Big data for situation awareness and decision making

Big Data for Situation Awareness and
Decision Making
Design issues and approaches
Prof. Dr. Paloma Diaz
Computer Science Department / Ins3tute of Financial Big Data
Universidad Carlos III de Madrid
@MPalomaD
pdp@inf.uc3m.es

Who am I?
¡ Full professor of CS and AI at Universidad Carlos II de
Madrid
@MpalomaD
pdp@inf.uc3m.es
¡UC3M
Young university in the South of Madrid
Committed to academic and research excellence and
internationalisation
¡Director of the Interactive Systems Research group
(DEILab)
www.dei.inf.uc3m.es
@MPalomaDBig data for situational awareness and decision making, 2018

dei.inf.uc3m.es
Useful, usable,
enjoyable and
affordable
technology

UNDERSTANDING
BEFORE DOING
CO-DESIGN WITH
END USERS

dei.inf.uc3m.es
Envisioning
technology FOR
and WITH end
users

INTERACTION
Design interaction
experiences that match the
nature of the task, the
users involved and the
contexts of use
VISUALIZATION
Define approaches for
conveying information in a
way that improves decision
making
COLLABORATION
Design and develop collaborative
environments, trying to
understand how the adoption and
the use of technology influence
group behaviors and efficiency
LEARNING
Use different technologies and
interaction techniques to develop
useful, usable, and effectiveness
educational experiences

VISION
IA > AI
Intelligence Amplification Artificial Intelligence
(Klinker, 2018)
DATA VISUALIZATION AS A WAY OF
AUGMENTING HUMAN CAPACITIES TO PROCESS INFORMATION AND
TAKE DECISIONS
NOT TO SUPPORT AUTOMATIC DECISION MAKING

Decision Making and Situation Awareness
Design challenges in the era of Big Data and
Advanced Interaction
Examples of visualization design for the smart city
Conclusions
AGENDA

Is more data more information?
Is more information more knowledge?
Does more knowledge lead to take better
decisions?
?

From data to wisdom
DATA
INFORMATION
KNOWLEDGE
WISDOM
VALUE
The DIKW pyramid (Rowley, 2007)
Meaning
Applicability
Value
Human Input

Individual or collaborative process
Based on data, knowledge, experience,
skills, intuition…
Tacit & explicit knowledge (Nonaka,
2008)
DECISION MAKING

SA is being aware of what is happening around you
and understanding what that information means to you
now and in the future
(Endsley et al, 2016)
SITUATION AWARENESS

SITUATION AWARENESS
“is the perception of the elements in the environment
within a volume of time and space, the comprehension
of their meaning, and the projection of their status in the
near future” (Endsley, 1988)
Level 1: perception of the elements of the environment
Level 2: understanding of the current situation
Level 3: projection of future status

SITUATION AWARENESS
Our SA is affected by our goals, preconceptions/biases,
expectations, abilities, experience, training, stress,
workload, complexity of the task…
Our SA influences our decision making process and the
actions we undertake
Systems can be designed to improve SA (individual) and
Activity Awareness (collective SA)

BIG DATA
Huge volumes
Generated almost in real time
Structured and unstructured data
Exhaustive
Fine-grained (as detailed as possible)
Relational
Flexible and scalable

BIG DATA VS USEFUL INFORMATION
Data
DELUGE
Information
DEARTH

BIG DATA DESIGN
Envisioning useful BIG DATA systems:
DATA
INFORMATION
KNOWLEDGE
WISDOM
VALUE
FROM DATA-CENTRIC TO USER-CENTRED
GOAL-ORIENTED DESIGN

DESIGN RECOMMENDATIONS (I)
Design to meet the information tasks, goals and needs
• Focus first on the decision making process
• which are the big questions?
• who are the main actors?
• where, when and how are decisions taken?
• Analyse then which data are needed, how to find/sort/filter/
aggregate them

DESIGN RECOMMENDATIONS (II)
To identify tasks, goals and needs, follow a user-centred design
Three main principles:
• Use technology to keep the user in control of the system and aware
of the situation
• Focus on tasks, goals and abilities not (necessarily) on user comments
• Put the stress on human information processing and decision making

ADVANCED INTERACTION
New ways to interact with information (individually and
collectively, co-located and distributed)
Different devices provide different interaction affordances

DATA
INFORMATION
KNOWLEDGE
WISDOM
VALUE
GOAL-ORIENTED DESIGN
BIG DATA & ADVANCED INTERACTION DESIGN

BIG DATA & ADVANCED INTERACTION DESIGN
DATA
INFORMATION
KNOWLEDGE
WISDOM
VALUE
GOAL-ORIENTED DESIGN ADDING CONTEXTS OF USE

DESIGN RECOMMENDATIONS (III)
Understand how do people interact with data to generate
knowledge and wisdom

DESIGN RECOMMENDATIONS (IV)
Understand which devices and in which contexts information
processing is carried out
Understand first the problem: design research
Design for user acceptance
Take into account non-rational criteria:
Emotional and semantic design

Design as a research process
A design process involves
• grounding—investigation to gain multiple perspectives
on a problem;
• ideation—generation of many possible different
solutions;
• iteration—cyclical process of refining concept with
increasing fidelity; and reflection

“Design research implies an inquiry focused on producing a
contribution of knowledge… an intention to produce
knowledge and not the work to more immediately inform
the development of a commercial product” (Zimmerman
et al,2007)
“Design science research addresses important unsolved
problems in unique or innovative ways or solved problems
in a more efficient way” (Hevner et al, 2007)

Design for user acceptance
Acceptance is much more than usability or efficacy
Complex sociotechnological systems
Difference between data science and data engineering
Davis, F. D.; Bagozzi, R. P.; Warshaw, P. R. (1989), "User acceptance of computer technology: A
comparison of two theoretical models", Management Science 35: 982–1003

Design approaches
Design to meet the information tasks, goals and needs —>
Design Research approach
Design iteratively taking into account that designing is
providing an efficient solution to a problem given a number
of constraints and resources —> Engineering approach
Design to meet the user capabilities (cognitive and
physiological) as well as the interaction affordances of the
context of use —> HCI approach

Design techniques
Qualitative methods to analyse the problem (interviews,
focus groups, Delphi…)
Lean and iterative design (from wireframes to prototypes)
Participatory design and co-design
Generative design techniques to co-envision solutions
Qualitative methods to assess the results (evaluation trough
UTAUT, NASA-TLX…)

ENERGOS
DATA VISUALIZATION
FOR THE ELECTRICAL
SMART GRID

Design Challenges:
• Massive data (streaming)
• Multiple sources of information
• Envisioning the future
Current situation:
• Use of tacit knowledge
• High learning curve

?how to envision a solution for a
future information processing
situation?

Analysing the problem applying a user-centred
approach
who do you ask when the problem doesn’t even
exist yet?
User-centred does not mean accepting the user comments
and suggestions, means focusing on the user needs and
abilities and on the information processing tasks (Endsley et al,
2003)

Analysing the problem applying a user-centred
approach
Interviews and ethnographic studies to understand the
current situation
GTA, wireframes and mockups to envision solutions and test
Human Factors (skills, workload, communication, error
recovery, alarms, training)
Literature review to ground decisions
!

New operation roles and design paradigms
• New roles and interaction possibilities
• Goal based-design
• Visual scalability and usability
• Situational and activity awareness

Interaction environment
• Curved display used to support seamless exploration
and avoid losing context
Interface
• Coordinated goal-oriented views
• Avoid overlapping and loss of context
• Support communication and coordination among users

emerCien / PACE
CITIZEN
PARTICIPATION
IN CRISIS
emerCien (TIN201232782)

emerCien
Current situation
• Citizens play a key role in major disasters reporting
information via SSNN and grassroots platforms
• They are first-first responders
• Scalability, lack of resources and trust hinder the
incorporation of citizen information/action

Current situation
• Participation goes beyond communication
• information/knowledge sharing
• information integration and sense making
• decision making and coordinated action
• Take profit from citizens’ social capital
• Machizukuri movement in Japan
emerCien

?can we move from just
communication with citizens to
service co-production?

Future situation
emerCien

Design challenges
• Massive volume of heterogeneous data (including SSNN)
• Scalability, trust and empowerment issues
• from formal to substantive empowerment of citizens
and organisations
• Organizational and task constraints
• Multi-device environment
emerCien

Extensive use of FG and studies to shape the problem
• 1st study in British Columbia and Washington State
• 2nd study in Spain
• EFG with operation center managers
Design models to analyse the solutions
• Theory of crowd capital
• Ecologies of participants
emerCien

emerCien

Ecologies of devices
• Applications to support different goals performed by
different participants in different contexts
• Coordination among applications and views
Semantic and adaptable visualizations
emerCien

?which visualization works better to
make sense of the situation and
take informed action?

PACE
Design challenges
• Identify affordable visualisation tools to support decision
makers in analysing citizen-generated information
• Identify the right visualisation to answer relevant
questions
• Explore the interaction affordances of different
interaction paradigms

PACE
Semantic visualisation design approaches
• EFG with 20 experts to identify relevant questions in the
domain of EM
• Design of tool to use different visualisations
• Evaluation with non-expert users focusing on utility and
acceptance (UTAUT)

PACE
Semantic Visualisation tool

PACE
Immersive visualisation design approaches
• Exploratory design comparing different ways one
interacting with immersive data

CONCLUSIONS
Big data is much more than data analytics and DATA
The real breakthroughs will come by AUGMENTING human
capability to take decisions using data not by REPLACING
them
This is a wicked problem, exploratory, iterative, generative
and participatory approaches are required

We now live in a world where information is
potentially unlimited. Information is cheap,
but meaning is expensive.
Where is the meaning? Only human beings
can tell you where it is. We’re extracting
meaning from our minds and our own lives
George Dyson
MESSAGE TO BRING HOME

Useful references
• Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human
factors, 37(1), 32-64.
• Endsley, M. R. , Bolté, B. and Jones, D.G. (2016). Designing for situation awareness: An
approach to user-centered design. CRC press.
• Nonaka, I. (2008). The knowledge-creating company. Harvard Business Review Press.
• Rowley, J. (2007). The wisdom hierarchy: representations of the DIKW hierarchy. Journal of
information science, 33(2), 163-180.
• Hevner, A., & Chatterjee, S. (2010). Design science research in information systems. In Design
research in information systems(pp. 9-22). Springer US.
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information
technology: Toward a unified view. MIS quarterly, 425-478.
• Rubio, S., Díaz, E., Martín, J., & Puente, J. M. (2004). Evaluation of subjective mental workload: A
comparison of SWAT, NASA-TLX, and workload profile methods. Applied Psychology, 53(1),
61-86.

Related publications
• Onorati, T., Díaz, P., & Carrion, B. (2018). From social networks to emergency operation centers:
A semantic visualization approach. Future Generation Computer Systems.
• Romero-Gómez, R., and Díaz, P. "Towards a Design Pattern Language to Assist the Design of
Alarm Visualizations for Operating Control Systems." Digitally Supported Innovation. Springer.
249-264.
• Romero, R., Díez, D., Wittenburg, K., & Díaz, P. (2012, May). Envisioning grid vulnerabilities:
multi-dimensional visualization for electrical grid planning. In Proceedings of the International
Working Conference on Advanced Visual Interfaces (pp. 701-704). ACM.
• Herranz, S., Romero-Gómez, R., Díaz, P., & Onorati, T. (2014). Multi-view visualizations for
emergency communities of volunteers. Journal of Visual Languages & Computing, 25(6),
981-994.
• Díaz, P., Aedo, I., & Herranz, S. (2014, October). Citizen participation and social technologies:
exploring the perspective of emergency organizations. In International Conference on
Information Systems for Crisis Response and Management in Mediterranean Countries (pp.
85-97). Springer
• Gómez, R., Díez, D., Díaz P, Aedo, I. Situation Awareness-Oriented Alarm Visualizations: A next
Step in HSC Environments. GRAPP/IVAPP 2013: 483-488
• Santos, A., Zarraonandia, T., Díaz, P., & Aedo, I. (2018, May). A virtual reality map interface for
geographical information systems. In Proceedings of the 2018 International Conference on
Advanced Visual Interfaces (p. 83). ACM

Prof. Dr. Paloma Diaz
DEI LAB - Ins3tute of Financial Big Data
Universidad Carlos III de Madrid
pdp@inf.uc3m.es

Big data for situation awareness and decision making

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