Information Integration

1. Information Integration

2. Information Resides on Heterogeneous Information Sources Personal database Excel WWW Flat File • different interfaces • different data representations • redundant and conflicting information

3. Modes of Information Integration • Federated Databases: the sources are independent, but one source can call on others to supply information • Data warehouse: copies of data from several sources are stored in a single database, called a data warehouse. The data stored at the warehouse is first processed in some way before storage; e.g. data may be filtered, and relations may be joined or aggregated. As the data is copied from the sources, it may need to be transformed in certain ways to make all data conform to the schema at the data warehouse

4. Modes of Information Integration • Mediation: a mediator is a software component that supports a virtual database, which the user may query as if it were materialized (physically constructed like a warehouse). The mediator store no data of its own. Rather, it translates the user’s query into one or more queries to its sources. The mediator then synthesizes the answer to the user’s query from the responses of those sources, and returns the answer to the user

5. Problems of Information Integration Example: The AAAI Automobile Co. has 1000 dealers each of which maintains a database of their cars in stock. AAAI wants to create an integrated database containing the information of all 1000 sources. The integrated database will help dealers locate a particular model if they don’t have one in stock. It also can be used by corporate analysts to predict the market and adjust production to provide the model most likely to sell

6. Problems of Information Integration • The 1000 dealers do not all use the same database schema: Cars (serialNo, model, color, autoTrans, cdPlayer, ...) or Autos (serialNo, model, color) Options (serialNo, option)

7. Problems of Information Integration • Schema difference • Different equivalent names • Data type differences: numbers may be represented by character strings of varying length at one source and fixed length at another • Value differences: the same concept may be represented by different constants at different sources (BLACK, BL, 100, etc) • Semantic differences: Terms can be given different interpretations at different sources (Cars includes trucks or not) • Missing values: a source may not record information of a type that all of the other sources provide

8. Goal: System Providing Integrated View of Heterogeneous Data Integration System • collects and combines information • provides integrated view, uniform user interface Personal database Excel WWW Flat File

9. The Data Warehousing Approach to Integration Client Stored Integrated View Mediator Wrapper Wrapper Excel Flat File

10. The Data Warehousing Approach to Integration • Data from several sources is extracted and combined into a global schema • The data is stored at the warehouse which looks like an ordinary database • There are three approaches to maintaining the data in the data warehouse: • off-line reconstruction of the whole data warehouse • the data warehouse is updated periodically based on the changes made to the original data sources • the data warehouse is updated immediately

11. The Data Warehousing Approach to Integration Example Suppose that there are two dealers in the system and that they usethe schemas: Cars (serialNo, model,color,autoTrans, cdPlayer, ...) and Autos (serialNo,model,color) Options (serialNo,option) Assume a data warehouse with the schema: AutoWhse(serialNo,model,color,autoTrans, dealer)

12. The Data Warehousing Approach to Integration • The software to extracts data from the dealer’s databases and populates the global schema can be written as SQL-queries. The query for the first dealer: insert into AutoWhse(serialNo,model,color,autoTrans, dealer) select serialNo, model, color, autoTrans, ‘dealer1’ from Cars The code for the second dealer is more complex since we have to decide whether or not a given car has an automatic transmission.

13. The Data Warehousing Approach to Integration insert into AutoWhse(serialNo,model,color,autoTrans, dealer) select serialNo, model, color, ‘yes’, ‘dealer2’ from Autos, Options where Autos.serialNo=Options.serialNo and option=‘autoTrans’; insert into AutoWhse(serialNo,model,color,autoTrans, dealer) select serialNo, model, color, ‘no’ ‘dealer2’ from Autos where not exists ( select * from Options where serialNo=Autos.serialNo and option=‘autoTrans’);

14. The Wrapper and Mediator Architecture Client Common Data Model portfolios for each company Mediator stock market prices business reports Wrapper Wrapper Excel Flat File

15. The Wrapper and Mediator Architecture • A mediator supports a virtual view, or collection of views, that integrates several sources in much the same way that the materialized relation(s) in a data warehouse integrate sources. • The mediator doesn’t store any data < < < Example: Let us consider the same scenario. The mediator integrates the same two data sources into a view that is a single relation with the schema: AutoMed(serialNo,model,color,autoTrans, dealer)

16. The Wrapper and Mediator Architecture Assume the user asks the mediator about the red cars: select serialNo, model from AutosMed where color = ‘red’; The mediator forward the same query to each of the two wrappers (1) select serialNo, model from Cars where color=‘red’; (2) select serialNo, model from Autos where color=‘red’; The mediator can take the union of answers and return the result to the user.

17. The Lazy Integration Approach Query Decomposition, Translation and Result Fusion Client IBM portfolio Mediator IBM price IBM related reports (in common model) Wrapper Wrapper IBM related reports Excel Flat File

18. Wrappers in Mediator-Based Systems • In a data warehouse system, the source extractors consist of: • one or more queries built-in that are executed at the source to produce data for the data warehouse • communication mechanisms, so that wrapper can: • pass ad-hoc queries to the source • receive responses from the source • pass information to the warehouse • Mediator systems require more complex wrappers - the wrapper must be able to accept a variety of queries from the mediator and translate any of them to the terms of the source.

19. Wrappers in Mediator-Based Systems • A systematic way to design a wrapper that connects a mediator to a source is to classify the possible queries that the mediator can ask into templates, which are queries with parameters that represent constants. • The mediator can provide the constants, and the wrapper executes the query with the given constants. • T  S the template T is turned into the source query S Example: The source of dealer1: Cars (serialNo, model,color,autoTrans, cdPlayer, ...)

20. Wrappers in Mediator-Based Systems Assume we use the mediator with schema: AutoMed(serialNo,model,color,autoTrans, dealer) How the mediator could ask the wrapper for cars of a given color> The template: select * from AutoMed where color= ‘$c’;  select serialNo, model color, autoTrans, ‘dealer1’ from Cars where color=‘$c’;

21. Wrappers in Mediator-Based Systems • The wrapper could have another template that specified the parameter $m representing a model • there would be 2N templates for N attributes • the number of templates could grow unreasonably large.

22. Wrapper Generators • The template defining a wrapper must be turned into code for the wrapper itself - the software that creates the wrapper is called a wrapper generator • The wrapper generator creates a table that holds the various query patterns contained in the templates, and the source queries that are associated with each. • A driver is used in each wrapper. The task of the driver is to : • accept a query from the mediator • search the table for a template that match the query • the source query is sent to the source using a communication mechanism • the response is processed by the wrapper, if necessary, and then returned to the mediator

23. Wrappers & Mediators from High-Level Specifications Client Mediator Specification Interpreter Mediator Mediator Specification Wrapper Generator Wrapper Wrapper Wrapper Specification Source Source

24. Filters • Complex template select * from AutoMed where color= ‘$c’ and model = ‘$m’;  select serialNo, model color, autoTrans, ‘dealer1’ from Cars where color=‘$c’ and model =‘$m’; • Wrapper filter approach - if the wrapper has a template that returns a superset of what the query wants then it is possible to filter the result at the wrapper • The decision whether a mediator asks for a subset of what the pattern of some wrapper template returns is a hard problem < < < <>>>>

25. Filters Example: Given the template select * from AutoMed where color= ‘$c’; The mediator needs to find blue Gobi model car: select * from AutoMed where color= ‘blue’ and model=‘Gobi’; • use the template with $c=blue to find all blue cars • store the result in the temporary relation Temp • select from Temp the Gobi’s and return the result

26. Other Wrapper Operations • It is possible to transform the data at the wrapper in different ways • The mediator is asked to find dealers and models such that the dealer has two red cars, of the same model, one with and one without automatic transmission. Suppose we have only one template as before. Select A1.model A1.dealer from AutoMed A1 AutoMed A2 where A1.model=A2.model and A1.color=‘red’ and A2.color=‘red’ and A1.autoTrans=‘no’ and A2.autoTrans=‘yes’;

27. Other Wrapper Operations • It is possible to answer the query by first obtaining from the Dealer’s 1 source a relation with all the red cars (use the original template) - RedAutos relation • select distinct A1.model A1.dealer from RedAutos A1, RedAutos A2 where A1.model=A2.model and A1.autoTrans=‘no’ and A2.autoTrans=‘yes’;

28. Challenge: Sources Without a Well-Structured Schema Examples • semistructured • irregular • deeply nested • cross-referenced • incomplete schema knowledge • autonomous • dynamic • HTML pages • SGML documents • genome data • chemical structures • bibliographic information • results of the integration process

29. Challenge: Different and Limited Source Capabilities Client retrieve IBM data Mediator (U = A + B) retrieve IBM data retrieve IBM data Wrapper (A) Wrapper (B)

30. Mediator has to Adapt to Query Capabilities of Sources Client retrieve IBM data Mediator (U = A + B) retrieve IBM data retrieve IBM data retrieve everything (A) does not allow selection Wrapper (A) Wrapper (B)

31. Part B • Semistructured Data Representation • Mediator Generation • Wrapper Generation • Capabilities-Based Rewriting

32. Representation of Semistructured Information using OEM semantic object-id label Set Value <http://www/~doe, faculty, {&f1,&l1,&r1}> <&f1, first_name, “John”> <&l1, last_name, “Doe”> <&r1, rank, “professor”> Atomic Value structural object-id

33. Object Exchange Model - Goals • Easy to read • Easy to edit • Easy to generate or parse by a program • Consistency with Stanford’s other projects (developed with the TSIMMIS’) • Possibility of extensions in the future

34. Graph Representation of OEM Data <http://www/~doe, faculty, {&f1,&l1,&r1}> <&f1, first_name, “John”> <&l1, last_name, “Doe”> <&r1, rank, “professor”> http://www/~doe faculty first_name “John” last_name “Doe” rank “professor”

35. OEM Structures Represent Arbitrary Labeled Graphs http://www/~smith faculty name “Mary Smith” project “Air DB” paper author name “John Doe” author name “Mary Smith” title “Thin Air DB” http://www/~doe faculty first_name “John” last_name “Doe” rank “professor”

36. Reprezentacja danych semistrukturalnych • Object Exchange Model • ACeDB • XML • Mogą być wykorzystywane w warstwie pomiędzy mediatorem a wrapper’ami.

37. Object Exchange Model • Zdefiniowany przy okazji budowy systemu Tsimmis służącego do integracji heterogenicznych źródeł danych. • Wykorzystywany przy projekcie Merlin (MQS) i Lorel (QL)

38. Object Exchange Model (cd) • Węzeł OEM skłąda się z czterech pól: • Object-ID – jest wykorzystywany do unikalnej identyfikacji określonego węzła OEM • Label – jest ciągiem znaków który opisuje to co węzeł OEM reprezentuje • Type – jest typem danych wartości węzła. (atomowy lub kolekcja) • Value – może być albo wartością atomową albo referencją do kolekcji węzłów OEM • Jest zwykle reprezentowany jako: <Object-ID Label Type Value>

39. Object Exchange Model – przykład <&oid1 Notowanie Set {&oid11 &oid12}> <&oid11 NrNotowania String „4004”> <&oid12 Rezultaty Set {&oid121 &oid122}> <&oid121 Miejsce1 Set {&oid1211}> <&oid1211 Utwor String „metropolis”> <&oid122 Miejsce2 Set {&oid1221}> <&oid1221 Utwor String „money”> <&oid2 Notowanie Set {&oid21 &oid22}> <&oid21 NrNotowania String „4005”> <&oid22 Rezultaty Set {&oid221 &oid222}> <&oid221 Miejsce1 Set {&oid2211}> <&oid2211 Utwor String „learning to fly”>

40. Object Exchange Model – cechy • Reprezentowany jako graf z obiektami na wierzchołkach i etykietami na krawędziach. • Wszystkie wystąpienia są obiektami. • Każdy obiekt ma swój unikalny identyfikator (oid). • Rozróżniane są dwa typy obiektów: atomowe i złożone.

41. Object Exchange Model – cechy (cd) • W OEM występują tzw. nazwy (ang. names), które mogą być traktowane jako aliasy do obiektów wewnątrz bazy danych. • Nazwa służy jako wskaźnik do bazy danych. • Każdy obiekt w bazie danych powinien być osiągalny za pomocą nazwy.

42. ACeDB • ACeDB (A C. elegans Database) była rozwijana jako baza danych informacji genetycznej organizmów. • Rozwijana od 1989. • Posiada swój własny język zapytań AQL - Acedb Query Language • http://www.acedb.org/

43. ACeDB – cechy • Schemat i dane mogą być traktowane jako drzewo z etykietowanymi krawędziami. • Krawędzie mogą być etykietowane jakimkolwiek typem podstawowym. (int, Notowanie)np.: array Int unique Int • Z określonego wierzchołka drzewa danych może wychodzić wiele gałęzi. • ACeDB pozwala na to aby jakakolwiek etykieta różna od etykiety głównej była pominięta. • Identyfikatory obiektów wprowadzane są przez użytkownika.

44. ACeDB – cechy (cd) • ACeDB wymaga schematu • Mimo to, fakt, że dane mogą być pomijane oraz to, że etykietowane dane są traktowane jednolicie z innymi prostymi typami powoduje, że jest on bardzo bliski semistrukturalnemu modelowi danych.

45. ACeDB – przykład >Book titleUNIQUEText authors Text chapters int UNIQUE Text language UNIQUEenglish french other date UNIQUEmonthInt yearInt &hock2 title ”Computer Simulation Using Particles” authors ”Hockney” ”Eastwood” chapters 1 ”Computer Experiments” 2 ”A One-Dimensional Model” ... language english

46. XML • Znany i lubiany

47. Różnice pomiędzy XML a OEM • XML jest uporządkowany. • Etykiety w OEM są wykorzystywane tylko jako punkt odniesienia oraz do oznaczania zależności pomiędzy obiektami. W XML każdy element nie będący ciągiem tekstowym zawiera identyfikujący go znacznik – etykietę. • XML nie wspiera bezpośrednio struktury grafu.

48. Overview • Semistructured Data Representation • Mediator Generation • Example of mediator specification • Language expressiveness • Implementation and performance • Wrapper Generation • Capabilities-Based Rewriting

49. Merge Information Relating to a Faculty faculty name “John Doe” rank “professor” birthday “April 1” papers ... • Schema Integration • Info fusion s1 s2 faculty name “John Doe” rank “professor” papers ... person name “John Doe” birthday “April 1”

50. Mediator Specification Example faculty name “John Doe” rank “professor” birthday “April 1” papers ... <N faculty {<L V>}> :- <faculty {<name N> <L V>}>@s1 <N faculty {<L V>}> :- <person {<name N> <L V>}>@s2 s1 s2 faculty name “John Doe” rank “professor” papers ... person name “John Doe” birthday “April 1”