Enhanced Data Models forAdvanced Applications

Nguyễn ToànNguyễn Hữu Vũ

Contents

Active Database and Triggers1.

Temporal Database Concepts2.

Multimedia Database3.

Deductive Database4.

Active Database AND Triggers

What are “Rules”? Actions are automatically triggered by

certain events.

And “Triggers”? A technique for specifying certain types of

active rules.

“Triggers” has existed in early versions of the SQL specification and they are now part of SQL-99 standard.

Generalized Model For Active Databases and Triggers

The Event-Condition-Action (ECA) model.

A rule has three components: Event(s): database update operation(s) Condition: determines whether the rule should

be excuted after the event occurred Action: action to be taken

• SQL command(s)• External program

Example

Name Ssn Salary Dno Supervisor_ssn

EMPLOYEE

Dname Dno Total_sal Manager_ssn

DEPARTMENT

Events: Inserting new employee tuples.(R1) Changing the salary of existing employees.(R2) Changing the assignment of existing employee

from one department to another.(R3) Delete employee tuples.(R4)

Example

Name Ssn Salary Dno Supervisor_ssn

EMPLOYEE

Dname Dno Total_sal Manager_ssn

DEPARTMENT

R1: Event: INSERT new employee Condition: Dno for new employee is not NULL Action: update value of Total_sal

Example

Name Ssn Salary Dno Supervisor_ssn

EMPLOYEE

Dname Dno Total_sal Manager_ssn

DEPARTMENT

R1:CREATE TRIGGER Total_sal1AFTER INSERT ON EMPLOYEEFOR EACH ROWWHEN (NEW.Dno IS NOT NULL)

UPDATE DEPARTMENTSET Total_sal = Total_sal + NEW.SalaryWHERE Dno = NEW.Dno;

Example

Name Ssn Salary Dno Supervisor_ssn

EMPLOYEE

Dname Dno Total_sal Manager_ssn

DEPARTMENT

R3: Event: UPDATE Dno attribute Condition: always excuted Action: update the Total_sal of the employee’s

old department + update the Total_sal of the employee’s new department

Example

Name Ssn Salary Dno Supervisor_ssn

EMPLOYEE

Dname Dno Total_sal Manager_ssn

DEPARTMENT

R3:CREATE TRIGGER Total_sal3AFTER UPDATE OF Dno ON EMPLOYEEFOR EACH ROWBEGIN

UPDATE DEPARTMENTSET Total_sal = Total_sal + NEW.SalaryWHERE Dno = NEW.Dno;UPDATE DEPARTMENTSET Total_sal = Total_sal - OLD.SalaryWHERE Dno = OLD.Dno;

END;

Oracle Triggers

<trigger>::= CREATE TRIGGER <trigger name>(AFTER|BEFORE) <trigger events> ON <table name>[FOR EACH ROW][WHEN <condition>]<trigger actions>;<triggering events>::= <trigger event> {OR <trigger

event>

<trigger event>::= INSERT|DELETE|UPDATE [OF <column name> {, <column name>}]

<trigger action>::= <PL/SQL block>

SQL-99 Triggers

<trigger>::= CREATE TRIGGER <trigger name>(AFTER|BEFORE) <trigger events> ON <table name>REFERENCING (NEW | PLD) (ROW | TABLE) AS

<reference name>

FOR EACH (ROW | STATEMENT)[WHEN <condition>]<trigger actions>;<triggering events>::= <trigger event> {OR <trigger

event><trigger event>::= INSERT|DELETE|UPDATE [OF

<column name> {, <column name>}]<trigger action>::= <PL/SQL block>

SQL-99 Triggers

R1:CREATE TRIGGER Total_sal1AFTER INSERT ON EMPLOYEEREFERENCING NEW ROW AS NFOR EACH ROWWHEN (N.Dno IS NOT NULL)

UPDATE DEPARTMENTSET Total_sal = Total_sal + N.SalaryWHERE Dno = N.Dno;

Design and Implementaion Issues

The 1st issue: activation, deactivation and grouping of rules. Deactivate command Activate command Drop command Rule sets Process rules command

Design and Implementaion Issues

The 2nd issue: Before, after, concurrently Separate transaction or a part of the same

transaction? Rule consideration

• Immediate consideration• Deferred consideration• Detached consideration

Excution consideration• …

Design and Implementaion Issues

The 3rd issue: Row-level rules Statement-level rules

Difficulty: No easy-to-use techniques for designing,

writing, verifying rules Limit the widespread use of active rules

Statement-Level Active Rules in STARBURST

R1S: CREATE RULE Total_sal1 ON EMPLOYEEWHEN INSERTEDIF EXISTS (SELECT * FROM INSERTED WHERE Dno IS NOT NULL)THEN UPDATE DEPARTMENT AS D

SET D.Total_sal= D.Total_sal +(SELECT SUM(I.Salary) FROM INSERTED AS (WHERE D.Dno=I.Dno)

WHERE D.Dno IN (SELECT Dno FROM INSERTED)

Potential Applications for Active Databases

Allow notification of certain conditions that occur Eg: an active database may be used to monitor, say,

the temperature of an industrial furnace

Enforce integrity constraints by specifying the types of events that may cause the caonstraints to be violated Eg: active rule monitor the GPA of students whenever

a new grade is entered.

Maintenance of derived data

Temporal Database Concepts

Temporal Databases: encompass all database applications that require some aspect of time when organizing their information.

TDB applications (healthcare, insurance…)The majority of DB have some temporal

information.

Time Representation, Calendars, and Time dimension

Points – ChrononCalendarEvent Information vs Duration (State)

InformationValid Time and Transaction Time Dimensions

Incorporating Time in RDBValid Time Relations

Name Ssn Salary Dno Supervisor_ssn

EMPLOYEE

Dname Dno Total_sal Manager_ssn

DEPARTMENT

Tuple Versioning

Name Ssn Salary Dno Supervisor_ssn Vst Vet

EMP_VT

Dname Dno Total_sal Manager_ssn Vst Vet

DEPT_VT

Vst, Vet : DATE

Incorporating Time in RDBValid Time Relations

Name Ssn Salary Dno Supervisor_ssn Vst Vet

EMP_VT

Dname Dno Total_sal Manager_ssn Vst Vet

DEPT_VT

Dname Dno Total_sal Manager_ssn Vst Vet

Research 5 300000 12345678 2001-09-20 2007-03-31

PR 3 65000 52134521 2005-05-19 now

Research 5 500000 87654321 2007-04-01 now

DEPT_VT

Incorporating Time in RDBTransaction Time Relations

Name Ssn Salary Dno Supervisor_ssn

EMPLOYEE

Dname Dno Total_sal Manager_ssn

DEPARTMENT

Name Ssn Salary Dno Supervisor_ssn Tst Tet

EMP_TT

Dname Dno Total_sal Manager_ssn Tst Tet

DEPT_TT

Tst, Tet : Timestamp

Incorporating Time in RDBBitemporal Relations

Name Ssn Salary Dno Supervisor_ssn

EMPLOYEE

Dname Dno Total_sal Manager_ssn

DEPARTMENT

Name Ssn Salary Dno Supervisor_ssn Vst Vet Tst Tet

EMP_VT

Dname Dno Total_sal Manager_ssn Vst Vet Tst Tet

DEPT_VT

Vst, Vet : DATETst, Tet :TIMESTAMP

Incorporating Time in RDBImplementation Considerations

Store all the tuples in the same tableCreate two tables:

Currently valid inforemation The rest of the tuples

Allows the DB administrator to have different access paths, such as indexes for each relation, and keeps the size of current table reasonable

Another option is to vertically partition the attributes of the TR into separate relations

Temporal Querying Constructsand the TSQL2 Language

TSQL2: extends SQL with constructs for temporal databases

CREATE TABLE statement is extended with an optional AS-clause AS VALID STATE<GRANULARITY>

(valid time relation with valid time period) AS VALID EVENT<GRANULARITY>

(valid time relation with valid time point) AS TRANSACTION

(transaction time relation with transaction time period) AS VALID STATE<GRANULARITY> AND TRANSACTION

(bitemporal relation, valid time period) AS VALID EVENT<GRANULARITY> AND TRANSACTION

(bitemporal relation, valid time point)

Temporal Querying Constructsand the TSQL2 Language

Some of the more common operations used in queries: [T.Vst, T.Vet] INCLUDES [T1, T2]

(T1>=T.Vst AND T2<=T.Vet) [T.Vst, T.Vet] INCLUDED IN [T1, T2]

(T1<=T.Vst AND T2>=T.Vet) [T.Vst, T.Vet] OVERLAPS [T1, T2]

(T1<=T.Vet AND T2>=T.Vst) [T.Vst, T.Vet] BEFORE [T1, T2] (T1>=T.Vet) [T.Vst, T.Vet] AFTER [T1, T2] (T2<=T.Vst) [T.Vst, T.Vet] MEETS_BEFORE [T1, T2]

(T1=T.Vet+1) [T.Vst, T.Vet] MEETS_AFTER [T1, T2]

(T2+1=T.Vst)

Multimedia Database

As hardware becomes more powerful and as software becomes more sophisticated, it is increasingly possible to make use of multimedia data, such as images and video

Example

Consider a police investigation of a large-scale drug operation. This investigation may generate the following types of data Video data captured by surveillance cameras that record the

activities taking place at various locations. Audio data captured by legally authorized telephone wiretaps. Image data consisting of still photographs taken by investigators. Document data seized by the police when raiding one or more

places. Structured relational data containing background information,

back records, etc., of the suspects involved. Geographic information system data remaining geographic data

relevant to the drug investigation being conducted.

Multimedia Database - Possible Possible QueriesQueries

Image Query (by example): Police officer Rocky has a photograph in front of him. He wants to find the identity of the person in the picture. Query: “Retrieve all images from the image library in which

the person appearing in the (currently displayed) photograph appears”

Image Query (by keywords): Police officer Rocky wants to examine pictures of “Big

Spender”. Query: "Retrieve all images from the image library in which

“Big Spender” appears."

Multimedia Database - Possible Possible QueriesQueries

Video Query: Police officer Rocky is examining a surveillance video of a particular person

being fatally assaulted by an assailant. However, the assailant's face is occluded and image processing algorithms return very poor matches. Rocky thinks the assault was by someone known to the victim.

Query: “Find all video segments in which the victim of the assault appears.” By examining the answer of the above query, Rocky hopes to find other

people who have previously interacted with the victim.

Heterogeneous Multimedia Query: Find all individuals who have been photographed with “Big Spender” and

who have been convicted of attempted murder in South China and who have recently had electronic fund transfers made into their bank accounts from ABC Corp.

Multimedia Database

Multimedia databases : provide features to store, query different types of multimedia information, such as images,video clips,audio clips, documents

The types of queries are content-based retrieval

In a multimedia database, it might be reasonable to have a query that asks for, say, the top 10 images that are similar to a fixed image. This is in contrast to a relational database,where the answer to a query is simply a set

Model in multimedia database Based on automatic analysis: DBMS scan to identify mathematic

characteristic of source index Based on manual identification: person scan multimedia sources

to identify and catalogindex

Image Characteristic

An image : stored as a set of pixel or cell values,or in compressed form (gif,jpeg,mpeg)

Each pixel: contain a pixel valueCompressed Standard: use various mathematic

transformation to reduce the number of pixelsUsing homogeneity predicate: define

conditions to divide image into homogenerous segment automaticaly Eg: adjacent cell have similar pixel ->a segment

Image Query

Typical Query: find images that similar to a given image

2 main techniques to solve: Distance function: compare to find result Transformation approach: using some

transformation to transform image->find result

Video Characteristic

A video : represented as a sequences of imagesDivided into: video segments, the images in

same segment similar object, activity (person, house, car, talking, delivering)

Segment is indexed ,by frame segment trees technique

Often be compressed

Text/document Characteristic

Document: contains full textIndexed by keyword in text, using 2 techniques

SVD(sigular value decompostion), telescopeing vector tree group similar document

Audio Source characteristic

Stored as recorded messageDiscrete transforms are used to identify main

characteristic of a voice-> make similar-based indexing and retreiving

Deductive Database

A deductive database system is a database system which can make Deductive deductions (ie: conclude additional facts) based on rules and facts stored in the database Specify rules through a declarative

language(datalog) Have an inference engine to deduce new fact

from rules

Facts

Facts are specified as the same way the relations are specified in the Relational Database Except it is not necessary to include the attribute

names. The meaning of an attribute value in a tuple is

determined solely by its position in the tuple. Eg:

• supervise(james,jennifer).• supervise(james,franklin).

predicate arguments

Rules

They specify “virtual relations” that are not actually stored but that can be formed from the facts by applying deduction mechanisms based on the rule specifications. somewhat similar to relational views, but different in

the sense that it may involve recursion. Eg: Rules:

• subordinate(X,Y) :- superior(Y,X).• superior(X,Y) :- supervise(X,Z), superior(Z,Y).• superior(X,Y) :- supervise(X,Y).

Deductive Database

The evaluation of Prolog is based on backward chaining technique

Forward chaining: starts with the available data and uses inference rules to extract more data

Backward chaining: starts with the goal

Examples

There are some rules: If X croaks and eats flies - Then X is a frog If X chirps and sings - Then X is a canary If X is a frog - Then X is green If X is a canary - Then X is yellow

My goal is to conclude the color of my pet Fritz, given that he croaks and eats flies.

Examples RDBMS provide well-proven, robust, reliable mechanisms for managing data 1) Portability.

Relational model uses SQL to access the data. SQL language is well standardized and is largely the same across different database vendors. If you know how to write SQLs for Oracle, for example, you'll be able to also do so for DB/2, Informix, Sybase and others.

It is often possible for applications to work with relational database systems from different vendors. require in-depth knowledge of vendor-specific programming SQL allows to build very sophisticated queries understood by any DB professional who knows SQL There is no serious competing data management technology, and a huge amount of data is committed to

RDBMSs. Relational databases let you manipulate data in complex, interesting ways, allowing you to retrieve all records that match your specific criteria, cross- reference different tables, and update records in bulk And when your data is saved in various formats on different computers, security and privacy are especially daunting, not to mention backup, recovery, and availability “ ”

Examples

There are some rules:

most widely used model :vedor IBM

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