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Humans as Users of Big Data
Ronald L. Boring, PhDThomas A. Ulrich, PhD
Idaho National Laboratory
Humans as Sources of Big Data
Big data are ultimately used by humans for some purpose—usually decision making—but we do not always consider humans in the data we create
the impetus for control rooms was the need to consolidate multiple
distal information sources and controls
the fundamental nature of control rooms remains largely unchanged, even as technology has gone digital
and data sources proliferate
main control room:ca. 3,000 indicators and controls
Should we add more sensors?ý Operators are already information overloaded
main control room:ca. 3,000 indicators and controls
Do we add advanced analytics?ý Better approach, but adding
analytics is still additional information for operators if nothing
is taken away first
main control room:ca. 3,000 indicators and controls
Do we automate?ý Taking operator out of the loop
risks minimizing tools to help operators make decisions
A wolf is a hunter• Goes looking for its prey• Information analogy: We seek information
• We pull information to us
A spider builds a nest• Builds a Web and waits for its prey to come to it• Information analogy: We subscribe to
information that is relevant to us• Information is pushed to us
We are Wolves / We are SpidersINFORMATION FORAGING THEORY
Wolves• Operators actively seek information in the control room to
support diagnosis of plant states• They pull plant information from status indicators
Spiders• Operators passively receive information in the control room
from alarms• Alarms push information to guide operator response
Are Operators Wolves or Spiders?
A spider• May miss the active process of
capturing the information• Active search for information
supports situation awareness and vigilance
• Can only catch information from the defined web• Keyhole effect—may miss important
information outside the web• May lose transparency of
information processing
Automating Analytics Makes Us Spiders
HABA-MABA• [Humans] Are Better At-
Machines Are Better At (HABA-MABA; Fitts, 1951; Swain, 1980)
• Machines (i.e., computers) are catching up (see *), whereas humans are not becoming more capable on their own
– Human performance has peaked
– Training, procedures, and HMIs can only go so far in improving human performance
• Humans are still better at some things
Humans Are Better At (HABA)
Detection of certain forms of energy*
Sensitivity to wide range of stimuli*
Pattern recognition and generalization*Signal detection in high noise environments*
Ability to remember relevant facts at appropriate times*
Ability to use judgment Ability to improvise and adopt flexible proceduresAbility to handle unexpected eventsAbility to arrive and novel solutions to problemsAbility to learn from experienceAbility to track wide variety of situations*Ability to perform fine manipulations*Ability to perform when overloaded*Ability to reason inductively
What Are Humans Good At?
HABA-MABA
Machines Are Better At (MABA)
Monitoring
Performance of routine, repetitive, precise tasks
Responding quicklyExerting large amounts of force smoothly and precisely
Storing and recalling large amounts of precise data
Computing abilitySensitivity to specific stimuliHandling of complex operations (multitasking)Deductive reasoningInsensitivity to extraneous factors (e.g., harsh environments)
What Are Machines Good At?
Function Allocation• Automation vs.
manual operation is not either-or
• Six levels of automation depending on context
What Are Machines Good At?
Key Concepts of Automation• Control automation improves efficiency and reliability beyond
what humans can do– Significant economic advantages in reducing staffing and
training requirements• Information automation improves operator situation awareness
and reduces workload– e.g., key information at a glance and lack of alarm flooding
Optimizing Automation
þ Keep operator in the loop for those things like decision making where human
input is desirable
Key Concepts of Automation• Control automation improves efficiency and reliability beyond
what humans can do– Significant economic advantages in reducing staffing and
training requirements• Information automation improves operator situation awareness
and reduces workload– e.g., key information at a glance and lack of alarm flooding
Optimizing Automation
þ Big data visualization should simplify or distill information for operators, not
increase the level of information
Example Visualization
Current DCS Advanced HMI State-of-Art HMI
prototype and evaluate through operator-in-the-loop studies
Computerized Operator Support System (COSS)• Collection of technologies (INL’s ANIME HMI + Argonne’s PROAID prognostic
system) to assist operators in monitoring the plant and making timely, informed decisions
State-of-Art HMI
Assisting operators in early fault detection• Detection – recognizing the symptoms of a
plant fault• Validation – determining that the symptoms
are the result of a real plant fault and not a sensor failure
• Diagnosis – determining the specific plant fault• Mitigation – either correcting or isolating the
plant fault such that it is no longer a threat to plant operations or nuclear safety
• Monitoring – monitoring the symptoms of the plant fault to ensure that the mitigation has been successful
• Recovery – restoring the plant to the pre-fault conditions
Guideline for Operator Nuclear Usability and Knowledge Elicitation (GONUKE)
Early (formative)
Late (summative)
More qualitative
More quantitative
Through their actions, operators are indirectly communicating• The problem they are focusing on• Their understanding (or lack of understanding)• Their situation awareness• Their stress levels• Their engagement• Their knowledge• Their performanceWhile there are privacy concerns, these data are not yet being harvestedOperator data can tell us how to tailor our big data to operators• Context-dependent information visualization• Adaptive interfaces• Dynamic levels of automation
Operators As Data
human limitationshumans are not able to process infinite amounts of data, and data must be distilled to be meaningful
human strengthsautomating analytics eliminates the core cognitive advantages of human decision makers
humans as datahumans provide clues to what they are doing, but these data are not being used to tailor plant controls
[email protected]@inl.gov