2002.11.19- SLIDE 1IS 257 - Fall 2002 Object-Oriented, Intelligent and Object-Relational Database...

IS 257 - Fall 2002 2002.11.19- SLIDE 1

Object-Oriented, Intelligent and Object-Relational Database

ModelsUniversity of California, Berkeley

School of Information Management and Systems

SIMS 257: Database Management

IS 257 - Fall 2002 2002.11.19- SLIDE 2

Lecture Outline

• Review– Applications for Data Warehouses– Decision Support Systems (DSS)– OLAP (ROLAP, MOLAP)– Data Mining

• Thanks again to lecture notes from Joachim Hammer of the University of Florida

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What is Decision Support?

• Technology that will help managers and planners make decisions regarding the organization and its operations based on data in the Data Warehouse.– What was the last two years of sales volume

for each product by state and city?– What effects will a 5% price discount have on

our future income for product X?

• Increasing common term is KDD– Knowledge Discovery in Databases

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Conventional Query Tools

• Ad-hoc queries and reports using conventional database tools– E.g. Access queries.

• Typical database designs include fixed sets of reports and queries to support them– The end-user is often not given the ability to

do ad-hoc queries

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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

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Data Cube

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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

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Star Schema

• Typical design for the derived layer of a Data Warehouse or Mart for Decision Support– Particularly suited to ad-hoc queries– Dimensional data separate from fact or event

data• Fact tables contain factual or quantitative

data about the business• Dimension tables hold data about the

subjects of the business• Typically there is one Fact table with

multiple dimension tables

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Star Schema for multidimensional data

OrderOrderNoOrderDate…

SalespersonSalespersonIDSalespersonNameCityQuota

Fact TableOrderNoSalespersonidCustomernoProdNoDatekeyCitynameQuantityTotalPrice City

CityNameStateCountry…

DateDateKeyDayMonthYear…

ProductProdNoProdNameCategoryDescription…

CustomerCustomerNameCustomerAddressCity…

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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)

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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?

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Data Mining Applications

• Profiling Populations

• Analysis of business trends

• Target marketing

• Usage Analysis

• Campaign effectiveness

• Product affinity

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Data Mining Algorithms

• Market Basket Analysis

• Memory-based reasoning

• Cluster detection

• Link analysis

• Decision trees and rule induction algorithms

• Neural Networks

• Genetic algorithms

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Market Basket Analysis

• A type of clustering used to predict purchase patterns.

• Identify the products likely to be purchased in conjunction with other products– E.g., the famous (and apocryphal) story that

men who buy diapers on Friday nights also buy beer.

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Memory-based reasoning

• Use known instances of a model to make predictions about unknown instances.

• Could be used for sales forcasting or fraud detection by working from known cases to predict new cases

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Cluster detection

• Finds data records that are similar to each other.

• K-nearest neighbors (where K represents the mathematical distance to the nearest similar record) is an example of one clustering algorithm

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Link analysis

• Follows relationships between records to discover patterns

• Link analysis can provide the basis for various affinity marketing programs

• Similar to Markov transition analysis methods where probabilities are calculated for each observed transition.

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Decision trees and rule induction algorithms

• Pulls rules out of a mass of data using classification and regression trees (CART) or Chi-Square automatic interaction detectors (CHAID)

• These algorithms produce explicit rules, which make understanding the results simpler

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Neural Networks

• Attempt to model neurons in the brain

• Learn from a training set and then can be used to detect patterns inherent in that training set

• Neural nets are effective when the data is shapeless and lacking any apparent patterns

• May be hard to understand results

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Genetic algorithms

• Imitate natural selection processes to evolve models using– Selection– Crossover– Mutation

• Each new generation inherits traits from the previous ones until only the most predictive survive.

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Today

• Object-Oriented Database Systems

• Inverted File and Flat File DBMS

• Object-Relational DBMS– OR features in Oracle– OR features in PostgreSQL

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Object-Oriented DBMS Basic 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)

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• 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

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• 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

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• 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.

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• 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).

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• 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.

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Generalization Hierarchy

Employee NoName

AddressDate hired

Date of Birth

employee

Contract No.Date Hired

consultant

Annual SalaryStock Option

Salaried

Hourly Rate

Hourly

calculateAge

AllocateToContractcalculateStockBenefitcalculateWage

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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

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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

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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

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

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IntelligentDatabases

ExpertSystems

TraditionalDatabases

Hypermedia

Automaticdiscovery

ObjectOrientation

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Intelligent Database Architecture

Intelligent DatabaseEngine

High-LevelUser Interface

High-LevelTools

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Environment Components

Data Dictionary

Concept Dictionary

Flexible queries

Error detection

Automatic Discovery

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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

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

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

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• 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

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

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

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Object Relational Databases

• Background• Object Definitions

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

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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)

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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

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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

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Object-Relational SQL in ORACLE

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

• CREATE TABLE OF typename;

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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”

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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

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Selecting from an Object Table

• Just use the columns in the object…

• SELECT Name from Animal;

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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);

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What Does the Table Look like?

• DESCRIBE CUSTOMER;

• NAME TYPE

• -----------------------------------------------------

• CUSTOMER_ID NUMBER

• PERSON NAMED TYPE

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Inserting

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

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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...

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Selecting from Abstract Datatypes

• SELECT Customer_id, person.name from Customer;

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

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Updating

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

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Functions

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

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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;

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Example

• Select NAME, BALANCE_CHECK(NAME) from Worker;

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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...

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PostgreSQL

• Derived from POSTGRES– Developed at Berkeley by Mike Stonebraker

and his students (EECS) starting in 1986

• Postgres95– Andrew Yu and Jolly Chen adapted

POSTGRES to SQL and greatly improved the code base

• PostgreSQL– Name changed in 1996, and since that time

the system has been expanded to support most SQL92 features

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PostgreSQL Classes

• The fundamental notion in Postgres is that of a class, which is a named collection of object instances. Each instance has the same collection of named attributes, and each attribute is of a specific type. Furthermore, each instance has a permanent object identifier (OID) that is unique throughout the installation. Because SQL syntax refers to tables, we will use the terms table and class interchangeably. Likewise, an SQL row is an instance and SQL columns are attributes.

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Creating a Class

• You can create a new class by specifying the class name, along with all attribute names and their types:

CREATE TABLE weather ( city varchar(80), temp_lo int, -- low temperature temp_hi int, -- high temperature prcp real, -- precipitation date date);

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PostgreSQL

• Postgres can be customized with an arbitrary number of user-defined data types. Consequently, type names are not syntactical keywords, except where required to support special cases in the SQL92 standard.

• So far, the Postgres CREATE command looks exactly like the command used to create a table in a traditional relational system. However, we will presently see that classes have properties that are extensions of the relational model.

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PostgreSQL

• All of the usual SQL commands for creation, searching and modifying classes (tables) are available. With some additions…

• Inheritance

• Non-Atomic Values

• User defined functions and operators

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Inheritance

CREATE TABLE cities ( name text, population float, altitude int -- (in ft));

CREATE TABLE capitals ( state char(2)) INHERITS (cities);

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Inheritance

• In Postgres, a class can inherit from zero or more other classes.

• A query can reference either – all instances of a class – or all instances of a class plus all of its

descendants

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Inheritance

• For example, the following query finds all the cities that are situated at an attitude of 500ft or higher:

SELECT name, altitude FROM cities WHERE altitude > 500;+----------+----------+|name | altitude |+----------+----------+|Las Vegas | 2174 |+----------+----------+|Mariposa | 1953 |+----------+----------+

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Inheritance

• On the other hand, to find the names of all cities, including state capitals, that are located at an altitude over 500ft, the query is:

SELECT c.name, c.altitude FROM cities* c WHERE c.altitude > 500;which returns: +----------+----------+|name | altitude |+----------+----------+|Las Vegas | 2174 |+----------+----------+|Mariposa | 1953 |+----------+----------+|Madison | 845 |+----------+----------+

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Inheritance

• The "*" after cities in the preceding query indicates that the query should be run over cities and all classes below cities in the inheritance hierarchy

• Many of the PostgreSQL commands (SELECT, UPDATE and DELETE, etc.) support this inheritance notation using "*"

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Non-Atomic Values

• One of the tenets of the relational model is that the attributes of a relation are atomic– I.e. only a single value for a given row and

column

• Postgres does not have this restriction: attributes can themselves contain sub-values that can be accessed from the query language– Examples include arrays and other complex

data types.

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Non-Atomic Values - Arrays

• Postgres allows attributes of an instance to be defined as fixed-length or variable-length multi-dimensional arrays. Arrays of any base type or user-defined type can be created. To illustrate their use, we first create a class with arrays of base types.

CREATE TABLE SAL_EMP ( name text, pay_by_quarter int4[], schedule text[][]);

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Non-Atomic Values - Arrays

• The preceding SQL command will create a class named SAL_EMP with a text string (name), a one-dimensional array of int4 (pay_by_quarter), which represents the employee's salary by quarter and a two-dimensional array of text (schedule), which represents the employee's weekly schedule

• Now we do some INSERTSs; note that when appending to an array, we enclose the values within braces and separate them by commas.

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Inserting into Arrays

INSERT INTO SAL_EMP VALUES ('Bill', '{10000, 10000, 10000, 10000}', '{{"meeting", "lunch"}, {}}');

INSERT INTO SAL_EMP VALUES ('Carol', '{20000, 25000, 25000, 25000}', '{{"talk", "consult"}, {"meeting"}}');

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Querying Arrays

• This query retrieves the names of the employees whose pay changed in the second quarter:

SELECT name

FROM SAL_EMP

WHERE SAL_EMP.pay_by_quarter[1] <>

SAL_EMP.pay_by_quarter[2];+------+|name |+------+|Carol |+------+

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Querying Arrays

• This query retrieves the third quarter pay of all employees:

SELECT SAL_EMP.pay_by_quarter[3] FROM SAL_EMP;

+---------------+|pay_by_quarter |+---------------+|10000 |+---------------+|25000 |+---------------+

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Querying Arrays

• We can also access arbitrary slices of an array, or subarrays. This query retrieves the first item on Bill's schedule for the first two days of the week.

SELECT SAL_EMP.schedule[1:2][1:1] FROM SAL_EMP WHERE SAL_EMP.name = 'Bill';+-------------------+|schedule |+-------------------+|{{"meeting"},{""}} |+-------------------+

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User Defined Functions

• CREATE FUNCTION allows a Postgres user to register a function with a database. Subsequently, this user is considered the owner of the function

CREATE FUNCTION name ( [ ftype [, ...] ] ) RETURNS rtype AS {SQLdefinition} LANGUAGE 'langname' [ WITH ( attribute [, ...] ) ]CREATE FUNCTION name ( [ ftype [, ...] ] ) RETURNS rtype AS obj_file , link_symbol LANGUAGE 'C' [ WITH ( attribute [, ...] ) ]

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Simple SQL Function

• CREATE FUNCTION one() RETURNS int4

AS 'SELECT 1 AS RESULT'

LANGUAGE 'sql';

SELECT one() AS answer;

answer

--------

1

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External Functions

• This example creates a C function by calling a routine from a user-created shared library. This particular routine calculates a check digit and returns TRUE if the check digit in the function parameters is correct. It is intended for use in a CHECK contraint.

CREATE FUNCTION ean_checkdigit(bpchar, bpchar) RETURNS bool

AS '/usr1/proj/bray/sql/funcs.so' LANGUAGE 'c';CREATE TABLE product ( id char(8) PRIMARY KEY, eanprefix char(8) CHECK (eanprefix ~ '[0-9]{2} [0-9]{5}') REFERENCES brandname(ean_prefix), eancode char(6) CHECK (eancode ~ '[0-9]{6}'), CONSTRAINT ean CHECK (ean_checkdigit(eanprefix,

eancode)));

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Creating new Types

• CREATE TYPE allows the user to register a new user data type with Postgres for use in the current data base. The user who defines a type becomes its owner. typename is the name of the new type and must be unique within the types defined for this database.

CREATE TYPE typename ( INPUT = input_function, OUTPUT = output_function

, INTERNALLENGTH = { internallength | VARIABLE } [ , EXTERNALLENGTH = { externallength | VARIABLE } ]

[ , DEFAULT = "default" ] [ , ELEMENT = element ] [ , DELIMITER = delimiter ] [ , SEND = send_function ] [ , RECEIVE = receive_function ] [ , PASSEDBYVALUE ] )

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New Type Definition

• This command creates the box data type and then uses the type in a class definition:

CREATE TYPE box (INTERNALLENGTH = 8,

INPUT = my_procedure_1, OUTPUT = my_procedure_2);

CREATE TABLE myboxes (id INT4, description box);

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Rules System

• CREATE RULE name AS ON event

TO object [ WHERE condition ]

DO [ INSTEAD ] [ action | NOTHING ]

• Rules can be triggered by any event (select, update, delete, etc.)

2002.11.19- SLIDE 1IS 257 - Fall 2002 Object-Oriented, Intelligent and Object-Relational Database...

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