11/7/2000Database Management -- R. Larson Object-Oriented, Intelligent and Object-Relational Database Models University of California, Berkeley School

11/7/2000 Database Management -- R. Larson

Object-Oriented, Intelligent and Object-Relational Database

ModelsUniversity of California, Berkeley

School of Information Management and Systems

SIMS 257: Database Management


Review

• OLAP

• Data Mining


OLAP

• Online Line Analytical Processing– Intended to provide multidimensional views of

the data– I.e., the “Data Cube”– The PivotTables in MS Excel are examples of

OLAP tools


Data Cube


Operations on Data Cubes

• Slicing the cube– Extracts a 2d table from the multidimensional

data cube– Example…

• Drill-Down– Analyzing a given set of data at a finer level of

detail


Data Mining

• Data mining is knowledge discovery rather than question answering– May have no pre-formulated questions– Derived from

• Traditional Statistics

• Artificial intelligence

• Computer graphics (visualization)


Goals of Data Mining

• Explanatory – Explain some observed event or situation

• Why have the sales of SUVs increased in California but not in Oregon?

• Confirmatory– To confirm a hypothesis

• Whether 2-income families are more likely to buy family medical coverage

• Exploratory– To analyze data for new or unexpected relationships

• What spending patterns seem to indicate credit card fraud?


Data Mining Applications

• Profiling Populations

• Analysis of business trends

• Target marketing

• Usage Analysis

• Campaign effectiveness

• Product affinity


Data Mining Algorithms

• Market Basket Analysis

• Memory-based reasoning

• Cluster detection

• Link analysis

• Decision trees and rule induction algorithms

• Neural Networks

• Genetic algorithms


Today

• Object-Oriented Database Systems

• Inverted File and Flat File DBMS

• Object-Relational DBMS– OR features in Oracle


Object-Oriented DBMSBasic Concepts

• Each real-world entity is modeled by an object. Each object is associated with a unique identifier (sometimes call the object ID or OID)



• Each object has a set of instance attributes (or instance variables) and methods.– The value of an attribute can be an object or set

of objects. Thus complex object can be constructed from aggregations of other objects.

– The set of attributes of the object and the set of methods represent the object structure and behavior, respectively



• The attribute values of an object represent the object’s status. – Status is accessed or modified by sending

messages to the object to invoke the corresponding methods



• Objects sharing the same structure and behavior are grouped into classes.– A class represents a template for a set of similar

objects.– Each object is an instance of some class.



• A class can be defined as a specialization of of one or more classes. – A class defined as a specialization is called a

subclass and inherits attributes and methods from its superclass(es).



• An OODBMS is a DBMS that directly supports a model based on the object-oriented paradigm. – Like any DBMS it must provide persistent

storage for objects and their descriptions (schema).

– The system must also provide a language for schema definition and and for manipulation of objects and their schema

– It will usually include a query language, indexing capabilities, etc.


Generalization Hierarchy

Employee NoName

AddressDate hired

Date of Birth

employee

Contract No.Date Hired

consultant

Annual SalaryStock Option

Salaried

Hourly Rate

Hourly

calculateAge

AllocateToContractcalculateStockBenefitcalculateWage


Inverted File DBMS

• Usually similar to Hierarchic DBMS in record structure– Support for repeating groups of fields and

multiple value fields

• All access is via inverted file indexes to DBS specified fields.

• Examples: ADABAS DBMS from Software AG -- used in the MELVYL system


Flat File DBMS

• Data is stored as a simple file of records. – Records usually have a simple structure

• May support indexing of fields in the records.– May also support scanning of the data

• No mechanisms for relating data between files.

• Usually easy to use and simple to set up


Intelligent Database Systems

• Intelligent DBS are intended to handle more than just data, and may be used in tasks involving large amounts of information where analysis and “discovery” are needed.

The following is based on “Intelligent Databases” by Kamran Parsaye, Mark Chignell, Setrag Khoshafian and Harry WongAI Expert, March 1990, v. 5 no. 3. Pp 38-47



• They represent the evolution and merging of several technologies:– Automatic Information Discovery– Hypermedia– Object Orientation– Expert Systems– Conventional DBMS



IntelligentDatabases

ExpertSystems

TraditionalDatabases

Hypermedia

Automaticdiscovery

ObjectOrientation


Intelligent Database Architecture

Intelligent DatabaseEngine

High-LevelUser Interface

High-LevelTools


Environment Components

Data Dictionary

Concept Dictionary

Flexible queries

Error detection

Automatic Discovery


Intelligent Databases

• Data Dictionary contains the system metadata

• Concept Dictionary defines ‘virtual fields’ based on approximate definitions

• Data Analysis and discovery– Find patterns– detect errors– Process queries



• Automatic Discovery– Data comprehension– Form Hypotheses– Make queries– View results and perhaps modify hypotheses– Repeat



• Automatic Error Detection– Integrity Constraints– Rule systems– Analysis of data for anomalies



• Flexible Query Processing– Approximate and “fuzzy” queries

• SELECT NAME, AGE, TELEPHONE FROM PERSONEL WHERE NAME = ‘Dovid Smith’ and AGE IS-CLOSE-TO 19;

• confidence factors

– Ranked query results



• Intelligent User Interfaces– Hyperlinked data in the data/knowledge base– Multimedia presentations– Dynamic linking of related information



• Intelligent Database Engine– OO support– Inference features– Global optimization– Rule manager– Explanation manager– Transaction manager– Metadata manager– Access module– Multimedia manager


Object Relational Databases

• Background• Object Definitions

– inheritance• User-defined datatypes• User-defined functions


Object Relational Databases• Began with UniSQL/X unified object-oriented and

relational system• Some systems (like OpenODB from HP) were

Object systems built on top of Relational databases.

• Miro/Montage/Illustra built on Postgres.• Informix Buys Illustra. (DataBlades)• Oracle Hires away Informix Programmers.

(Cartridges)


Object Relational Data Model• Class, instance, attribute, method, and integrity

constraints• OID per instance• Encapsulation• Multiple inheritance hierarchy of classes• Class references via OID object references• Set-Valued attributes• Abstract Data Types


Object Relational Extended SQL (Illustra)

• CREATE TABLE tablename {OF TYPE Typename}|{OF NEW TYPE typename} (attr1 type1, attr2 type2,…,attrn typen) {UNDER parent_table_name};

• CREATE TYPE typename (attribute_name type_desc, attribute2 type2, …, attrn typen);

• CREATE FUNCTION functionname (type_name, type_name) RETURNS type_name AS sql_statement


Object-Relational SQL in ORACLE

• CREATE (OR REPLACE) TYPE typename AS OBJECT (attr_name, attr_type, …);

• CREATE TABLE OF typename;


Example

• CREATE TYPE ANIMAL_TY AS OBJECT (Breed VARCHAR2(25), Name VARCHAR2(25), Birthdate DATE);

• Creates a new type

• CREATE TABLE Animal of Animal_ty;

• Creates “Object Table”


Constructor Functions

• INSERT INTO Animal values (ANIMAL_TY(‘Mule’, ‘Frances’, TO_DATE(‘01-APR-1997’, ‘DD-MM-YYYY’)));

• Insert a new ANIMAL_TY object into the table


Selecting from an Object Table

• Just use the columns in the object…

• SELECT Name from Animal;


More Complex Objects• CREATE TYPE Address_TY as object (Street

VARCHAR2(50), City VARCHAR2(25), State CHAR(2), zip NUMBER);

• CREATE TYPE Person_TY as object (Name VARCHAR2(25), Address ADDRESS_TY);

• CREATE TABLE CUSTOMER (Customer_ID NUMBER, Person PERSON_TY);


What Does the Table Look like?

• DESCRIBE CUSTOMER;

NAME TYPE

-----------------------------------------------------

CUSTOMER_ID NUMBER

PERSON NAMED TYPE


Inserting

• INSERT INTO CUSTOMER VALUES (1, PERSON_TY(‘John Smith’, ADDRESS_TY(‘57 Mt Pleasant St.’, ‘Finn’, ‘NH’, 111111)));


Selecting from Abstract Datatypes

• SELECT Customer_ID from CUSTOMER;

• SELECT * from CUSTOMER;

CUSTOMER_ID PERSON(NAME, ADDRESS(STREET, CITY, STATE ZIP))---------------------------------------------------------------------------------------------------1 PERSON_TY(‘JOHN SMITH’, ADDRESS_TY(‘57...


Selecting from Abstract Datatypes

• SELECT Customer_id, person.name from Customer;

• SELECT Customer_id, person.address.street from Customer;


Updating

• UPDATE Customer SET person.address.city = ‘HART’ where person.address.city = ‘Briant’;


Functions

• CREATE [OR REPLACE] FUNCTION funcname (argname [IN | OUT | IN OUT] datatype …) RETURN datatype (IS | AS) {block | external body}


Example

Create Function BALANCE_CHECK (Person_name IN Varchar2) RETURN NUMBER is BALANCE NUMBER(10,2) BEGIN

SELECT sum(decode(Action, ‘BOUGHT’, Amount, 0)) - sum(decode(Action, ‘SOLD’, amount, 0)) INTO BALANCE FROM LEDGER where Person = PERSON_NAME;

RETURN BALANCE;END;


Example

• Select NAME, BALANCE_CHECK(NAME) from Worker;


TRIGGERS

• Create TRIGGER UPDATE_LODGING INSTEAD OF UPDATE on WORKER_LODGING for each row BEGIN

• if :old.name <> :new.name then update worker set name = :new.name where name = :old.name;

• end if;• if :old.lodging <> … etc...

Documents

11/7/2000Database Management -- R. Larson Object-Oriented, Intelligent and Object-Relational Database Models University of California, Berkeley School