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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

Where’s the human?

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

Big data risks becoming an impediment rather than a help to human data users

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

main control room:ca. 3,000 indicators and controls

Should we add more sensors?

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?

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?

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

ADDING INFORMATIONTHE PROBLEM WITH

human information processing capacity

information from plant is increasinghuman capacity is not

ANALYTICS & AUTOMATIONTHE PROBLEM WITH

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?

What’s the problem

with automating analytics?

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

Operators Leave Traces

Operators Leave Traces

Guideline for Operator Nuclear Usability and Knowledge Elicitation (GONUKE)

Guideline for Operator Nuclear Usability and Knowledge Elicitation (GONUKE)

Guideline for Operator Nuclear Usability and Knowledge Elicitation (GONUKE)

Early (formative)

Late (summative)

More qualitative

More quantitative

Guideline for Operator Nuclear Usability and Knowledge Elicitation (GONUKE)

Guideline for Operator Nuclear Usability and Knowledge Elicitation (GONUKE)

Epistemiation: Capturing Expert Operator Knowledge to Design New System

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

ronald.boring@inl.govthomas.ulrich@inl.gov