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1. CS-651 Intelligent Information Systems
Khalid Saleem
Spring 2022

2. “Computersare useless. They only give answers.”
Pablo Picasso

3. So, we need to device right
questions for the computers to
answer.
Come up with systems which can
help us in this activity ….

4. Any system which gives us
knowledge ….

5. PSU definition of IIS Research
 The goal of the Intelligent Information Systems
Research is to explore and support all levels of
research that will improve and enhance our ability
to generate, manage, search, and mine
information and knowledge. Current research
covers Internet database design and analysis,
mobile Web computing, Web mining and
navigation, Web agents, novel and intelligent
Web tools, multimedia retrieval, Web and internet
models, Web usage, automatic content analysis
and digital libraries, Web search, niche search
engines, semantic web, scientific databases, data
mining, and information retrieval.
 All about Web ….

6. Knowledge
Information
Data
 Data : Simple things; easily captured, structured,
transferred, compressible and quantifiable
 Information : Relevant and related data having
some purpose; needs consensus on meaning and
human mediation necessary
 Knowledge : Valuable information from human
mind; contextual; hard to capture electronically and
structure; mostly tacit
Source : Adapted from Thomas H. Davenport, Information Ecology

7. Transactio
n Systems
Information
Systems
Data
Mining
OLAP
Decision Support
Systems

8. Definition - DM and KDD
 Data mining(DM) is a step in the knowledge
discovery process consisting of particular data
mining algorithms that, under some acceptable
computational efficiency limitations, find patterns
or models in data
 Knowledge discovery in databases (KDD)
(Fayyad et al, 1996), is the process of identifying
valid, novel, potentially useful, and ultimately
understandable patterns or models in data.

9. Data Items
 Data items refer to an elementary description
of things, events, activities, and transactions
 that are recorded, classified, and stored but
are not organized to convey any specific
meaning.
 Data items can be numbers, letters, figures,
sounds, or images. Examples of data items
are a
 student grade in a class and the number of
hours an employee worked in a certain week.

10. Information
 Information refers to data that have been
organized so that they have meaning and
value to the recipient.
 For example, a grade point average (GPA) is
data, but a student’s name coupled with his or
her GPA is information.
 The recipient interprets the meaning and
drawsconclusions and implications from the
information.

11. Knowledge
 Knowledge consists of data and/or
information that have been organized and
processed
 to convey understanding, experience,
accumulated learning, and expertise as they
apply to a current business problem. For
example, a company recruiting at your
university has found
 Over time that students with grade point
averages over 3.0 have had the most success
in its management program.
 Based on its experience, that company may

12. Information
and
Information Systems

13. Types of Information Systems
KEY SYSTEM APPLICATIONS IN
THE ORGANIZATION

14. Major Types of
Systems
• Executive Support Systems (ESS)
• Decision Support Systems (DSS)
• Management Information Systems (MIS)
• Knowledge Work Systems (KWS)
• Office Systems
• Transaction Processing Systems (TPS)
MAJOR TYPES OF SYSTEMS IN
ORGANIZATIONS

15. MAJOR TYPES OF SYSTEMS IN
ORGANIZATIONS

16. Task – LOSE WEIGHT!
 UK survey - relevant respondents were asked to
name the top reasons as to how using dieting
apps on their Smartphone has helped them to
lose weight. The five highest ranked answers
emerged as follows:
 1. Easier to track calories and food intake at the
push of a button (47%)
 2. Can check calorie content of items before
deciding to eat them (36%)
 3. Helpful for planning healthy and nutritious
meals in advance (32%)
 4. Helps keep me motivated (24%)
 5. Cheaper and easier alternative to diet books
and magazines (18%)

17. Information System
 Executive Support System
 Management Information System
 Decision Support System
 Knowledge Work System
 Office System
 Transaction Processing System
5 year sales plan
Inventory Control
Pricing/Profit Analysis
Engineering/Graphics
Word Processing/Agenda
Order Processing
6 month diet plan
What should be in refrigerator
Best food within budget
Which food to eat with which food
Keeping agenda of eating timings

18. Big Data …. Open Data

19. Magnitude of Data
 Human brain capacity 2.5 PETABYTES
 Total digital data created 422 EXABYTES (2008)
 Web size - 98 PETABYTES (2010)
 Total genome sequences of all people on Earth 4.75
EXABYTES
 Web users - 2 Billion + (2011)
 World’s digital storage capacity 1 ZETTABYTE (2011)
 Digital data created 1.8 ZETTABYTES (2011) 2.7 ZB
(2012)
 Digital Data to be produced - 35 ZETTABYTES (by 2020 )
 Drastic price reduction in per Gigabyte production of storage
 => All data is being or is going to be conserved!
 => Huge data centres
 1 bit on 12 atoms …. 1 bit on 1000000 atoms
IDC Digital Universe 2010/ Popular Science Nov 2011/ IBM
GIGA 9
.
.
TERA 12
.
.
PETA 15
.
.
EXA 18
.
.
ZETTA 21
.
.
YOTTA 24
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20. Some systems we will come
across…
 Decision Support Systems
 Strategic Information Systems
 OLAP
 Executive Information Systems
 Enterprise Information Systems
 …..
 Green Information Systems
based on
 Data Warehousing
 Data Mining

21. Data Equity
 UK Report : Annual economic benefit of these big
data analytics can be above 40 billion £ for UK,
for 2017 from Government and Enterprise point of
view.
 US government has dedicated 200 million dollars
for research to handle big data
 $10 million data project at the University of
California, Berkeley, support for a geosciences
data effort called Earth Cube, and more.
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22. Some Scenarios …

23. Obama’s secret weapon in re-
election
 With a sluggish economy, unemployment
teetering at around the eight per cent mark, and
growing anti-Obama sentiment in some parts of
the country, a second term seemed an uphill task
for Obama and it was going to take an
extraordinary campaign to make it happen
 From the get-go David Axelrod, the brain behind
the Obama campaign, recognised the role that
data and information could play in the election.
The process had been initiated in 2008 but
databases were scattered and it wasn’t until the
2010 midterm elections that the Democratic
Party, despite heavy losses, was able to
streamline the data to accurately forecast results
in a meaningful way.

24. Obama’s secret weapon in re-
election
 Pakistani scientist Rayid Ghani
 Ghani’s job was to make sense of huge amounts of
information
“The core of the work I was doing was looking at a
large amount of data and making sense of it to help
other people make better decisions”
If the 2008 campaign was about charisma and hope,
the 2012 campaign was about science and data.
 How data helps you, is it makes you more efficient and it
helps you spend your money carefully and in the right
way

25. Collaborative Social IS
The data content published by individuals on web,
specifically in a collaborative environment like social
forum, blog, games etc.
Data can be analysed/created by thousands of users
(crowd-sourcing initiatives).
• Right after the earthquake in Haiti in 2009, the
company holding the license to use geo-imagery
opened up the map data of Haiti to general public.
The web users all over the world examined the
imagery and within a couple of days populated the
map with information like refugee camps, hospitals,
damaged buildings. The relief workers in the area
used the map to organise the relief work
http://observedchange.com/demos/linked-haiti/
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26. 26

27. Other scenarios …
• Online business transactional system :
Millions of transactions over internet, e.g.
online game servers, business transaction
portals or other business to business oriented
services
• New York Stock Exchange produces about
one terabyte of data per day
• WallMart Data Warehouse Intelligence
• Querying these transactions in Column
databases!
• NoSQL Databases
• Graph Databases

28. Evaluation Criteria:
 Sessionals (2) 25, Assignments: 20, Quizzes: 5
 Terminal 50
Recommended Readings:
 B1. Introduction to Information Systems, 4th Edition,
International Student Version, by R. Kelly Rainer,
Casey G. Cegielski, April 2012, ©2012
 B2. Fundamentals of Database Systems by Ramez
ElMisri and Shamkant B. Navathe
 B3. Database Systems by Thomas Connolly and
Carolyn Begg
 B4. Web Data Mining by Bing Liu
 B5. Data Mining: Concepts and Techniques. Jiawei
Han, Micheline Kamber and Jian Pei. Third Ed. 2012.