Data and Knowledge Mgmt

8/8/2019 Data and Knowledge Mgmt

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Dr. Mrs. T. DEVI Ph.D. (UK)Reader and HeadSchool of Computer Science and Engineering

Bharathiar University

[email protected]

A T A A N D K N O W L E D G EM A N A G E M E N T


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Contents

Data and Information Relational Database

Data Base Management System DWH, DATA MINING KNOWLEDGE MANAGEMENT

DATA ANALYTICS AND BI CONCLUSIONS


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Data in Information Technology

D A ILY Q U ER IE S IN FO R M AT IO N SY ST EMD ATA

C an you bo ok a ticket

from H ea th row to N ew

D e lh i?

H ow m a n y p a rticip a n ts inth is co u rse ?

C an yo u d e live r th eorde red go od s b efore tw o

d a y s ?

- Is item J00 1 cap acitorou t of stock ?

Is o rd e r P0 0 1 stillp e n d in g ?

. .D a te o f tra ve l 2 1 1 9 7 R e se rva tio nSystem

Flig h t N u m b e r B A 1 4 6N u m b e r o f se a ts 3

/C o u rse N u m b e r o r C 01 L1 Educational SystemC ou rse N am e IMDSN u m b e r o f p a rticip a n ts 1 5

Ite m d e scrip tio n R esisto r M a n u fa ctu rin gSystem

. .D u e d a te 7 1 1 9 7Ite m co d e J0 0 1 In ve n to ry S yste m

D e scrip tio n C a p a cito rQ u a n tity o n h a n d 3 0

/O rd e r N u m b e r O U R 0 3 Pu rch a se O rd e rSystem

O rd e re d q u a n tity 2 0

D e live re d q u an tity 1 5. .D u e d a te 5 1 1 9 7

33333


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Data in Information Technology

D A ILY Q U ER IE S IN FO R M AT IO N SY ST EMD ATA

C an you bo ok a ticket

from H ea th row to N ew

D e lh i?

H ow m a n y p a rticip a n ts inth is co u rse ?

C an yo u d e live r th eorde red go od s b efore tw o

d a y s ?

- Is item J00 1 cap acitorou t of stock ?

Is o rd e r P0 0 1 stillp e n d in g ?

. .D a te o f tra ve l 2 1 1 9 7 R e se rva tio nSystem

Flig h t N u m b e r B A 1 4 6N u m b e r o f se a ts 3

/C o u rse N u m b e r o r C 01 L1 Educational SystemC ou rse N am e IMDSN u m b e r o f p a rticip a n ts 1 5

Ite m d e scrip tio n R esisto r M a n u fa ctu rin gSystem

. .D u e d a te 7 1 1 9 7Ite m co d e J0 0 1 In ve n to ry S yste m

D e scrip tio n C a p a cito rQ u a n tity o n h a n d 3 0

/O rd e r N u m b e r O U R 0 3 Pu rch a se O rd e rSystem

O rd e re d q u a n tity 2 0

D e live re d q u an tity 1 5. .D u e d a te 5 1 1 9 7


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Data

2.11.97 3 Capacitor 30

'raw' facts Raw facts is of limited use


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Types of Data

Numeric 0, 1, 2,

3, ......, 9

Texta, b, ... z, A,

B,.., Z0, 1, ... 9, *, /, (,

Graphic

Map

CAD

Graph


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Database

User 1

User 2

User 3

User 4

User 5

Database


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DBMS: Database Management SystemsDBMS: Database Management Systems

Database-A collection of data stored instandardized format, designed to beshared by multiple users.

Database Management System

-Software that defines a database,

stores the data, supports a querylanguage, produces reports, andcreates data entry screens.


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Managing Databases

Data Base Administrator Backups

Security and Privacy


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Data Base ManagementSystem

User DBMS

Database


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Data Base Administrator

Functions of DBA

Implementing the DB Providing Security for the DB

Monitoring performance

Designing standards

Providing Backup andRecoveryDBA


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Backup & Security

Backup - automatic backup

Security - by user account numberand password Login : user1

Password : ________

t t


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vantages sa vantages o

Relational Databases

Data redundancy reduced

Data integrity improved

Data security improved

Data consistency maintained

Easier data access and use.

Highly complex

Require specialised designers

ExpensiveVulnerable to hardware failure

Advantages outweigh the disadvantagesAdvantages outweigh the disadvantages

Advantages Disadvantages


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Distributed Databases

Networking System

Satellite

DB

A A A O S


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DATA WAREHOUSE

Reaping Benefits from Your Data

Data warehouseData gathered over time andstored in a separate database


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Data Warehouses - Basic concepts

What is a data warehouse?

A decision support database that ismaintained separately from theorganizations operational database

A data warehouse is a

subject-oriented

integrated

time-varying

non-volatile collection of data that is used primarily in

organizational decision making

-- W.H. Inmon, Building the Data Warehouse, 1992


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Operational Vs Warehouse Data

perationalDaily Activities

arehouseHistorical in Nature

Used to Obtain Various Perspectiv

Historical

Data Analysis

Insights Decisions


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DATA MINING

Automatic Extraction of Patternsof Information From Historical Data

Enabling Scientists to Focus on theMost Important Aspects of Their Research

What They Did Not KnowHad Not Even Thought of Asking


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Data Mining - On what kind ofdata?

Relational Databases

Database queries Used to access relational data (look-up

queries)

Written in relational query language, e.g.SQL

Defined with graphical user interface (GUI)Show me a list of all items that were sold in the last.quarter

Show me a list of all items that were sold in the last.quarter

, .Show me the total sales of the last month grouped by branch, .Show me the total sales of the last month grouped by branch

How many sales transactions occurred in the month of December?How many sales transactions occurred in the month of December?

Which sales person had the highest amount of sales? Which sales person had the highest amount of sales?

D t Mi i O h t ki d f


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u rc e in T or o n to

Data Mining - On what kind ofdata?

Data Warehouses

u rc e in C hi c a go

o ur ce i n Pa r i s

o ur ce i n To k y o

Give me an analysis of the company s sales per item type per branch forthe third quarter. ( )President AllElectronics

ive me an analysis of the company s sales per item type per branch forthe third quarter. ( )President AllElectronics

CleanTransformIntegra

teLoad

at a W ar e h ou s e i n Le uv e n

B1 B2 B3

IT1 12 11 14

IT2 25 16 42

IT3 32 9 21

Item

Typ

e

Branch

a n a l ys e

Data cube( )OLAP


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Data Mining - On what kind of data?Other Databases

Transactional database File where each record represents transaction = transaction identifier +

list of items (e.g. items purchased)

Market basket analysis

Object-Oriented/Relational databases Spatial databases

Temporal and time-series databases (e.g.stock exchange)

Text and multimedia databases

Heterogeneous/Legacy databases

World Wide Web

SALES

TransID List of Item_IDs

T100 I3,I8

T101I1,I8,I16,I21

DATA MINING


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DATA MINING

PROGRESS OF THE FIELD


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PROGRESS OF THE FIELD


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KNOWLEDGE ACCESS APPROACH

Th D t Fl d


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The Data Flood

"It is estimated that the amount ofinformation in the world doublesevery 20 months. What are we supposedto do with this flood of raw data? Clearlylittle of it will ever be seen by

human eyes."

--Proceedings of the 1995 Conference in Knowledge Discovery inDatabases (KDD)

Th D t Fl d


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The Data Flood

"Computers promisedfountains of wisdom but

delivered floods of data."

--Proceedings of the 1995 Conference in Knowledge Discovery inDatabases (KDD)
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KNOWLEDGE MANAGEMENT SYSTEM
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The Challenge of

Knowledge Management

Not only of how to develop newknowledge, BUT

how to locate and acquire othersknowledge

how to diffuse knowledge in yourorganisation

how to recognise knowledge

interconnections how to embody knowledge in products

how to get access to the learningexperiences of customers


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-1 29

WHAT IS KNOWLEDGE

MANAGEMENT? Process of capturing and making use of a

firms collective expertise anywhere in thebusiness

Doing the right thing, NOT doing things right

Viewing company processes as knowledgeprocesses

Knowledge creation, dissemination, upgrade,and application toward organizational

survival

Part science, part art, part luck


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-1 30

EXPLICIT AND TACIT

KNOWLEDGE

Oral CommunicationTacit Knowledge

50-95%

Information Request ExplicitKnowledge

Explicit Knowledge Base

5 %

Information Feedback


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THE KNOWLEDGEORGANIZATION

Create

KnowledgeOrganization

Collect

Organize

Refine

Disseminate

Culture

Leadership

TechnologyIntelligence

Maintain

Competition

KnowledgeManagementProcess

KM Drivers


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implementing knowledgemanagement?

1.5

1.7

1.9

2.1

2.3

2.5

2.7

2.9

3.1

3.3

3.5

Negative attitudes to knowledge

Unwillingness to share knowledge

Willingness to share knowledge buttoo little time for individuals to do so

Lack of skill in knowledge

management techniques

Lack of understanding ofknowledge management andbenefitsLack of appropriate technology

Lack of commitment to knowledgemanagement from seniormanagementLack of funding for knowledgemanagement initiatives

Current culture does not encourageknowledge sharing


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Crucial Concepts forKnowledge Management

=InformationKnowledge

Information is digitisableKnowledge exists in intelligent

systems


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Critical Concepts for KMWhats to Manage?

Organisational information

Organisational knowledge

Individual knowledge


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Establish effective information capture and&management systems processes

Way

/ &Identify map organisational individualknowledge capabilities your knowledge asset

register , Codify knowledge where possible but don t discard- ( )non codifiable tacit components

Nourish a culture that supports and rewardsknowledge sharing

Promote individual knowledge development

AND THEY ALL INTERACT!


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The Strategy Connection? What s Knowledge? The Car Story


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A li i f DM d T l


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1. Crime Detection

2. Marketing Churning3. Rolls Royce Engg KM3. KM Tool - PCPACK

Applications of DM and Tools

CRIME DETECTION


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CRIME DETECTION

West Midlands Police DepartmentUnited Kingdom

Challenge

Detemine a way to quickly &easily find patterns and trendsin unsolved Criminal cases

SOLUTION


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SOLUTION

1. Identify key case patterns &trends

2. In hopes of solving old cases3. Identifying new criminalbehavioural patterns

SoftwareClementines rapid modelling env.

RESULTS


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RESULTS

1. Matched unsolved cases with

known perpetrators

2. Target and catch repeat

offenders

Complicating factors


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Complicating factors

1. Shrinking resources

2. Few leads

3. Aging cases

Type of Cases


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Type of Cases

Cases that lack clear evidence

1. House Burglaries2. Vehicle theft

filed away until new evidenceis found

Electronic Case File


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Electronic Case File

Physical description of thieves

Modus Operandi (MO)

Clementine - 2 Kohonen n/w

Data Mining Process


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Data Mining Process

Cluster similar physicaldescriptions & MOs

Combine Clusters to see if groups of

similar physical descriptionscoincide with groups of similar MOs

Data Mining Process


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Data Mining Process...

GoodMatch?

Yes

Perpetrators

known?

Yes

No

No

Unsolved cases are committed bysame individuals

Further Investigation


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Clusters - statistics - similaritiesimportance

If the criminal is unknown but alarge cluster indicates thesame offender then

leads can be combinedand the case reprioritised



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Further Investigation..

Behaviour of repeat offenders

Goal of identifying crimes thatseem to fit their

Behavioural pattern


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NOWLEDGE MANAGEMENT TOO

C C


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PCPACK

Prof. Nigel Shadbolt,Univ. ofSouthampton

www.epistemics.co.uk

PCPACK
http://www.epistemics.co.uk/http://www.epistemics.co.uk/


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PCPACK

Step 1 : Download trial Version ofPCPACK

Step 2 : Install PCPACK

Step 3 :Click

Start Programs PCPACK TOOL LAUNCHER


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BASE


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NEW KB CREATED -LAUNCHER


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IMPORT AND MARK-UP NEWTRANSCRIPT


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PROTOCOL NAME


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PROTOCOL TOOL


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MARK CONCEPTS


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RETURN TOLAUNCHER


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CONCEPT TREE


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ANNOTATE A NODE


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TEMPLATE


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ANNOTATION (VIEW)


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To Publish on WEB


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Publisher


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PUBLISHER TOOL

REATE WEB ITE


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ADDRESS


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TO PREVIEW WEBSITE


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KM LIFE CYCLE

Four-Process View of KM:

Capturing data entry, scanning, voiceinput, interviewing, brainstorming

Organizing cataloging, indexing,filtering, linking, codifying

Refining contexualizing, collaborating,contexualizing, collaborating,

compacting, Projecting, mining Transfer flow, sharing, alert, push

BOOKS


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BOOKS

'The Knowledge ManagementFieldbook' by Bukowitzand Williams,published by Prentice Hall, 2000

MOKA book - 'Managing EngineeringKnowledge: MOKA Methodology forKnowledge Based EngineeringApplications', edited by M Stokes,ASME, 2001 this book discusses on

how to apply knowledgemanagement to engineeringproblems, MOKA is a popular KMbasis in the USA)

W b it


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Websites

www.brint.com

What is Business Intelligence


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What is Business Intelligence(BI)?

Process for Increasing the

Competitive Advantage of aCompany by Intelligent use of

available Data inDecision-making.


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Business Intelligence

Global term for all Processes, Techniques, and

Tools that support Business

Decision Making based on Information

Technology


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S


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Summary

User 1

User 4Database

.....

.....

.....

.....

...


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Summary

Introduction to Data

Databases and DBMS

DWH, DM

Knowledge Management

DATA ANALYTICS and BI


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Documents

Data and Knowledge Mgmt