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Generalized Model for Active Databases and Oracle Triggers
Triggers are executed when a specified condition occurs during insert/delete/update
Triggers are action that fire automatically based on these conditions
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Triggers follow an Event-condition-action (ECA) model • Event: Database modification E.g., insert, delete, update,
• Condition: Any true/false expression Optional: If no condition is specified then condition is
always true• Action: Sequence of SQL statements that will be automatically
executed
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When a new employees is added to a department, modify the Total_sal of the Department to include the new employees salary;
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;
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From the previous example we can find that:• Logically this means that we will CREATE a TRIGGER, let us call the
trigger Total_sal1
This trigger will execute AFTER INSERT ON Employee table (EVENT)
It will do the following FOR EACH ROW
WHEN NEW.Dno is NOT NULL (CONDITION)
The trigger will UPDATE DEPARTMENT (ACTION) By SETting the new Total_sal to be the sum of old Total_sal and NEW. Salary WHERE the Dno matches the NEW.Dno;
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CREATE TRIGGER <name>• Creates a trigger if one does not exist
ALTER TRIGGER <name>• Alters a trigger if one does exist
Works in both cases, whether a trigger exists or not
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Conditions can be one of the following: AFTER
• Executes after the event BEFORE
• Executes before the event INSTEAD OF
• Executes instead of the event Note that event does not execute in this case INSTEAD OF triggers are used for modifying views
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Triggers can be • Row-level
FOR EACH ROW specifies a row-level trigger Executed separately for each affected row NEW and OLD keywords can be used here
• Statement-level Default (when FOR EACH ROW is not specified) Execute once for the SQL statement,
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Any true/false condition to control whether a trigger is activated or not• Absence of condition means that the trigger will
always execute for the even (True)• Otherwise, condition is evaluated
before the event for BEFORE trigger after the event for AFTER trigger
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Action can be• One SQL statement• A sequence of SQL statements enclosed between a
BEGIN and an END Action specifies the relevant modifications
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Design and Implementation Issues for Active Databases
An active database allows users to make the following changes to triggers (rules)• Activate• Deactivate• Drop
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An event can be considered in 3 ways: Immediate consideration:
• Part of the same transaction and can be one of the following depending on the situation Before After Instead of
Deferred consideration:• Condition is evaluated at the end of the transaction
Detached consideration:• Condition is evaluated in a separate transaction
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Potential Applications for Active Databases Notification
• Automatic notification when certain condition occurs• Enforcing integrity constraints
Triggers are smarter and more powerful than constraints• Maintenance of derived data
Automatically update derived data and avoid anomalies due to redundancy E.g., trigger to update the Total_sal in the earlier example
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Triggers in SQL-99 Can alias variables inside the REFERENCING
clause
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Trigger examples in SQL-99
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Time Representation, Calendars, and Time Dimensions Time is considered ordered sequence of points in some
granularity• Use the term choronon instead of point to describe minimum
granularity
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A calendar organizes time into different time units for convenience.• Accommodates various calendars
Gregorian (western), Chinese, Islamic, Etc. Point events
• Single time point event E.g., bank deposit
• Series of point events can form a time series data Duration events
• Associated with specific time period Time period is represented by start time and end time
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Transaction time• The time when the information from a certain
transaction becomes valid
Bitemporal database• Databases dealing with two time dimensions
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Incorporating Time in Relational Databases Using Tuple Versioning
Add to every tuple • Valid start time • Valid end time
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Incorporating Time in Object-Oriented Databases Using Attribute Versioning
A single complex object stores all temporal changes of the object
Time varying attribute• An attribute that changes over time
E.g., age
Non-Time varying attribute• An attribute that does not changes over time
E.g., date of birth
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Declarative Language• Language to specify rules
Inference Engine (Deduction Mechanism)• Can deduce new facts by interpreting the rules• Related to logic programming
Prolog language (Prolog => Programming in logic) Uses backward chaining to evaluate
Top-down application of the rules
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Specification consists of:• Facts
Similar to relation specification without the necessity of including attribute names
• Rules Similar to relational views (virtual relations that are not
stored)
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Predicate has• a name• a fixed number of arguments
Convention: Constants are numeric or character strings Variables start with upper case letters
• E.g., SUPERVISE(Supervisor, Supervisee) States that Supervisor SUPERVISE(s) Supervisee
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Rule• Is of the form head :- body
where “:-” is read as “if and only if”• E.g., SUPERIOR(X,Y) :- SUPERVISE(X,Y)• E.g., SUBORDINATE(Y,X) :- SUPERVISE(X,Y)
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Query• Involves a predicate symbol followed by some variable
arguments to answer the question• E.g., SUPERIOR(james,Y)?• E.g., SUBORDINATE(james,X)?
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(a) Prolog notation (b) Supervisory tree
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• Program is built from atomic formulas Literals of the form p(a1, a2, … an) where
p predicate name n is the number of arguments
Built-in predicates are included E.g., <, <=, =, etc.
• A literal is either An atomic formula An atomic formula preceded by not
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A formula can have quantifiers• Universal• Existential
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In clausal form, a formula must be transformed into another formula with the following characteristics• All variables are universally quantified• Formula is made of a number of clauses where each
clause is made up of literals connected by logical ORs only
• Clauses themselves are connected by logical ANDs only.
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Any formula can be converted into a clausal form• A specialized case of clausal form are horn clauses
that can contain no more than one positive literal Datalog program are made up of horn clauses
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There are two main alternatives for interpreting rules:• Proof-theoretic• Model-theoretic
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Proof-theoretic• Facts and rules are axioms • Ground axioms contain no variables• Rules are deductive axioms• Deductive axioms can be used to construct new facts
from existing facts• This process is known as theorem proving
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Model-theoretic• Given a finite or infinite domain of constant values, we
assign the predicate every combination of values as arguments
• If this is done for every predicated, it is called interpretation
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Model• An interpretation for a specific set of rules
Model-theoretic proofs • Whenever a particular substitution to the variables in
the rules is applied, if all the predicated are true under the interpretation, the predicate at the head of the rule must also be true
Minimal model• Cannot change any fact from true to false and still get a
model for these rules
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A program is safe if it generates a finite set of facts Two main methods of defining truth values• Fact-defined predicates (or relations)
Listing all combination of values that make a predicate true• Rule-defined predicates (or views)
Head (LHS) of one or more Datalog rules, for example:
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Many operations of relational algebra can be defined in the for of Datalog rules that defined the result of applying these operations on database relations (fact predicates)• Relational queries and views can be easily specified in
Datalog
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Define an inference mechanism based on relational database query processing concepts
• Example: predicate dependencies
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Knowledge databases
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KDD: A Definition
106-1012 bytes:we never see the wholedata set, so will put it in the memory of computers
What is the knowledge?How to represent and use it?
Then run Data Mining algorithms
KDD is the automatic extraction of non-obvious, hidden knowledge from large volumes of data.KDD is the automatic extraction of non-obvious, hidden knowledge from large volumes of data.
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We often see data as a string of bits, or numbers and symbols, or “objects” which we collect daily.
Information is data stripped of redundancy, and reduced to the minimum necessary to characterize the data.
Knowledge is integrated information, including facts and their relations, which have been perceived, discovered, or learned as our “mental pictures”.
Data, Information, Knowledge
Knowledge can be considered data at a high level of abstraction and generalization.
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From Data to KnowledgeFrom Data to Knowledge From Data to KnowledgeFrom Data to Knowledge
...10, M, 0, 10, 10, 0, 0, 0, SUBACUTE, 37, 2, 1, 0,15,-,-, 6000, 2, 0, abnormal, abnormal,-, 2852, 2148, 712, 97, 49, F,-,multiple,,2137, negative, n, n, ABSCESS,VIRUS
12, M, 0, 5, 5, 0, 0, 0, ACUTE, 38.5, 2, 1, 0,15, -,-, 10700,4,0,normal, abnormal, +, 1080, 680, 400, 71, 59, F,-,ABPC+CZX,, 70, negative, n, n, n, BACTERIA, BACTERIA
15, M, 0, 3, 2, 3, 0, 0, ACUTE, 39.3, 3, 1, 0,15, -, -, 6000, 0,0, normal, abnormal, +, 1124, 622, 502, 47, 63, F, -,FMOX+AMK, , 48, negative, n, n, n, BACTE(E), BACTERIA
16, M, 0, 32, 32, 0, 0, 0, SUBACUTE, 38, 2, 0, 0, 15, -, +, 12600, 4, 0,abnormal, abnormal, +, 41, 39, 2, 44, 57, F, -, ABPC+CZX, ?, ? ,negative, ?, n, n, ABSCESS, VIRUS...
Medical Data by Dr. Tsumoto, Tokyo Med. & Dent. Univ., 38 attributes
Numerical attribute categorical attribute missing values class labels
IF cell_poly <= 220 AND Risk = n AND Loc_dat = + AND Nausea > 15THEN Prediction = VIRUS [87,5%]
[confidence, predictive accuracy]
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People gathered and stored so much data because they think some valuable assetsare implicitly coded within it.
Raw data is rarely of direct benefit.Its true value depends on the ability to extract information useful for decision support.
Impractical Manual Data Analysis
knowledge base
inference engine
How to acquire knowledge for knowledge-based systems remains as the main difficult and crucial problem. ?
Tradition: via knowledge engineersNew trend: via automatic programs
Data Rich Knowledge Poor
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Volume
Value
EDP
MIS
DSS
Benefits of Knowledge Discovery
Generate
Rapid Response
Disseminate
EDP: Electronic Data ProcessingMIS: Management Information Systems
DSS: Decision Support Systems
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Lecture 1: Overview of KDD
1. What is KDD and Why ?
2. The KDD Process
4. Data Mining Methods
3. KDD Applications
5. Challenges for KDD
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The KDD processThe non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data - Fayyad, Platetsky-Shapiro, Smyth (1996)
non-trivial process
Multiple process
valid Justified patterns/models
novel Previously unknown
useful Can be used
understandableby human and machine
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The Knowledge Discovery The Knowledge Discovery ProcessProcess The Knowledge Discovery The Knowledge Discovery ProcessProcess
KDD is inherentlyinteractive and
iterative
a step in the KDD process consisting of methods that produce useful patterns or models from the data, under some acceptable computational efficiency limitations
1
2
3
4
5
Understand the domain and Define problems
Collect and Preprocess Data
Data MiningExtract Patterns/Models
Interpret and Evaluate discovered knowledge
Putting the results in practical use
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The KDD Process
Data organized by function
Create/selecttarget database
Select samplingtechnique and
sample data
Supply missing values
Normalizevalues
Select DM task (s)
Transform todifferent
representation
Eliminatenoisy data
Transformvalues
Select DM method (s)
Create derivedattributes
Extract knowledge
Find importantattributes &
value ranges
Test knowledge
Refine knowledge
Query & report generationAggregation & sequencesAdvanced methods
Data warehousing 1
2
3 4
5
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Main Contributing Areas of KDDMain Contributing Areas of KDD Main Contributing Areas of KDDMain Contributing Areas of KDD
DatabasesStore, access, search, update data (deduction)
StatisticsInfer info from data (deduction & induction, mainly numeric data)
Machine LearningComputer algorithms that
improve automatically through experience (mainly induction,
symbolic data)
KDD
[data warehouses:integrated data]
[OLAP: On-Line Analytical Processing]
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Potential Potential ApplicationsApplications Potential Potential ApplicationsApplications
Business information
- Marketing and sales data analysis- Investment analysis- Loan approval- Fraud detection- etc.
Manufacturing information
- Controlling and scheduling- Network management- Experiment result analysis- etc.
Scientific information- Sky survey cataloging- Biosequence Databases- Geosciences: Quakefinder- etc.
Personal information
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KDD: Opportunity and KDD: Opportunity and Challenges Challenges
KDD: Opportunity and KDD: Opportunity and Challenges Challenges
Data RichKnowledge Poor(the resource)
Enabling Technology(Interactive MIS, OLAP, parallel computing, Web, etc.)
Competitive Pressure
Data Mining TechnologyMature
KDD
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KDD workshops: since 1989.Inter. Conferences: KDD (USA), first in 1995;PAKDD (Asia), first in 1997; PKDD (Europe), first in 1997.ML’04/PKDD’04 (in Pisa, Italy)
Industry interests and competition: IBM, Microsoft, Silicon Graphics, Sun, Boeing, NASA, SAS, SPSS, …About 80% of the Fortune 500 companies are involved in data mining projects or using data mining systems.
JAPAN: FGCS Project (logic programming and reasoning).
“Knowledge Discovery is the most desirable end-product of computing”. Wiederhold, Standford Univ.
KDD: A New and Fast Growing Area
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Primary Tasks of Data Primary Tasks of Data MiningMining Primary Tasks of Data Primary Tasks of Data MiningMining
Classification
Deviation andchange detection
?
Summarization
Clustering
Dependency
Modeling
Regression
finding the descriptionof several predefined classes and classify a data item into one of them.
maps a data item to a real-valued prediction variable.
identifying a finite set of categories or clusters to describe
the data.
finding a compact description
for a subset of data
finding a model which describes
significant dependencies between variables.
discovering the most significant changes in the data
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Data General patterns
Examples
Cancerous Cell Data
Classification“What factors determine cancerous cells?”
Classification Algorithm
MiningAlgorithm
- Rule Induction- Decision tree- Neural Network
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If Color = light and Tails = 1 and Nuclei = 2Then Healthy Cell (certainty = 92%)
If Color = dark and Tails = 2 and Nuclei = 2Then Cancerous Cell (certainty = 87%)
Classification: Rule Induction“What factors determine a cell is cancerous?”
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Color = dark
Color = light
healthy
Classification: Decision Trees
#nuclei=1
#nuclei=2
#nuclei=1
#nuclei=2
#tails=1 #tails=2
cancerous
cancerous healthy
healthy
#tails=1 #tails=2
cancerous
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Healthy
Cancerous
“What factors determine a cell is cancerous?”
Classification: Neural Networks
Color = dark
# nuclei = 1
…
# tails = 2
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End of Unit-iv