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Modeller och språk för objekt-, relations- och webbdatabaser

Modeller och språk för objekt-, relations- och webbdatabaser. HT 2002. Data models. Relational, object, and semistructured. Types of database applications. Queries No queries. Payroll. GIS. Word processing. CAD/CAM. Simple data Complex data. An example schema. COMPANY.

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Modeller och språk för objekt-, relations- och webbdatabaser

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  1. Modeller och språk för objekt-, relations- och webbdatabaser HT 2002

  2. Data models Relational, object, and semistructured

  3. Types of database applications Queries No queries Payroll GIS Word processing CAD/CAM Simple data Complex data

  4. An example schema COMPANY head_office address PERSON ceo departments ADDRESS ISA office boss DEPARTMENT EMPLOYEE EMPLOYEEs

  5. An example schema

  6. A relational schema • COMPANY(CID, Name, Street, City, CEO) • DEPARTMENT(CID, DeptName, Street, City, Boss) • PERSON(SS#, Name, Street, City) • EMPLOYEE(SS#, Salary) • EMPLOYMENT(CID, DeptName, Emp) COMPANY.CEO << EMPLOYEE.SS# DEPARTMENT. CID << COMPANY.CID EMPLOYEE.SS# << PERSON.SS# EMPLOYMENT.(CID, DeptName) << DEPARTMENT.(CID, DeptName) EMPLOYMENT.Emp << EMPLOYEE.SS#

  7. Problems with the schema • Composite attributes do not exist Head office cannot be described directly but is decomposed into Street, City • Set valued attributes do not existThe fact that people are employed at departments is described in an extra table

  8. Problems with the schema • Generalisation does not exist The generalisation relationship between EMPLOYEE and PERSON is not explicit • Artificial identifiers requiredCID is an artificial identifier for COMPANY

  9. A SQL query SELECT A.SS#, B.SS# FROM EMPLOYEE AS A, EMPLOYEE AS B, EMPLOYMENT AS EMPL1, EMPLOYMENT AS EMPL2, PERSON AS P1, PERSON AS P2 DEPARTMENT AS D1, DEPARTMENT AS D2 WHERE A.SS# = P1.SS# AND B.SS# = P2.SS# AND P1.Street = P2.Street AND P1.City = P2.City AND A.Salary > 20000 AND B.Salary > 20000 AND EMPL1.SS# = A.SS# AND EMPL2.SS# = B.SS# AND EMPL1.CID = D1.CID AND EMPL2.CID = D2.CID AND EMPL1.DeptName = D1.DeptName AND EMPL2.DeptName = D2.DeptName AND D1.City = London AND D2.City = London Which employees live at the same address, work at departments in London and earn more than 20000?

  10. An object database schema ADDRESS Street: String City: String PERSON SS#: String Name: String Address: Address EMPLOYEE ISA PERSON Salary: Integer Boss: Employee Departments: {Department} COMPANY Name: String Head office: Address Departments: {Department} CEO: Employee DEPARTMENT Name: String Office: Address Boss: Employee Employees: {Employee}

  11. An OO query Are there employees who live at the same address, work at departments in London and earn more than 20000? SELECT e1.ss#, e2.ss# FROM e1, e2 in EMPLOYEE; d1, d2 in DEPARTMENT WHERE e1 in d1.Employees AND e2 in d2.Employees AND e1.Address = e2.Address AND e1.Salary > 20000 AND e2.Salary > 20000 AND f1.Office.City = London AND f2.Office.City = London

  12. OODB models Fundamentals • Complex types • Types and classes • Object identity • Inheritance

  13. Complex types • Base typesStringIntegerBoolean • ConstructorsTupleSetBagListArray

  14. Complex types - an example • COORDINATEX: IntegerY: IntegerZ: Integer • PARKING SPOTPosition: COORDINATEOccupied: Boolean • CAR PARKSpots: ARRAY OF PARKING SPOTPersonnel: SET OF EMPLOYEE The type constructors can be applied to any type - cf. the relational model

  15. Relation schemes and relations PERSON Relation scheme SS# Name Age Salary 650101-2288 Eva Svensson 33 25000 750203-3133 Per Jonsson 23 20000 500107-5532 Sven Olsson 47 25000 800515-0044 Pia Eriksson 17 18000 Relation

  16. Types and classes • A type is a time independent description of a set of (base or complex) values • A class has a time dependent extension that is a set of object identifiers

  17. Object identity • Every object is identified by an object identifier • The object identifier does not change during the life span of the object • Two different objects with different object identifiers may have the same values OID: 1293301 Name: Simba Age: 11 Weight: 260 OID: 1088732 Name: Simba Age: 11 Weight: 260

  18. Object identity The relational model Lion(Name, Age, Weight) (Simba, 11, 260) OO-model LION Name: String Age: Integer Weight: Integer 1088732 OID: 1088732 Name: Simba Age: 11 Weight: 260

  19. Object identity Why object identifiers are better than keys: • Key attributes not stable • Key values not stable • Object sharing Object identifiers can be used in relational databases but must be maintained by the users

  20. Inheritance Employees are persons The relational model PERSON(SS#, Name, City) EMPLOYEE(SS#, Salary) EMPLOYEE.SS# << PERSON.SS# OO model PERSON SS#: String Name: String City: String EMPLOYEE ISA PERSON Salary: Integer

  21. Inheritance in the relational model PERSON(SS#, Name, City) EMPLOYEE(SS#, Spouse#, Salary) EMPLOYEE.Spouse# << PERSON.SS# PERSON(SS#, Name, City) EMPLOYEE(Emp#, SS#, Salary) EMPLOYEE.SS# << PERSON.SS# PERSON PERSON ISA spouse EMPLOYEE EMPLOYEE

  22. Inheritance in OO databases PERSON SS#: String Name: String City: String EMPLOYEE Emp#: String Salary: Integer Spouse: PERSON PERSON SS#: String Name: String City: String EMPLOYEE ISA PERSON Emp#: String Salary: Integer PERSON PERSON ISA spouse EMPLOYEE EMPLOYEE

  23. Extended base types create table slides ( id int, date date, caption document, picture photo_CD_image); create table landmarks ( name varchar(30), location point); Find sunsets within a 20 kilometers radius around Sacramento. select id from slides P, landmarks L S where sunset (P.picture) and contains (P.caption, L.name) and L.location |20| S.location and S.name = 'Sacramento';

  24. Types of DBMSs Queries No queries Relational OO/OR File systems OO Simple data Complex data

  25. Why do we like types? • Types facilitate understanding • Types enable compact representations • Types enable query optimisation • Types facilitate consistency enforcement

  26. Background assumptions fortyped data • Data stable over time • Organisational body to control data • Exercise: Give an example of a context where these assumptions do not hold

  27. Semistructured data Semistructured data is schemaless and self describing The data and the description of the data are integrated

  28. Value Label name tel email “John” 112233 “john@123.edu” Label-value pairs • {name: “John”, tel: 112233, email: “john@123.edu”}

  29. name tel email 112233 “john@123.edu” first last “John” “Smith” Nested label-value pairs {name: {first: “John”, last: “Smith”}, tel: 112233, email: “john@123.edu”}

  30. Duplicate labels {name: {first: “John”, last: “Smith”}, tel: 112233, tel: 445566, tel: 778899, email: “john@123.edu”}

  31. Representing variations {person: {name: “John”, tel: 112233, email: “john@123.edu”}, person: {name: “John”, email: “john@123.edu”}, person: {name: “John”, age: 33, email: “john@123.edu”}, person: {name: “John”, tel: 112233, tel: 332211}}

  32. Representing relational data CAR PERSON Regno Manuf Weight Ssno City Weight AA11 Volvo 1100 1122 London 80 CC33 Volvo 900 2233 Paris 70 EE55 Saab 1000 3344 Berlin 80 {CAR: {row: {Regno: “AA11”, Manuf: “Volvo”, Weight: 1100}, {row: {Regno: “CC33”, Manuf: “Volvo”, Weight: 900}, {row: {Regno: “EE55”, Manuf: “Saab”, Weight: 1000}}, PERSON: {row: {Ssno: “1122”, City: “London”, Weight: 80}, {row: {Ssno: “2233”, City: “Paris”, Weight: 70}, {row: {Ssno: “3344”, City: “Berlin”, Weight: 80}}}

  33. An object graph This graph represents two people, Eva and Abel, where Abel is the child of Eva. &o1 and &o2 are object identifiers denoting the people Eva and Abel. person person child &o1 &o2 name age name age “Eva” 40 “Abel” 20

  34. person person child &o1 &o2 name age name age “Eva” 40 “Abel” 20 Representing objects {person: &o1{name: “Eva”, age: 40, child: &o2}, person: &o2{name: “Abel”, age: 20}} An object identifier, such as &o1, before a structure, binds the object identifier to the identity of that structure. The object identifier can then be used to refer to the structure.

  35. Finding Info on the Web Suppose you spot the following news article in a paper and want to find contact information to the relevant scientists. What do you do? A group of scientists from New Zealand have discovered a gene, called ADB, which increases the risk of catching the flu. The discovery was made possible thanks to the detailed registration in New Zealand of flu patients.

  36. Contact info flu ADB gene Searching and Navigating Genetic Causes to Disease Peter Black, Eric, Green New Zealand University www.nzu.nz/¨pg This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is This may cause flu. about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is A gene called ADB about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is A gene called ADB about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases. This paper is about gentic causes to contagious diseases.

  37. The Semantic Web • Structuring data in documents • XML (eXtensible Markup Language) • Structuring data about documents • RDF (Resource Description Framework) • Structuring the semantics of domains • Ontologies - shared conceptualisations

  38. Course goals • The course will familiarise the student with: • database models and query languages with respect to expressiveness and usability • theory and principles of object data bases • semistructured data and its applications • application areas for object and relational databases • interactive and embedded query languages • Semantic Web, including ontologies

  39. Lectures 1. Introduction to data models 2. Query languages for relational databases 3. Models and query languages for object databases 4. Embedded query languages 5. Models and query languages for semistructured data, XML 6. Semantic Web, introduction 7. Semantic Web, continued

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