SlideShare una empresa de Scribd logo
1 de 26
Descargar para leer sin conexión
Multidimensional Perceptual Map for
Project Prioritization and Selection
Jack Zheng
Vijay Vaishnavi
2014 Update
Originally Presented at AMCIS 2009
Citation:
• Zheng, Guangzhi and Vaishnavi, Vijay (2011) "A Multidimensional Perceptual Map Approach to Project
Prioritization and Selection," AIS Transactions on Human-Computer Interaction (3) 2, pp. 82-103,
https://www.researchgate.net/publication/264935893_A_Multidimensional_Perceptual_Map_Approach_to_Project_Prioriti
zation_and_Selection
http://www.slideshare.net/jgzheng/multidimensional-perceptual-map
Multidimensional Perceptual Map:
First Impression
2
Introduction
 Projects are commonly prioritized using a scoring approach
 evaluated according to predefined categories, which are then
aggregated into one or two priority numbers.
 Decision is based on the understanding of multiple aspects
(dimensions) of projects, such as project size (in terms of budget,
time, or people), risk level, expected return, business goal,
strategic impact, etc.
 Aggregated scores may only offer a limited view of project
importance. This often leads decision makers to ignore the
possible differences masked by the aggregation.
 This research presents a visual exploration approach based
on multidimensional perceptual maps which is generated by
self organizing maps.
 It incorporates human intuition in the process and maintains
the multidimensionality of project data as a decision basis for
project prioritization and selection.
3
Common Prioritization Methods
 Follow an indexing or scoring approach which
evaluates projects in a set of predefined categories
with an option of providing simple quadrant diagrams.
 The project priority is commonly represented by one
aggregated number (score) based on a weighted
summation of scores in each criteria.
 Some other methods use two numerical indicators
instead of one
 the additional indicator adds one more dimension of
information and enrich the meaning of projects.
 projects are readily plotted on a two-dimensional diagram
based on two indicators; in doing so, users can easily see
project distributions and overall portfolio composition.
4
Problems when dealing with
multidimensional projects data
 The final decision relies on simple calculated numbers.
 Multiple attributes may be used as inputs and contribute to the calculation
process, but at the end, these attributes are transformed into one or two
indicators for interpretation simplicity. Such simplicity does not always
satisfy business need.
 These calculated final scores may only offer a limited view of the project
importance. An aggregated score tends to homogenize many projects,
hiding useful and relevant information that may effectively distinguish
them (Wang et al. 2003). That often leads decision makers to ignore the
possible differences that get masked by the aggregation, and may result
in decisions that are not well justified.
 Visualization is a good mechanism to comprehend portfolio
composition intuitively. Challenges:
 many visualization diagrams are more confirmatory (for reporting
purposes) than exploratory, where they are mere static reflections of
results after the decision making process has been completed; they are
not well integrated into the decision making process itself.
 focused on techniques of generating the visualization, less focused on
the use of visualization
5
2D Perceptual Map
 Traditional perceptual maps are created using scatter charts or quadrant
diagrams, which are based on two dimensions (X and Y axes). Then data
items are plotted on the plane based on their values for the two attributes.
 These perceptual maps are commonly use for marketing segmentation or
project portfolio management.
6
Cooper, R., Edgett, S., and Klwinschmidt,
E. "Portfolio Management for New
Product Development: Results of an
Industry Practices Study," R&D
Management (31:4) 2001, pp 361-380.
Quadrant or matrix diagrams are
fundamentally constructed based on
only two dimensions. Trying to fit
high dimensional information into
these low dimensional models often
leaves out the richness of project
information, and leads to a narrower
understanding of project distribution.
System Design Overview
 A system that provides assistance in viewing, understanding and analyzing
projects and project portfolios directly based on multiple dimensions of
project data in the complete decision process.
 An intuitive visual exploration approach based on multidimensional
perceptual maps (MdPM)
 addresses the weaknesses of traditional scoring/ranking approaches and
visualization approaches, while keeping their simplicity and interpretability
 reveals the values of underlying attributes and makes them transparent in the
process of viewing, understanding, and analyzing projects and portfolios
 utilizes proper interactive visualizations to effectively and intuitively handle
multidimensional information for the information seeking process.
 involving human strength and supporting managerial intuition (Kuo 1998)
 Two visual elements
7
Profile Chart A profile chart is a visualization of an object based on the values of its
multiple attributes (dimensions) selected to represent the object; such a
visualization forms a representative shape pattern that can offer a unique
visual impression of the object.
Multidimensional
Perceptual Map
A high level overview visualization that shows the distribution and relative
positioning of all objects based on multiple attributes. It is the basis to map
analysis targets (products, projects, people, etc.).
8
Sample Data and Dimensions
Six dimensions
Scores for each
dimension (for one
sample project)
Profile Chart
 A profile chart is a visualization of an object based on the values of its multiple attributes
(dimensions) selected to represent the object; such a visualization forms a representative
shape pattern that can offer a unique visual impression of the object.
 Profile charts are able to present complete multidimensional “profiles,” avoiding the
reduction of multiple dimensions to a single “number,” and providing a strong and
memorable impression that is easy for users to remember and compare.
 Examples: candle-stick charts (used in stock trading technical analysis), Star and Petal,
Parallel Coordinates, radar (or star, spider) diagrams, or can be created using various
types of basic charts such as bar charts, line graphs, area graphs.
9
10
Profile Chart (Details on Demand)
Multidimensional Perceptual Map
 Two major features:
 The map is composed of smaller areas (cells), which are
characterized by a vector of values that represent multiple
attributes (dimensions).
 The vector of each area may be directly visualized on the map.
 The map can be divided into areas at different granularity levels to
meet various exploration needs.
 The positioning of data items in the map is determined by its
calculated measure (usually Euclidean distance) again each
cell.
 The multidimensional perceptual map does not rely on
the definition of any fixed axes.
 An unsupervised clustering technique called Self-
Organizing Map (SOM) (Kohonen 2001) is used to
generate such maps.
11
Multidimensional Perceptual Map
Various map layout
12
Each cell is represented
by a vector (rather than
coordinates) for its
properties
MD Perceptual Map Views
13
Cells
View
14
An unsupervised
clustering technique
called Self-
Organizing Map
(SOM) is used to
generate such maps.
Each cell is represented by a
vector which represents the
characteristics of a map cell.
Each vector is visualized using
the profile chart, which is
embedded directly in the cell. A
Cell Profile View displays profile
charts of all cells. In such a view,
the changing trend/pattern of all
cells can be directly observed on
the map.
Items
View
15
Clusters View + Items View
16
Another Four-Cluster Setting
17
18
All 3 Views
Prioritization Based on Clustering
19
Visual Exploration Process
20
1. Map Generation
 In this sub-process, the goal is to define and create a multidimensional
perceptual map for visual exploration. The essential step is to apply SOM
algorithm and further customize the results by visual exploration.
21
The first step is to prepare data
for map construction and
analysis. The most important
data are the attributes of
projects selected for a
particular task. Once the
attributes are determined, a
data table is prepared based
on all values of these attributes
for every project. Depending on
the value domain of each
attribute, weighting and scaling
may be applied.
In the second step, the map is defined using
the same attributes that describe the
projects. The most important map setting at
this step is the map size, defined as number
of cells (number of rows by number of
columns). The finest granularity of map
regions is determined by the map size. The
size of the map is not predetermined or
suggested by the tool, but rather to be
explored and tested out by users. Based on
a certain map size, the map is generated
using a computer algorithm such as SOM.
The verification step can be done initially or
may be conducted later in the “Visual
Exploration” process if any abnormality is
discovered.
2. Visual Exploration In this sub-process, the key steps are
user interactions with the visualization
(visual exploration actions). The process
and actions are designed in accordance
with the visual information seeking
mantra: Overview, Zoom & Filter, Details-
on-Demand (Shneiderman, 1996).
22
Users utilize three map views to quickly
focus on certain parts of the map and
narrow down candidate projects for final
comparison. They may focus on specific
regions and projects that are of interest
(“Zoom”). Users can switch between fine
grained Cells Profile View (“Drill Down”)
and any coarse grained Region Profile
View (“Drill Up”). A set of target project
groups can be defined and highlighted
on the map (“Filter”) (Figure 9).
Once candidate projects are selected,
users can go further to compare
individual projects head-to-head using
the profile chart comparison tool
(“Details-on-Demand”) (Figure 10).
The profile charts give clear
justifications for analyses and
decisions.
The overview is used to comprehend the whole map and customize it.
The system will transform SOM results into the Cells Profile View (Figure
6) and give users a general sense of the map (“Overview”).
A major action in this sub-process is setting different granularity levels of
map regions (Region Profile View). The system supports the definition of
multiple region sets (multiple ways and levels to divide a map). A user
defines these regions by directly observing, comparing, and contrasting
cell patterns. Each region’s profile is created by the system on the fly and
presented to users though profile charts (see Figure 8).
If the map is not satisfactory, then users have three options: 1) try a
different set of SOM parameters and re-create the map; 2) increase the
size of the map (to decrease the granularity level) such that the resulting
vector change trends are smoother; or 3) directly change selected cell
vectors in the SOM result (bypassing the algorithm).This kind of direct
human intervention is an example of applying intuition and sometimes is
very effective.
Once the map is deemed to be satisfactory, it can be saved and reused
later for analysis consistency. Last, selected projects can be plotted on
the map (Item Projection View).
Conceptual Model
23
In general, the designed
approach is a computer
system driven visual
information seeking
process
Understanding the Approach
An exploratory approach, rather than a
confirmatory one
It helps to quickly understand the big picture,
discover potential patterns, narrow down areas
of focus, and come up with hypotheses
intuitively.
The system is complementary to other
approaches and systems, not a
replacement
May need to use together with other kinds of
tools
24
Applicable Areas
Project portfolio management, project
prioritization
Marketing research
Product evaluation
Performance management
Portfolio management
25
More Infomration
 Zheng, Guangzhi and Vaishnavi, Vijay (2011) "A
Multidimensional Perceptual Map Approach to
Project Prioritization and Selection," AIS
Transactions on Human-Computer Interaction (3)
2, pp. 82-103
 https://www.researchgate.net/publication/264935893_
A_Multidimensional_Perceptual_Map_Approach_to_Pr
oject_Prioritization_and_Selection
 http://aisel.aisnet.org/thci/vol3/iss2/3/
 Zheng, Guangzhi, "A Multidimensional and Visual
Exploration Approach to Project Portfolio
Management." Dissertation, Georgia State
University, 2009.
 http://scholarworks.gsu.edu/cis_diss/34
26

Más contenido relacionado

La actualidad más candente

Measurement in Marketing Research
Measurement in Marketing ResearchMeasurement in Marketing Research
Measurement in Marketing Research
Shameem Ali
 
Personal characteristics and sales aptitude
Personal characteristics and sales aptitudePersonal characteristics and sales aptitude
Personal characteristics and sales aptitude
Rheginald Estonactoc
 
Sales cost and cost analysis
Sales cost and cost analysisSales cost and cost analysis
Sales cost and cost analysis
Rupam Chakraborty
 

La actualidad más candente (20)

Brand revitalization
Brand revitalizationBrand revitalization
Brand revitalization
 
A STUDY ON EFFECTIVE BRAND PROMOTIONAL STRATEGIES INFLUENCING CUSTOMERS
A STUDY ON EFFECTIVE BRAND PROMOTIONAL STRATEGIES INFLUENCING CUSTOMERSA STUDY ON EFFECTIVE BRAND PROMOTIONAL STRATEGIES INFLUENCING CUSTOMERS
A STUDY ON EFFECTIVE BRAND PROMOTIONAL STRATEGIES INFLUENCING CUSTOMERS
 
Measurement in Marketing Research
Measurement in Marketing ResearchMeasurement in Marketing Research
Measurement in Marketing Research
 
Brand tracking studies
Brand tracking studiesBrand tracking studies
Brand tracking studies
 
Philip kotler marketing
Philip kotler marketingPhilip kotler marketing
Philip kotler marketing
 
CURRENT & EMERGING TRENDS IN MARKETING
CURRENT & EMERGING TRENDS IN MARKETINGCURRENT & EMERGING TRENDS IN MARKETING
CURRENT & EMERGING TRENDS IN MARKETING
 
Managing Mass Communications, Five M’s of Marketing, Advertising
Managing Mass Communications, Five M’s of Marketing, AdvertisingManaging Mass Communications, Five M’s of Marketing, Advertising
Managing Mass Communications, Five M’s of Marketing, Advertising
 
Motivating the sales force
Motivating the sales forceMotivating the sales force
Motivating the sales force
 
MBA Marketing - Final project report on sales promotion in home automation , ...
MBA Marketing - Final project report on sales promotion in home automation , ...MBA Marketing - Final project report on sales promotion in home automation , ...
MBA Marketing - Final project report on sales promotion in home automation , ...
 
Elements of promotion mix
Elements of promotion mixElements of promotion mix
Elements of promotion mix
 
Personal characteristics and sales aptitude
Personal characteristics and sales aptitudePersonal characteristics and sales aptitude
Personal characteristics and sales aptitude
 
Unit 2 Sales Management
Unit 2 Sales ManagementUnit 2 Sales Management
Unit 2 Sales Management
 
Marketing Mix - Price
Marketing Mix - PriceMarketing Mix - Price
Marketing Mix - Price
 
Advertising budget
Advertising budgetAdvertising budget
Advertising budget
 
Nature, objectives and functions of Personal selling
Nature, objectives and functions of Personal sellingNature, objectives and functions of Personal selling
Nature, objectives and functions of Personal selling
 
Introduction to e marketing
Introduction to e  marketingIntroduction to e  marketing
Introduction to e marketing
 
Sales Organisation
Sales OrganisationSales Organisation
Sales Organisation
 
Sales cost and cost analysis
Sales cost and cost analysisSales cost and cost analysis
Sales cost and cost analysis
 
Effect of Advertisements
Effect of Advertisements Effect of Advertisements
Effect of Advertisements
 
Terms for smartPLS.pptx
Terms for smartPLS.pptxTerms for smartPLS.pptx
Terms for smartPLS.pptx
 

Destacado

Johnsson: applications of_self-organizing_maps
Johnsson: applications of_self-organizing_mapsJohnsson: applications of_self-organizing_maps
Johnsson: applications of_self-organizing_maps
ArchiLab 7
 
SDLC 101 Cartoon
SDLC 101 CartoonSDLC 101 Cartoon
SDLC 101 Cartoon
Jack Zheng
 
Learning Vector Quantization LVQ
Learning Vector Quantization LVQLearning Vector Quantization LVQ
Learning Vector Quantization LVQ
ESCOM
 

Destacado (20)

Marketing Research - Perceptual Map
Marketing Research - Perceptual MapMarketing Research - Perceptual Map
Marketing Research - Perceptual Map
 
Mobile Web Overview https://www.edocr.com/v/k52p5vj4/
Mobile Web Overview https://www.edocr.com/v/k52p5vj4/Mobile Web Overview https://www.edocr.com/v/k52p5vj4/
Mobile Web Overview https://www.edocr.com/v/k52p5vj4/
 
Project Prioritization - The Cost Of Strategy
Project Prioritization - The Cost Of StrategyProject Prioritization - The Cost Of Strategy
Project Prioritization - The Cost Of Strategy
 
Johnsson: applications of_self-organizing_maps
Johnsson: applications of_self-organizing_mapsJohnsson: applications of_self-organizing_maps
Johnsson: applications of_self-organizing_maps
 
Unit 1
Unit 1Unit 1
Unit 1
 
Web navigation systems for information seeking (updated in Feb 2015)
Web navigation systems for information seeking (updated in Feb 2015)Web navigation systems for information seeking (updated in Feb 2015)
Web navigation systems for information seeking (updated in Feb 2015)
 
SDLC 101 Cartoon
SDLC 101 CartoonSDLC 101 Cartoon
SDLC 101 Cartoon
 
NOSQL Overview, Neo4j Intro And Production Example (QCon London 2010)
NOSQL Overview, Neo4j Intro And Production Example (QCon London 2010)NOSQL Overview, Neo4j Intro And Production Example (QCon London 2010)
NOSQL Overview, Neo4j Intro And Production Example (QCon London 2010)
 
KSU IT4983 Capstone Projects Report 2017 Update
KSU IT4983 Capstone Projects Report 2017 UpdateKSU IT4983 Capstone Projects Report 2017 Update
KSU IT4983 Capstone Projects Report 2017 Update
 
Implementation Of Back-Propagation Neural Network For Isolated Bangla Speech ...
Implementation Of Back-Propagation Neural Network For Isolated Bangla Speech ...Implementation Of Back-Propagation Neural Network For Isolated Bangla Speech ...
Implementation Of Back-Propagation Neural Network For Isolated Bangla Speech ...
 
Web Landscape - updated in Jan 2016
Web Landscape - updated in Jan 2016Web Landscape - updated in Jan 2016
Web Landscape - updated in Jan 2016
 
Information system a system view
Information system a system viewInformation system a system view
Information system a system view
 
face detection
face detectionface detection
face detection
 
Goal setting
Goal settingGoal setting
Goal setting
 
Artificial Neural Networks Lect7: Neural networks based on competition
Artificial Neural Networks Lect7: Neural networks based on competitionArtificial Neural Networks Lect7: Neural networks based on competition
Artificial Neural Networks Lect7: Neural networks based on competition
 
ARRA Integrated Fuel Prioritization Project Overview
ARRA Integrated Fuel Prioritization Project OverviewARRA Integrated Fuel Prioritization Project Overview
ARRA Integrated Fuel Prioritization Project Overview
 
Learning Vector Quantization LVQ
Learning Vector Quantization LVQLearning Vector Quantization LVQ
Learning Vector Quantization LVQ
 
Vector quantization
Vector quantizationVector quantization
Vector quantization
 
Back propagation network
Back propagation networkBack propagation network
Back propagation network
 
Semantic Web Landscape 2009
Semantic Web Landscape 2009Semantic Web Landscape 2009
Semantic Web Landscape 2009
 

Similar a Multidimensional Perceptual Map for Project Prioritization and Selection - 2014 update

GRP 1 GIS Presentation.pptx
GRP 1 GIS Presentation.pptxGRP 1 GIS Presentation.pptx
GRP 1 GIS Presentation.pptx
GauravDas84
 

Similar a Multidimensional Perceptual Map for Project Prioritization and Selection - 2014 update (20)

GI2013 ppt decewicz_dresden2013.pdf
GI2013 ppt decewicz_dresden2013.pdfGI2013 ppt decewicz_dresden2013.pdf
GI2013 ppt decewicz_dresden2013.pdf
 
A Study on Data Visualization Techniques of Spatio Temporal Data
A Study on Data Visualization Techniques of Spatio Temporal DataA Study on Data Visualization Techniques of Spatio Temporal Data
A Study on Data Visualization Techniques of Spatio Temporal Data
 
SD-miner System to Retrieve Probabilistic Neighborhood Points in Spatial Dat...
SD-miner System to Retrieve Probabilistic Neighborhood Points  in Spatial Dat...SD-miner System to Retrieve Probabilistic Neighborhood Points  in Spatial Dat...
SD-miner System to Retrieve Probabilistic Neighborhood Points in Spatial Dat...
 
Mrp Intrim
Mrp IntrimMrp Intrim
Mrp Intrim
 
GRP 1 GIS Presentation.pptx
GRP 1 GIS Presentation.pptxGRP 1 GIS Presentation.pptx
GRP 1 GIS Presentation.pptx
 
3-1_geo Spatial analysis_spatial_modeling.pptx
3-1_geo Spatial analysis_spatial_modeling.pptx3-1_geo Spatial analysis_spatial_modeling.pptx
3-1_geo Spatial analysis_spatial_modeling.pptx
 
Glyph-Placement-Strategy
Glyph-Placement-StrategyGlyph-Placement-Strategy
Glyph-Placement-Strategy
 
0329.emccormi.ppt
0329.emccormi.ppt0329.emccormi.ppt
0329.emccormi.ppt
 
CNN MODEL FOR TRAFFIC SIGN RECOGNITION
CNN MODEL FOR TRAFFIC SIGN RECOGNITIONCNN MODEL FOR TRAFFIC SIGN RECOGNITION
CNN MODEL FOR TRAFFIC SIGN RECOGNITION
 
Data models in geographical information system(GIS)
Data models in geographical information system(GIS)Data models in geographical information system(GIS)
Data models in geographical information system(GIS)
 
A novel predicate for active region merging in automatic image segmentation
A novel predicate for active region merging in automatic image segmentationA novel predicate for active region merging in automatic image segmentation
A novel predicate for active region merging in automatic image segmentation
 
A novel predicate for active region merging in automatic image segmentation
A novel predicate for active region merging in automatic image segmentationA novel predicate for active region merging in automatic image segmentation
A novel predicate for active region merging in automatic image segmentation
 
A Review on data visualization tools used for Big Data
A Review on data visualization tools used for Big DataA Review on data visualization tools used for Big Data
A Review on data visualization tools used for Big Data
 
International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)
 
Principles of GIS unit 1
Principles of GIS unit 1Principles of GIS unit 1
Principles of GIS unit 1
 
A Comparative Survey of 3D GIS Models
A Comparative Survey of 3D GIS ModelsA Comparative Survey of 3D GIS Models
A Comparative Survey of 3D GIS Models
 
FAMILY OF 2-SIMPLEX COGNITIVE TOOLS AND THEIR APPLICATIONS FOR DECISION-MAKIN...
FAMILY OF 2-SIMPLEX COGNITIVE TOOLS AND THEIR APPLICATIONS FOR DECISION-MAKIN...FAMILY OF 2-SIMPLEX COGNITIVE TOOLS AND THEIR APPLICATIONS FOR DECISION-MAKIN...
FAMILY OF 2-SIMPLEX COGNITIVE TOOLS AND THEIR APPLICATIONS FOR DECISION-MAKIN...
 
FAMILY OF 2-SIMPLEX COGNITIVE TOOLS AND THEIR APPLICATIONS FOR DECISION-MAKIN...
FAMILY OF 2-SIMPLEX COGNITIVE TOOLS AND THEIR APPLICATIONS FOR DECISION-MAKIN...FAMILY OF 2-SIMPLEX COGNITIVE TOOLS AND THEIR APPLICATIONS FOR DECISION-MAKIN...
FAMILY OF 2-SIMPLEX COGNITIVE TOOLS AND THEIR APPLICATIONS FOR DECISION-MAKIN...
 
paper24_SRomalewski
paper24_SRomalewskipaper24_SRomalewski
paper24_SRomalewski
 
paper24_SRomalewski
paper24_SRomalewskipaper24_SRomalewski
paper24_SRomalewski
 

Último

IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
Enterprise Knowledge
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
Joaquim Jorge
 

Último (20)

08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
 
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdfGenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
Tech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdfTech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdf
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 

Multidimensional Perceptual Map for Project Prioritization and Selection - 2014 update

  • 1. Multidimensional Perceptual Map for Project Prioritization and Selection Jack Zheng Vijay Vaishnavi 2014 Update Originally Presented at AMCIS 2009 Citation: • Zheng, Guangzhi and Vaishnavi, Vijay (2011) "A Multidimensional Perceptual Map Approach to Project Prioritization and Selection," AIS Transactions on Human-Computer Interaction (3) 2, pp. 82-103, https://www.researchgate.net/publication/264935893_A_Multidimensional_Perceptual_Map_Approach_to_Project_Prioriti zation_and_Selection http://www.slideshare.net/jgzheng/multidimensional-perceptual-map
  • 3. Introduction  Projects are commonly prioritized using a scoring approach  evaluated according to predefined categories, which are then aggregated into one or two priority numbers.  Decision is based on the understanding of multiple aspects (dimensions) of projects, such as project size (in terms of budget, time, or people), risk level, expected return, business goal, strategic impact, etc.  Aggregated scores may only offer a limited view of project importance. This often leads decision makers to ignore the possible differences masked by the aggregation.  This research presents a visual exploration approach based on multidimensional perceptual maps which is generated by self organizing maps.  It incorporates human intuition in the process and maintains the multidimensionality of project data as a decision basis for project prioritization and selection. 3
  • 4. Common Prioritization Methods  Follow an indexing or scoring approach which evaluates projects in a set of predefined categories with an option of providing simple quadrant diagrams.  The project priority is commonly represented by one aggregated number (score) based on a weighted summation of scores in each criteria.  Some other methods use two numerical indicators instead of one  the additional indicator adds one more dimension of information and enrich the meaning of projects.  projects are readily plotted on a two-dimensional diagram based on two indicators; in doing so, users can easily see project distributions and overall portfolio composition. 4
  • 5. Problems when dealing with multidimensional projects data  The final decision relies on simple calculated numbers.  Multiple attributes may be used as inputs and contribute to the calculation process, but at the end, these attributes are transformed into one or two indicators for interpretation simplicity. Such simplicity does not always satisfy business need.  These calculated final scores may only offer a limited view of the project importance. An aggregated score tends to homogenize many projects, hiding useful and relevant information that may effectively distinguish them (Wang et al. 2003). That often leads decision makers to ignore the possible differences that get masked by the aggregation, and may result in decisions that are not well justified.  Visualization is a good mechanism to comprehend portfolio composition intuitively. Challenges:  many visualization diagrams are more confirmatory (for reporting purposes) than exploratory, where they are mere static reflections of results after the decision making process has been completed; they are not well integrated into the decision making process itself.  focused on techniques of generating the visualization, less focused on the use of visualization 5
  • 6. 2D Perceptual Map  Traditional perceptual maps are created using scatter charts or quadrant diagrams, which are based on two dimensions (X and Y axes). Then data items are plotted on the plane based on their values for the two attributes.  These perceptual maps are commonly use for marketing segmentation or project portfolio management. 6 Cooper, R., Edgett, S., and Klwinschmidt, E. "Portfolio Management for New Product Development: Results of an Industry Practices Study," R&D Management (31:4) 2001, pp 361-380. Quadrant or matrix diagrams are fundamentally constructed based on only two dimensions. Trying to fit high dimensional information into these low dimensional models often leaves out the richness of project information, and leads to a narrower understanding of project distribution.
  • 7. System Design Overview  A system that provides assistance in viewing, understanding and analyzing projects and project portfolios directly based on multiple dimensions of project data in the complete decision process.  An intuitive visual exploration approach based on multidimensional perceptual maps (MdPM)  addresses the weaknesses of traditional scoring/ranking approaches and visualization approaches, while keeping their simplicity and interpretability  reveals the values of underlying attributes and makes them transparent in the process of viewing, understanding, and analyzing projects and portfolios  utilizes proper interactive visualizations to effectively and intuitively handle multidimensional information for the information seeking process.  involving human strength and supporting managerial intuition (Kuo 1998)  Two visual elements 7 Profile Chart A profile chart is a visualization of an object based on the values of its multiple attributes (dimensions) selected to represent the object; such a visualization forms a representative shape pattern that can offer a unique visual impression of the object. Multidimensional Perceptual Map A high level overview visualization that shows the distribution and relative positioning of all objects based on multiple attributes. It is the basis to map analysis targets (products, projects, people, etc.).
  • 8. 8 Sample Data and Dimensions Six dimensions Scores for each dimension (for one sample project)
  • 9. Profile Chart  A profile chart is a visualization of an object based on the values of its multiple attributes (dimensions) selected to represent the object; such a visualization forms a representative shape pattern that can offer a unique visual impression of the object.  Profile charts are able to present complete multidimensional “profiles,” avoiding the reduction of multiple dimensions to a single “number,” and providing a strong and memorable impression that is easy for users to remember and compare.  Examples: candle-stick charts (used in stock trading technical analysis), Star and Petal, Parallel Coordinates, radar (or star, spider) diagrams, or can be created using various types of basic charts such as bar charts, line graphs, area graphs. 9
  • 11. Multidimensional Perceptual Map  Two major features:  The map is composed of smaller areas (cells), which are characterized by a vector of values that represent multiple attributes (dimensions).  The vector of each area may be directly visualized on the map.  The map can be divided into areas at different granularity levels to meet various exploration needs.  The positioning of data items in the map is determined by its calculated measure (usually Euclidean distance) again each cell.  The multidimensional perceptual map does not rely on the definition of any fixed axes.  An unsupervised clustering technique called Self- Organizing Map (SOM) (Kohonen 2001) is used to generate such maps. 11
  • 12. Multidimensional Perceptual Map Various map layout 12 Each cell is represented by a vector (rather than coordinates) for its properties
  • 13. MD Perceptual Map Views 13
  • 14. Cells View 14 An unsupervised clustering technique called Self- Organizing Map (SOM) is used to generate such maps. Each cell is represented by a vector which represents the characteristics of a map cell. Each vector is visualized using the profile chart, which is embedded directly in the cell. A Cell Profile View displays profile charts of all cells. In such a view, the changing trend/pattern of all cells can be directly observed on the map.
  • 16. Clusters View + Items View 16
  • 19. Prioritization Based on Clustering 19
  • 21. 1. Map Generation  In this sub-process, the goal is to define and create a multidimensional perceptual map for visual exploration. The essential step is to apply SOM algorithm and further customize the results by visual exploration. 21 The first step is to prepare data for map construction and analysis. The most important data are the attributes of projects selected for a particular task. Once the attributes are determined, a data table is prepared based on all values of these attributes for every project. Depending on the value domain of each attribute, weighting and scaling may be applied. In the second step, the map is defined using the same attributes that describe the projects. The most important map setting at this step is the map size, defined as number of cells (number of rows by number of columns). The finest granularity of map regions is determined by the map size. The size of the map is not predetermined or suggested by the tool, but rather to be explored and tested out by users. Based on a certain map size, the map is generated using a computer algorithm such as SOM. The verification step can be done initially or may be conducted later in the “Visual Exploration” process if any abnormality is discovered.
  • 22. 2. Visual Exploration In this sub-process, the key steps are user interactions with the visualization (visual exploration actions). The process and actions are designed in accordance with the visual information seeking mantra: Overview, Zoom & Filter, Details- on-Demand (Shneiderman, 1996). 22 Users utilize three map views to quickly focus on certain parts of the map and narrow down candidate projects for final comparison. They may focus on specific regions and projects that are of interest (“Zoom”). Users can switch between fine grained Cells Profile View (“Drill Down”) and any coarse grained Region Profile View (“Drill Up”). A set of target project groups can be defined and highlighted on the map (“Filter”) (Figure 9). Once candidate projects are selected, users can go further to compare individual projects head-to-head using the profile chart comparison tool (“Details-on-Demand”) (Figure 10). The profile charts give clear justifications for analyses and decisions. The overview is used to comprehend the whole map and customize it. The system will transform SOM results into the Cells Profile View (Figure 6) and give users a general sense of the map (“Overview”). A major action in this sub-process is setting different granularity levels of map regions (Region Profile View). The system supports the definition of multiple region sets (multiple ways and levels to divide a map). A user defines these regions by directly observing, comparing, and contrasting cell patterns. Each region’s profile is created by the system on the fly and presented to users though profile charts (see Figure 8). If the map is not satisfactory, then users have three options: 1) try a different set of SOM parameters and re-create the map; 2) increase the size of the map (to decrease the granularity level) such that the resulting vector change trends are smoother; or 3) directly change selected cell vectors in the SOM result (bypassing the algorithm).This kind of direct human intervention is an example of applying intuition and sometimes is very effective. Once the map is deemed to be satisfactory, it can be saved and reused later for analysis consistency. Last, selected projects can be plotted on the map (Item Projection View).
  • 23. Conceptual Model 23 In general, the designed approach is a computer system driven visual information seeking process
  • 24. Understanding the Approach An exploratory approach, rather than a confirmatory one It helps to quickly understand the big picture, discover potential patterns, narrow down areas of focus, and come up with hypotheses intuitively. The system is complementary to other approaches and systems, not a replacement May need to use together with other kinds of tools 24
  • 25. Applicable Areas Project portfolio management, project prioritization Marketing research Product evaluation Performance management Portfolio management 25
  • 26. More Infomration  Zheng, Guangzhi and Vaishnavi, Vijay (2011) "A Multidimensional Perceptual Map Approach to Project Prioritization and Selection," AIS Transactions on Human-Computer Interaction (3) 2, pp. 82-103  https://www.researchgate.net/publication/264935893_ A_Multidimensional_Perceptual_Map_Approach_to_Pr oject_Prioritization_and_Selection  http://aisel.aisnet.org/thci/vol3/iss2/3/  Zheng, Guangzhi, "A Multidimensional and Visual Exploration Approach to Project Portfolio Management." Dissertation, Georgia State University, 2009.  http://scholarworks.gsu.edu/cis_diss/34 26