA, France) SIMS/Ariadne (USC/ISI) Emerac/Havasu (ASU) Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 18 Industry Nimble Technology Enosys Markets IBM BEA

Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 19 Dimensions to Consider How many sources are we accessing? How autonomous are they? Meta-data about sources? Is the data structured? Queries or also updates? Requirements: accuracy, completeness, performance, handling inconsistencies. Closed world assumption vs. open world? Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 20 Outline Motivation / introduction Wrappers / information extraction Database Review Integrating data sources

Content, completeness, capabilities Reformulation algorithms: Bucket Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 21 Wrapper Programs Task to communicate with the data sources and do format translations. Built w.r.t. a specific source. Can sit either at the source or mediator. Often hard to build (very little science). Can be intelligent perform source-specific optimizations. Daniel S. Weld, PLANET 2003 Tutorial on Data Integration

22 Example Introduction to DB Transform: Phil Bernstein Eric Newcomer Addison Wesley, 1999 into: Introduction to DB Phil Bernstein Eric Newcomer Addison Wesley 1999 Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 23 Wrapper Construction

Use PERL, or Generate wrappers automatically Get training examples Human marks up selected pages with GUI tool Use shallow NLP to create features Favorite learning method HMMs, VS on prefix, postfix strings, ?? Boosting Co-training See research on information extraction Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 24 SemTag & Seeker WWW-03 Best Paper Prize Seeded with TAP ontology (72k

concepts) And ~700 human judgments Crawled 264 million web pages Extracted 434 million semantic tags Automatically disambiguated Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 25 Outline Motivation / Introduction Wrappers / Information extraction Database Review Relational algebra, SQL, datalog Views Optimization (query planning) Integrating data sources Content, completeness, capabilities Reformulation algorithms: Bucket

Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 26 Traditional Database Architecture Query (SQL) Database Manager (DBMS) -Storage mgmt -Query processing Answer -View management (relation) -(Transaction processing) Daniel S. Weld, PLANET 2003 Tutorial on Data Integration Database (relational)

27 Relational Data: Terminology Product relation (Arity=4) attribute Name Price Manufacturer Category gizmo GizmoWorks $19.99 gadgets

Power gizmo $29.99 gadgets GizmoWorks SingleTouch $149.99 photography Canon tuple MultiTouch schema $203.99 household

Hitachi roduct(Name: string, Price: real, category: enum, Man: stri Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 28 Relational Algebra Operators tuple sets as input, new set as output Operations Name Price Category Manufacturer

gizmo $19.99 gadgets GizmoWorks Power gizmo $29.99 gadgets GizmoWorks SingleTouch $149.99 photography Canon MultiTouch

$203.99 household Hitachi Union, Intersection, difference, .. Selection ( Projection () Cartesian product (X) Join ( ) City Tempe Daniel S. Weld, PLANET 2003 Tutorial on Data Integration Manufacturer GizmoWorks

Kyoto Canon Dayton Hitachi 29 SQL: A query language for Relational Algebra Many standards out there: SQL92, SQL2, SQL3, SQL99 Select attributes From relations (possibly multiple, joined) Where conditions (selections) Other features: aggregation, group-by etc. Find companies that manufacture products

bought by Joe Blow SELECT Company.name FROM Company, Product WHERE Company.name=Product.maker AND Product.name IN (SELECT product FROM Purchase WHERE buyer = Joe Blow); Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 30 Deductive Databases Tables viewed as predicates. Ops on tables expressed as datalog rules (Horn clauses, without function symbols) Enames(Name) :- Employe(Name, SSN) [Projection] Wealthy-Employee(Name) :- Employee(Name,SSN), Salary(SSN,Money),Money> 10

[Selection] Ed(Name, Dname) :- Employee(Name, SSN), E_Dependents(SSN, Dname) [Join] ERelated(Name,Dname) :- Ed(Name,Dname) ERelated(Name,Dname) :- Ed(Name,D1), ERelated(D1,D2) [Recursion] Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 31 Views Views are relations, except that they are not physically stored. Uses: simplify complex queries, & define conceptually different views of DB for diff. users.

Example: purchases of telephony products: CREATE VIEW telephony-purchases AS SELECT product, buyer, seller, store FROM Purchase, Product WHERE Purchase.product = Product.name AND Product.category = telephony Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 33 A Different View CREATE VIEW Seattle-view AS SELECT buyer, seller, product, store FROM Person, Purchase WHERE Person.city = Seattle AND Person.name = Purchase.buyer We can later use the view: SELECT name, store

FROM Seattle-view, Product WHERE Seattle-view.product = Product.name AND Product.category = shoes Whats really happening when we query a view?? Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 34 Materialized Views Views whose corresponding queries have been executed and the data is stored in a separate database Uses: Caching Issues Using views in answering queries Normally, views are available in addition to DB (so, views are local caches) In information integration, views may be the only things

we have access to. An internet source specializing in tom hanks movies can be seen as a view on a database of all movies. Except, there is no DB out there which contains all movies.. Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 35 DB Inference Type 1 Evaluate query on instance of data Exponential in size of query Key is performance in size of data Type 2 Query containment Q1 Q2 No matter what data instances

Logical entailment Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 36 Query Optimization Declarative query Imperative execution plan SELECT S.buyer FROM Purchase P, Person Q WHERE P.buyer=Q.name AND Q.city=seattle AND Q.phone > 5430000 buyer City=seattle

phone>5430000 Inputs: Buyer=name (Simple Nested Loops) the query available memory statistics about the data Person Purchase indexes, (Table scan) (Index scan) cardinalities, selectivity factors eally: Want to find best plan. Practically: Avoid worst plan Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 37 (On-the-fly)

sname bid=100 (On-the-fly) sname (On-the-fly) rating > 5 Pipelined (Use hash index; do sid=sid not write result to temp) rating > 5 bid=100 (Simple Nested Loops)

hash join sid=sid Reserves Reserves Sailors (On-the-fly) SELECT Goal of optimization: To find more efficient plans that compute the same answer. sname

Sailors rating > 5 (On-the-fly) S.sname FROM (Sort-Merge Join) sid=sid (Scan; write to temp T1) bid=100 Reserves Daniel S. Weld, PLANET 2003 Tutorial on Data Integration Sailors Reserves R,

Sailors S WHERE R.sid=S.sid AND 38 Relational Algebra Equivalences Allow us to choose different join orders and to push selections and projections ahead of joins. Selections: c1 ... cn R c1 . . . cn R c1 c 2 R c 2 c1 R (Commute) Projections: Joins: R a1 R a1 ... an R

(S T) (R S) (Cascade) (R S) T (Associative) (S R) Daniel S. Weld, PLANET 2003 Tutorial on Data Integration (Commute) Optimizing Joins Q(u,x) :- R(u,v), S(v,w), T(w,x) R S T Many ways of doing a single join R S

Symmetric vs. asymmetric join operations Nested join, hash join, double pipe-lined hash join etc. Processing costs alone vs. proc. + transfer costs Get R and S together vs, get R, get just the tuples of S that will join with R (semi-join) Many orders in which to do the join (R join S) join T (S join R) join T (T join S) join R etc. All with different costs Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 40 Determining Join Order In principle, must consider all possible join orderings: D D

As # of joins increases, # plans grows rapidly Must restrict search space. D A B A C System-R: consider only left-deep join trees. This lets us generate all fully pipelined plans: Intermediate results not written to temporary files. (Not all left-deep trees are fully pipelined) Daniel S. Weld, PLANET 2003 Tutorial on Data Integration C B C A

B Cost Estimation For each plan considered, estimate cost: Estimate cost of each operation in plan tree. Depends on input cardinalities. Estimate size of result for each op in tree! Use information about the input relations. Selectivity (Histograms) For selections and joins, assume independence of predicates. System R cost estimation approach. Very inexact, but works ok in practice. More sophisticated techniques known now. Daniel S. Weld, PLANET 2003 Tutorial on Data Integration Key Lessons in Optimization Classic planning / execution scenario

Uncertainty / replanning key for data integration Main points Disk IO as cost metric Algebraic rules / use in query transformation.. Join ordering via dynamic programming Estimating cost of plans Size of intermediate results. Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 44 s i r p Re e Integrator vs. DBMS

No common schema Sources with heterogeneous schemas Semi-structured sources Legacy Sources Not relational-complete Access/process limitations Autonomous sources Uncontrolled source content overlap Lack of source statistics Tradeoffs: plan cost, coverage, quality, Multi-objective cost models Unpredictable run-time behavior Makes query execution hard Daniel S. Weld, PLANET 2003 Tutorial on Data Integration

45 Outline Motivation Wrappers / information extraction Database Review Integrating data sources Content, completeness, capabilities Reformulation algorithms: Bucket Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 46 Remnder: Info Integration Want agent such that User says what she wants Agent decides how & when to achieve it Example:

Show me all reviews of movies starring Matt Damon that are currently playing in Seattle Sidewalk IMDB Daniel S. Weld, PLANET 2003 Tutorial on Data Integration Ebert o g n a d n Fa Data Source Catalog Contains meta-information about sources: Logical source contents (books, new cars). Source capabilities (can answer SQL queries?) Source completeness (has all books). Physical properties of source and network. Statistics about the data (like in an RDBMS)

Source reliability Mirror sources? Update frequency. Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 48 Content Descriptions User queries refer to the mediated schema. Source data is stored in a local schema. Content descriptions provide semantic mappings between different schemas. Data integration system uses the descriptions to translate user queries into queries on the sources. Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 49

Desiderata for Source Descriptions Expressive power: distinguish between sources with closely related data. Enable pruning of access to irrelevant sources. Easy addition: make it easy to add new data sources. Reformulation: be able to reformulate a user query into a query on the sources efficiently and effectively. Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 50 Reformulation Problem Given: A query Q posed over the mediated schema Descriptions of the data sources Find: A query Q over the data source relations,

such that: Q provides only correct answers to Q, and Q provides all possible answers from to Q given the sources. Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 51 Approaches to Specifying Source Descriptions Global-as-view: express the mediated schema relations as a set of views over the data source relations Local-as-view: express the source relations as views over the mediated schema. Can be combined with no additional cost. Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 52

Global-as-View Mediated schema: Movie(title, dir, year, genre), Schedule(cinema, title, time). Create View Movie AS select * from S1 [S1(title,dir,year,genre)] union select * from S2 [S2(title, dir,year,genre)] union [S3(title,dir), S4(title,year,genre)] select S3.title, S3.dir, S4.year, S4.genre from S3, S4 where S3.title=S4.title Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 53 Global-as-View: Example 3 Mediated schema:

Movie(title, dir, year, genre), Schedule(cinema, title, time). Source S4: S4(cinema, genre) Create View Movie AS select NULL, NULL, NULL, genre from S4 Create View Schedule AS select cinema, NULL, NULL from S4. But what if we want to find which cinemas are playing comedies? Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 55 Global-as-View Summary Very easy conceptually. Query reformulation view unfolding. Can build hierarchies of mediated schemas. Sometimes loose information.

Not always natural. Adding sources is hard. Need to consider all other sources that are available. May need to modify every global view defn Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 56 Local-as-View: example 1 Mediated schema: Movie(title, dir, year, genre), Schedule(cinema, title, time). Create Source S1 AS [S1(title, dir, year, genre)] select * from Movie Create Source S3 AS [S3(title, dir)] select title, dir from Movie Create Source S5 AS

[S5(title, dir, year)] select title, dir, year from Movie where year > 1960 AND genre=Comedy Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 57 Local-as-View: Example 2 Mediated schema: Movie(title, dir, year, genre), Schedule(cinema, title, time). Source S4: S4(cinema, genre) Create Source S4 select cinema, genre from Movie m, Schedule s where m.title=s.title . Now if we want to find which cinemas are playing comedies, there is hope! Daniel S. Weld, PLANET 2003 Tutorial on Data Integration

58 Local-as-View Summary Very flexible. You have the power of the entire query language to define the contents of the source. Hence, can easily distinguish between contents of closely related sources. Adding sources is easy: Theyre independent of each other. Query reformulation: Answering queries using views! Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 59 The General Problem Given a set of views V1,,Vn, and a query Q, can we answer Q using only

the answers to V1,,Vn? Many, many papers on this problem. Great survey on the topic: (Halevy, 2001). The best performing algorithm: MiniCon (Pottinger & Levy, 2000). Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 60 Example Movie, Query: Q(x):- r1(x,y) & r2(y,x) Schedule Views: Create Source V1 as V1(a):-r1(a,b) select a

V2(d):-r2(c,d) from r1 V3(f):- r1(f,g) & r2(g,f) Create Source V3 as select r1.arg1 from r1, r2 where r1.arg1 = r2.arg2 Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 61 Bucket Algorithm (1) 1) For each subgoal in the query, place relevant views in the subgoals bucket Query: Q(x):- r1(x,y) & r2(y,x) Views: V1(a):-r1(a,b)

V2(d):-r2(c,d) V3(f):- r1(f,g) & r2(g,f) Buckets: r1(x,y) r2(y,x) V1(x),V3(x) V2(x), V3(x) Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 62 Bucket Algorithm (2) 2) For every combo in the Cartesian product of the buckets, check containment of the query Q(x):-r1(x,y) & r2(y,x)Q4(x):-r1(x,a) & r2(a,x) & r1(x,b) & r2(b,x) Bucket Algorithm will check all possible

combinations r1(x,y) V1(x),V3(x) r2(y,x) V2(x), V3(x) Candidate rewritings: Q1(x) :- V1(x) & V2(x) Q2(x) :- V1(x) & V3(x) Q3(x) :- V3(x) & V2(x) Q4(x) :- V3(x) & V3(x) r1(x,y r2(y,x) Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 63 Modeling Source Capabilities Negative capabilities: A web site may require certain inputs (in an HTML form).

Positive capabilities: A source may be an ODBC compliant system. Need to decide placement of operations according to capabilities. Problem: how to describe and exploit source capabilities. Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 64 Example #1: Access Patterns Mediated schema relation: cites(paper1, paper2) Create Source S1 as select * from cites given paper1 S1($x,y) :- cites(x, y) Create Source S2 as

select paper1 from cites S2(x) :- cites(x, y) Query: Q(x) :- cites(x, W03) Select paper1 from cites where paper2=W03 cites(x, y) Daniel S. Weld, PLANET 2003 Tutorial on Data Integration & 65 Example #2: Access Patterns Create Source S1 as select * from Cites

given paper1 Create Source S2 as select paperID from UW-Papers Create Source S3 as select paperID from AwardPapers given paperID Query: select * from AwardPapers Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 66 Example #2: Solutions Cant go directly to S3 (it requires a binding). Can go to S1, get UW papers, and check if theyre in S3. Can go to S1, get UW papers, feed them to S2, and then check if theyre in S3. Can

go to S1, feed results into S2, feed results into S2 again, then check if theyre in S3. Note: we cant a priori decide when to stop. Need recursive query processing. Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 67 Algorithms Inverse Rules [Duschka & Genesereth] Minicon [Pottinger & Levy] Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 68 Complexity of finding maximallycontained plans Complexity depends on sources, not query Sources as unions of conjunctive queries (NP-hard) Disjunctive descriptions

Sources as recursive queries (Undecidable) True source contents Advertised description Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 69 Matching Objects Across Sources How do I know that D. Weld in source 1 is the same as Daniel S. Weld in source 2? If uniform keys across sources, easy. If not: Domain specific solutions (e.g., maybe look at the address, ). Use IR techniques (Cohen, 98). Judge similarity as you would between documents. Use concordance tables.

These are time-consuming to build, but you can then sell them for lots of money. Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 70 Schema Mapping Problem Types of structures: Database schemas, XML DTDs, ontologies, , Input: Two (or more) structures, S1 and S2 (perhaps) Data instances for S1 and S2 Background knowledge Output: A mapping between S1 and S2 Should enable translating between data instances. Daniel S. Weld, PLANET 2003 Tutorial on Data Integration

71 Semantic Mappings between Schemas Source schemas = XML DTDs house address contact-info agent-name num-baths agent-phone 1-1 mapping non 1-1 mapping house location

contact name full-baths half-baths phone Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 72 Summary Data Integration Providing uniform access to disparate data srcs AI meets DB Modeling Data Sources GAV, LAV Query Reformulation Answering queries using views

Bucket algorithm [Inverse rules, Minicon] Schema Matching Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 73 Next Talk Executing data integration plans Variable latency, Poor info on size & speed of remote sources Adaptive execution Service integration Invoking and composing web services Query and update Planning with incomplete information Daniel S. Weld, PLANET 2003 Tutorial on Data Integration 74

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