Chapter 7
User Interface and Decision
Visualization Applications


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Key to successful use of MSS is the user
interface
The simpler the better
Many MSS applications have hard to use
user interfaces
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
7.1 Opening Vignette:
Geographic Information
System at the Dallas Area
Rapid Transit (DART)
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Buses
Vans
Light Rail System
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
By the Mid-1980s Could Not
–
–
–
–

Respond to customer requests
Make changes rapidly
Plan properly
Manage security
DART had
– 5,000 daily customer inquiries
– Over 200 bus routes
– Over 13,500 bus stops
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Geographic Information
System (GIS) Solution


View and analyze data on digitized maps
Now, DART Employees can
– Rapidly respond to customer inquiries (response
time cut by 1/3)
– Provide more accurate information
– Plan services
– Perform environmental impact studies
– Cut bus schedule production costs
– Track bus locations via GPS
– Improve bus security
– Monitor subcontractors
– Analyze productivity and utilization
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ


Analysis time cut from days to
less than an hour
Preparation of special maps:
time cut from up to a week to
five minutes (cost cut from
$15,000 to pennies)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
7.2 User Interfaces: An
Overview


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Most computer users have limited computer
experience
Inexperienced users do not want to learn the
computer-oriented details
Most systems were developed for experienced
users
Need better user interfaces
The design of an appropriate MSS user
interface could be the most important
determinant of success of the MSS
implementation
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
User Interface Design is
Influenced by User
Characteristics
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MSS execution time
Learning time of the MSS
Ease of recall
System's versatility
Errors made by end users
Quality of help
Adaptability to changes in the users' computer
competency
Concentration level required by end users
Fatigue from using the system
Command uniformity
Fun the user derives
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
User Interface


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Human-computer interaction
Surface
Physical aspects (see Figure 7.1)
– Input Devices
– Display (Output) Devices
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
The Cyclical Process
(Figure 7.1)
1. Knowledge
2. Dialog
3. Action Language
4. Computer
5. Presentation Language
6. User's Reaction
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Important Issues in Building a
User Interface
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Choice of input and output devices
Screen design
Human-machine interaction sequence
Use of colors and shading
Information density
Use of icons and symbols (especially for objectoriented)
Information display format
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
The User Interface
Management System (UIMS)



Accommodates the various information
representations
Accommodates the action languages
Provides an interface between the system
user and the rest of the system
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
7.3 Interface Modes (Styles)



Interface (or interactive) Mode: the
combination of presentation and action
languages
Determines how information is entered and
displayed
Determines the ease and simplicity of
learning and using the system
–
–
–
–
–
–
Menu interaction
Command language
Questions and answers
Form interaction
Natural language processing
Graphical user interface (object manipulation)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Menu Interaction

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Includes Pull-down Menus (in GUI)
Command Language
Questions and Answers
Computer asks, user answers
Form Interaction
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Natural Language

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Mainly with keyboard
Some with voice input and output
Major limitation
Inability of the computer to understand
natural language
AI advances are improving it
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Graphical User Interface
(GUI)

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Icons (or symbols) are directly
manipulated by the user
Most common PC GUI OS: Windows 95
Usability of four styles along four
dimensions (Table 7.1)
Hybrid Modes
– NLP + Hypermedia
– Command + Menu
– GUI + Menu
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E 7 .1 C om p a rison of Interfa ce M od es.
Fill in the
D im ensio ns
Q uestio ns
M enu
B lanks
C o m m and
Interactio n
(Fo rm s)
L ang uag es
and A nsw ers
GUI
S peed
S lo w at tim es
M o derate
Fast
C o uld be slo w
S lo w at tim es
A ccuracy
E rro r free
M o derate
M any erro rs
E rro r free
M o derate
T raining tim e
S ho rt
M o derate
L o ng
S ho rt
S ho rt
U sers'
V ery hig h
L ow
Prefer, if
H ig h
H ig h
M o derate-
M o derate
preference
Po w er
trained (o nly )
L ow
L ow
V ery hig h
hig h
Flexibility
C o ntro l
L im ited
T he sy stem
V ery lim ited
T he sy stem
V ery hig h
T he user
M o derate-
H ig h (if o pen
hig h
ended)
T he sy stem
T he sy stem
and the user
S o u rce: B a sed on M a jchrza k et a l. [1 9 8 7 ].
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
User Interface Importance


Interface cost can be 60 to 70 % of
the total DSS cost
Ideally, interface adaptable to
different users’ needs and
communicate consistent commands
internally
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
7.4 Graphics

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Graphics Software
Purpose: to present visual images of
information
Integrated software packages: create
graphic output directly from
databases or spreadsheets
– Stand-alone graphics packages
– Integrated packages - often include
– 3-D graphic presentations and virtual
reality
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
The Role of Computer
Graphics


Help managers "visualize" data,
relationships, and summaries (Figure
7.2)
Graphics forms (Table 7.2)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E 7.2 T yp es of C om p u ter G rap h ics
 T ex t play s a critical role in g raphics--listing points that the speaker is discussing , show ing
subject titles, identify ing com ponents and values of a chart, and so on.
 T im e-series ch a rts show the value of one or m ore variables over tim e.
 B a r a n d p ie ch a rts can be used to show total values (by the size of the bar or pie), as w ell as
com ponent values, such as breakdow ns of, say , " source of m oney received."
 S ca tter d ia g ra m s show the relationship betw een tw o variables, such as the num ber of air
travelers w ho fly on M onday s, T uesday s, and so on.
 M a p s can be tw o- or three-dim ensional. T w o-dim ensional m aps are useful for show ing
spatial relationships, for ex am ple, the locations of custom ers and the locations of a
com pany 's custom er service facilities. T hree-dim ensional m aps show surface contours w ith
a three-dim ensional effect (see the G IS in the opening vig nette).
 L a yo u ts of room s, building s, or shopping centers convey m uch inform ation in relatively
sim ple diag ram s.
 H iera rch y ch a rts, such as org anizational charts, are w idely used.
 S eq u en ce ch a rts, such as flow charts, show the necessary sequence of events, and w hich
activities can be done in parallel.
 M o tio n g ra p h ics, such as m otion pictures and television, clearly w ill continue to perform
vital functions.
 D esk to p p u b lish in g sy stem s that have ex tensive g raphic capabilities (e.g ., transferring a
picture into the com puter, lay ing it in a desirable position, and then printing it) are g aining
in popularity .
S o u rce: B ased o n R . H . Sp rague, Jr. and B . M cN urlin, In fo rm a tio n S ystem s M a n a g em en t in P ra ctice , 1 st ed ., 3 rd ed . E nglew o o d
C liffs, N J: P rentice-H all.
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
7.5 Multimedia and
Hypermedia


Multimedia
Pool of human-machine
communication media (Table 7.4)
–
–
–
–
–
–
Sound
Text
Graphics
Animation
Video
Voice
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E 7 .4 H u m a n -M a c h in e
C o m p u te r
C R T a n d te r m in a ls
C D -R O M
C o m p u te r in te r a c tiv e
v id e o d isk
D ig ita l v id e o in te r a c tiv e
C o m p a c t d isc in te r a c tiv e
C o m p u te r sim u la tio n
T e le te x t/v id e o te x t
In te llig e n t tu to r in g sy ste m
H y p e r te x t
Im a g e d ig itiz in g
Scanners
S c r e e n p r o je c tio n
O b je c t-o r ie n te d
p r o g r a m m in g
C o m m u n ic a tio n M e d ia
P r o je c te d still v isu a ls
S lid e
O verhead
M o tio n im a g e
V id e o d isc (c a sse tte )
M o tio n p ic tu r e s
B r o a d c a st te le v isio n
T e le c o n fe r e n c e /
v id e o c o n fe r e n c e
A n im a tio n
V ir tu a l r e a lity
T ext
G r a p h ic m a te r ia ls
P ic tu r e s
P r in te d jo b a id s
V isu a l d isp la y
A u d io
T a p e /c a sse tte /r e c o r d
T e le c o n fe r e n c e /
a u d io c o n fe r e n c e
S o u n d d ig itiz in g
M ic r o p h o n e
C o m p a c t d isc
M u sic
S o u rce: P . C ha o et a l., "U sing E x p ert S ystem s A p p roa ches to S olve M edia S election P rob lem :
M a trix F orm a t," P ro ceed in g s o f th e A sso cia tio n o f C o m p u ter In terfa ce S ystem s , L os A la m itos,
C A : IE E E C om p u ter S ociety P ress, N ovem b er 1 9 9 0 .  IE E E .
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Hypermedia


Virtual reality via Virtual Reality
Modeling Language (VRML) for Web
delivery
Hypermedia: multimedia documents
linked by association
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Multiple Layers of
Information

Menu-based natural language interface
Object-oriented database
A relational query interface
A hypermedia abstract machine
Media editors
Change management virtual memory

Especially effective in searching





Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Hypermedia
Characterizations



Explicitly linked different
information structures
Multimedia
Linking information by association
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Classes of Hypermedia

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Presentation for knowledge and data
navigation (Figure 7.3)
Active participation in research to help
record, organize, and integrate
information and processes (Figure 7.4)
Hypertext
– Nonlinear information access
– Follow a thread (drill)
– Internet browsing
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Multimedia, Hypermedia, the
Internet/Web and the Objectoriented Approach
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GUI Icons
Visual Programming
Web Hooks
Electronic Document Management
(EDM)
– Problems with paper documents
– EDM systems
– Multimedia and Web access
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
7.6 Virtual Reality

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3-D Presentations
3-D user interfaces
– Manufacturing
– Marketing

Virtual reality (VR)
–
–
–
–

Decision making
Advertising
Data visualization
Visual, spatial, and aural immersion
VRML: Virtual Reality Markup Language
for the Web
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E 7 .5 E xa m p les of V irtua l R ea lity A p p lica tions.
Ind ustry
A utom otiv e/H ea v y
E q uip m ent/M ilita ry
A p p lica tion






D esig n testing
V irtua l p rototy p ing
E ng ineering a na ly sis
E rg onom ic a na ly sis
V irtua l sim ula tion of a ssem b ly ,
p rod uction, a nd m a intena nce
T ra ining
M ed icine



T ra ining surg eons (w ith sim ula tor)
S urg ery
P hy sica l thera p y
R esea rch/E d uca tion





V irtua l p hy sics la b
H urrica ne stud ies
G a la xy config ura tions
R ep resenta tion of com p lex
m a them a tics
V irtua l M useum s




3 -D R a ce ca r g a m es (on P C )
A ir com b a t sim ula tion (on P C )
V irtua l R ea lity a rca d es
V irtua l R ea lity p a rks
A m usem ent
S o u rce: C om piled from J . A da m , “ V irtua l R ea lity is R ea l,” IE E E S p ectru m ,
V R S pecia l R eport 1 9 9 3 .
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
7.7 Geographic Information
Systems (GIS)
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

Computer-based system for
capturing, storing, checking,
integrating, manipulating, and
displaying data using digitized
maps
GIS Software
GIS Data In-house or purchased
GIS and Decision Making
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
GIS Applications

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Political campaign support
Consumer marketing and sales support
Sales and territory analysis
Site selection
Fleet management
Route planning
Disaster planning
Regulatory compliance
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ

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GIS and the Internet/Intranet
GIS Servers
Client GIS data
Emerging GIS Applications
With GPS
Intelligent GIS
Virtual reality
More Web hooks
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
D SS In F o cus 7 .6 : H o w C o m pa nies a re U sing G IS

S u p er V alu , th e cou n try’s N o. 1 su p erm arket w h olesaler, u ses a G IS to h elp
locate stores. G IS frees u p an alysts from m u n d an e m an u al m ap p in g tasks to
actu ally an alyze th e p rob lem at h an d .

W estern A u to, a S ears R oeb u ck su b sid iary, in tegrates com p an y d ata w ith G IS to
create a d etailed d em ograp h ic p rofile of a store’s n eigh b orh ood so it can set u p
th e righ t p rod u ct m ix for its cu stom er b ase. T h is estab lish es cu stom er loyalty
m ore q u ickly. T h e resu lt is th at on average, a store b reaks even on its op eratin g
exp en ses in six m on th s in stead of p reviou s to th e G IS , w h en it took an average of
18 m on th s.

T ravelers In su ran ce u ses a d esktop G IS to p erform statistical an alysis for sitep lan n in g, d em ograp h ics of p op u lation s served , d atab ase visu alization for
em p loyers an d sales su p p ort.

S ears, R oeb u ck & C o. h as d ep loyed a G IS to rep lace a com p u terized , rou tep lan n in g system th at req u ired tru ck d isp atch ers at each d istrib u tion cen ter to
h ave exten sive kn ow led ge of road s an d traffic in th eir region s.

H ealth m ain ten an ce organ ization s an d m ed ical clin ics u sin g m ap p in g to
d eterm in e op tim u m clin ic location s.
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ

S ev era l h ea lth ca re p ro v id ers a re u sin g G IS to b etter u n d ersta n d th e m a rk et th ey
serv e. T w o su ch w a y s a re to ch a rt reg io n a l a n o m a lies in th eir serv ice a rea , su ch
a s h ig h er th a n n o rm a l ca n cer ra tes, a n d to a n a ly ze p o ten tia l p a rtn ersh ip s w ith
o th er p ro v id ers to exp a n d th e serv ice a rea o f th e h ea lth ca re sy stem , a s w ell a s
a ssess th e n eed a n d p o ten tia l m a rk et fo r exp en siv e ca p ita l in v estm en ts, su ch a s
m a g n etic reso n a n ce im a g in g sca n n ers.

W o o d P erso n n el S erv ices In c., a N a sh v ille-b a sed em p lo y m en t a g en cy , b o o sted
p la cem en ts b y 2 5 p ercen t in o n e y ea r b y m a p p in g n eig h b o rh o o d s w h ere
tem p o ra ry w o rk ers liv ed , th en lo ca tin g m a rk etin g a n d recru itin g sites th ere.

W ilk en in g & C o ., a P a rk R id g e, IL , C o n su ltin g firm , u ses W essex’s F irst S t. G IS
to d esig n o p tim ized sa les territo ries a n d ro u tes fo r clien ts, sla sh in g th eir tra v el
co sts b y a n a v era g e o f 1 5 p ercen t.

C ellu la rO N E , a S a n F ra n cisco cellu la r n etw o rk p ro v id er, u ses M a p In fo fro m
M a p In fo C o rp . to m a p its en tire cellu la r n etw o rk , h elp in g it to id en tify clu sters
o f ca ll d isco n n ects a n d d isp a tch field -serv ice tech n icia n s fo r tro u b lesh o o tin g .

A co rd ia S en io r B en efits, a su b sid ia ry o f A co rd ia In c., u ses In fo m a rk fro m
E q u ifa x M a rk etin g D ecisio n S y stem s In c. a n d A rcV iew fro m E n v iro n m en ta l
S y stem s R esea rch In stitu te In c. to m a p o u t lo ca tio n s fo r n ew in su ra n ce p ro d u cts
a n d to d ecid e w h en n o t to g et in to a n a rea .

N E S A , a D a n ish u tility , h a s im p lem en ted a co m p reh en siv e in fo rm a tio n
m a n a g em en t sy stem b a sed o n E S R I’s A rc/In fo G IS sy stem , to en co u ra g e a n d
en h a n ce d a ta a ccessib ility . G IS p ro v id ed a g rea ter p o ten tia l fo r im p ro v in g th e
d a ily ro u tin e a n d fo r crea tin g p o ssib ilities fo r n ew a p p lica tio n s, v ersu s C A D o r
sta n d a rd d a ta b a se so ftw a re.

In n o rth ern C a lifo rn ia , P a cific B ell is u sin g G IS to h elp p lo t a b ro a d b a n d
n etw o rk o f fib er-o p tic ca b le.
(S o u rce: C o n d en sed fro m B id g o li [1 9 9 5 ], B o rch [1 9 9 6 ], H a m ilto n [1 9 9 6 ], S w en so n
[1 9 9 6 a ], W estm o se [1 9 9 6 ], a n d p u b lic d o m a in d o cu m en ts.)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
7.8 Natural Language
Processing (NLP)







Applied artificial intelligence technology
Communicating with a computer in
English (or other human) language
Advantages:
Disadvantages:
Natural language understanding
Natural language generation
Versus speech recognition
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
7.9 Natural Language
Processing: Methods

Natural language into the computer
– Example: English into Netscape
Navigator Commands

Natural language into another
natural language - English to French
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Major NLP Techniques



Key word search (pattern matching)
Language processing (syntactic and
semantic analysis)
Neural computing (relatively new)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Key Word Analysis (Pattern
Matching)

Pattern matching process:
– Search for selected key words or phrases


Provide canned response
Flow diagram (Figure 7.5)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
O utput S uitable
or
C hange Input
S tart
A ccept and
S tore Input
Input
M essage
NO
S can input,
S earch for K ey
W ord
K ey W ord(s)
F ound?
YES
M ore
K ey
W ords?
YES
END
D evelop and O utput
A ppropriate R esponse
NO
F IG U R E 7.5 T he P rocess of K ey W ord A nalysis.
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Key Activities



Parsing to determine word
boundaries
Pattern matching to compare to
prestored words and phrases
OK for few key words
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Language Processing
(Syntactic, Semantic, and
Pragmatic Analysis)

Problems
– Many words with multiple meanings
– Many structures including those words
in sentences
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Definitions




Syntax analysis looks at the way a sentence
is constructed; the arrangement of its
components and their relationships
Syntactic processes analyze and designate
sentences to clarify the grammatical
relationships between words in sentences
Semantics assigns meaning to the syntactic
constituents
Pragmatic analysis relates individual
sentences to each another and to the
surrounding context
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
The Procedures


How Language Processing Works
Simplified block diagram (Figure
7.6)
– Parser
Lexicon
– Understander
– Knowledge base
– Generator
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Parser Syntactically
Analyzes the Input
Sentence







Each word is identified and its part of speech
clarified
The Parser maps the words into a structure called
a parse tree
The Parse tree shows the meanings of all of the
words and how they are assembled
The Lexicon is a dictionary
The Parser is a pattern matcher and builds the
parse tree
The Understander works with the knowledge base
to determine sentence meaning
The Knowledge
base is a repository of knowledge
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ



The understander uses the parse tree to
reference the knowledge base
The understander can draw inferences
from the input statement
The generator can initiate additional
action
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
7.10 Applications of Natural
Language Processing and
Software
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





Database interfaces
Abstracting and summarizing text
Grammar analysis
Natural language translation
Computer language to computer
language translation
Letter composition
Speech understanding
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
7.11 Speech (Voice) Recognition
and Understanding


The computer recognizes the normal
human voice
Advantages of Speech Recognition
–
–
–
–
–

Ease of Access
Speed
Manual Freedom
Remote Access
Accuracy
Good Morning Dave (2001)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Classifying Speech
Recognizers






Word Recognizers
Continuous Speech Recognizers
Speaker Dependent
Speaker Independent
Voice Synthesis
Computers speak
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E 7 .6 V o ic e T e c h n o lo g y A p p lic a tio n s S a m p le r
C om p a ny
A p p lic a tions
S ca n d in a v ia n A irlin es, o th er a irlin es
A n sw erin g in q u iries a b o u t reserv a tio n s,
sch ed u les, lo st b a g g a g e, etc.
In fo rm in g cred it ca rd h o ld ers a b o u t
b a la n ces a n d cred its, p ro v id in g b a n k
a cco u n t b a la n ces a n d o th er in fo rm a tio n to
cu sto m ers
V erify in g co v era g e in fo rm a tio n
R eq u estin g p ick u p s
G iv in g in fo rm a tio n a b o u t serv ices, receiv in g
o rd ers
E n a b lin g sto res to o rd er su p p lies, p ro v id in g
p rice in fo rm a tio n
A llo w in g in sp ecto rs to rep o rt resu lts o f
q u a lity a ssu ra n ce tests
A llo w in g receiv ers o f sh ip m en ts to rep o rt
w eig h ts a n d in v en to ry lev els o f v a rio u s
m ea ts a n d ch eeses
C o n d u ctin g m a rk et resea rch a n d
telem a rk etin g
N o tify in g p eo p le o f em erg en cies d etected b y
sen so rs
N o tify in g p a ren ts w h en stu d en ts a re a b sen t
a n d a b o u t ca n cella tio n o f cla sses
C a llin g p a tien ts to rem in d th em o f
a p p o in tm en ts, su m m a rizin g a n d rep o rtin g
resu lts
A ctiv a tin g ra d io s, h ea ters, a n d so o n , b y
v o ice
L o g g in g in a n d o u t b y v o ice to p a y ro ll
d ep a rtm en t
P ro m p tin g d o cto rs in th e em erg en cy ro o m to
co n d u ct a ll n ecessa ry tests, rep o rtin g o f
resu lts b y d o cto rs
S en d in g a n d receiv in g p a tien t d a ta b y v o ice,
sea rch in g fo r d o cto rs, p rep a rin g sch ed u les
a n d m ed ica l reco rd s
E n ter d a ta o n a rriv in g lo g s
C itib a n k , m a n y o th er b a n k s
D elta D en ta l P la n (C A )
F ed era l E xp ress
Illin o is B ell, o th er telep h o n e co m p a n ies
D o m in o ’s P izza
G en era l E lectric, R o ck w ell In tern a tio n a l,
A u stin R o v er, W estp o in t P ep p erell, K o d a k
C a ra D o n n a P ro v isio n s
W eid n er In su ra n ce, A T & T
U .S . D ep a rtm en t o f E n erg y , Id a h o N a tio n a l
E n g in eerin g L a b o ra to ry , H o n ey w ell
N ew J ersey D ep a rtm en t o f E d u ca tio n
K a iser-P erm a n en te H ea lth F o u n d a tio n
(H M O )
A u to m a n u fa ctu rers
T exo m a M ed ica l C en ter
S t. E liza b eth ’s H o sp ita l
H o sp ita l C o rp o ra tio n o f A m erica
R o b b in s L u m b er
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
7.12 Research on User
Interfaces in MSS

4 Independent Variables
1. Human user
Demographics (age, education, experience)
Psychological (cognitive style, intelligence, risk
attitude).
2. Decision environment
– Decision structure
– Organizational level
– Others (stability, time pressure, uncertainty).
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
3. Task
Decision support (e.g., complexity level)
Inquiry/information retrieval
Data entry
Word processing
Computer-aided instruction.
4. Interface characteristics
Input/output media
Dialogue type
Presentation format (tabular, graphical, colors,
animation)
Language characteristics (help facility, default
options, other options).
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Dependent Variable:
Human/Computer
Effectiveness
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
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
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Usefulness
Perceived ease of use
Performance (time, errors, profit)
User attributes (satisfaction,
confidence)
Use of system option (high, low).
– Hwang and Wu [1990]
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Results of Some
Experiments
1. Colors improve performance
2. Graphic versus tabular: inconclusive

Research on Graphics and Modeling
Metagraphs to represent system structure
graphically for analysis

New Interfaces

– Fish-eye View for GUI - Xerox Parc Research
Center
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E 7 .3 C o m p a riso n B etw een th e C u rren t U ser In terfa ce
G en era tio n o f C o m m a n d -b a sed In terfa ces a n d th e P o ten tia l N ex t
G en era tio n o f In terfa ces A cro ss 1 2 D im en sio n s.
C u rren t In terfa ce G en era tio n
U ser fo cu s
C o m p u ter's
ro le
C o n tro llin g co m p u ter
O b ey in g o rd ers litera lly
In terfa ce
co n tro l
B y u ser (i.e., in terfa ce is n o t
ex p licitly m a d e v isib le)
S yn ta x
O b ject-A ctio n co m p o sites
O b ject
visib ility
E ssen tia l fo r th e u se o f d irect
m a n ip u la tio n
In tera ctio n
strea m
B a n d w id th
S in g le d ev ice a t a tim e
T ra ck in g
feed b a ck
T u rn -ta k in g
L o w (key b o a rd ) to fa irly lo w
(m o u se)
P o ssib le o n lex ica l lev el
Y es; u ser a n d co m p u ter w a it
fo r ea ch o th er
N ex t-G en era tio n
In terfa ces
C o n tro llin g ta sk d o m a in
In terp retin g u ser a ctio n s
a n d d o in g w h a t it d eem s
a p p ro p ria te
B y co m p u ter (sin ce u ser
d o es w o rry a b o u t th e
in terfa ce a s su ch )
N o n e (n o co m p o sites
sin ce sin g le u ser
co n stitu tes a n in tera ctio n
u n it)
S o m e o b jects m a y b e
im p licit a n d h id d en
P a ra llel strea m s fro m
m u ltip le d ev ices
H ig h to v ery h ig h
(v irtu a l rea lities)
N eed s d eep kn o w led g e o f
o b ject sem a n tics
N o ; u ser a n d co m p u ter
b o th keep g o in g
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Interface
locus
W orkstation screen, m ouse,
and keyboard
User
Im perative and poorly
program m ing structured m acro languages
Software
packaging
M onolithic applications
Em bedded in user's
environm ent, including
entire room and
building
Program m ing-bydem onstration and
nonim perative,
graphical languages
Plug-and-play m odules
Source: J. Nielson, "Noncommand User Interfaces, Communications of the ACM , April 1993, p.
86.
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Summary








Users want computer systems that are easy
to use
The user interface represents the system to
most users
The user interface must be relatively
friendly
Graphics are crucial
GIS
Virtual reality
Natural language processing and speech
recognition
Decision Support Systems
Intelligent Systems,
Efraim Turban andcontinues
Jay E. Aronson
Research
onanduser
interfaces
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Internet Exercise
10. Contact IBM (http://www.ibm.com)
to find information about their Voice
Type Dictation, Merlin and other
voice technology products.
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Group Exercise
Each group member will interview five computer
users at school, work or home. For each user,
identify the three interface modes preferred by
the user, ranked in descending order. Also, the
interviewer should discern the reasons why
people prefer a particular interface mode.
Then, the group will consolidate their findings
and prepare a report to guide a novice
computer user to the interface(s) with which he
should become familiar.
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Questions for the
Opening Vignette
1. Why is a GIS considered a graphical
user interface?
2. What are the advantages of GIS from
a user interface point of view?
3. Which of the capabilities listed in the
vignette support actual decision
making?
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Exercises
1. What is a natural language? Name two. What
distinguishes a natural language from a
computer language? Is Esperanto a natural
language? Why or why not?
2. Obtain an NLP/DBMS software (e.g., Q&A). Try
to use it on the database of Chapter 4, Exercise
5. Compare the use of a regular DBMS to the
one supported by NLP.
3. Explain why icons in the Windows environment
might be easier to use than typed commands.
Demonstrate the two to verify your opinion.
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
4. Why is it “easier” for a natural language to be
translated into another by a human versus by
a computer?
5. In the early days of language translation, the
expression “The spirit is willing, but the flesh
is weak” was translated to Russian and then
back to English. The new English rendering
was “The vodka is good, but the meat is
rotten.” What happened? Why?
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Questions for
Case Application 7.1
1. Identify the voice recognition and voice synthesis
portions of the system.
2. Identify all the tasks, which do not involve voice, carried
out by a computer.
3. What paperwork can be eliminated by such a system?
4. What are the benefits to Nabisco?
5. What are the benefits to the employees?
6. What alternative communication technologies described
in this chapter can be used instead of the system
described here? Would you recommend any of these;
why or why not?
7. Are there any disadvantages to the use of the technology?
Explain.
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
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Chapter 7 User Interface and Decision Visualization