Overarching technologies:Information Mgmt

Ed Hovy, USC/ISIBill Scherlis, CMU

Phil Cohen, OHSUHsinchun Chen, Arizona

Mike Goodchild, UCSBEva Kingsbury, NSF/CISE

Sharad Mehrotra, UCIDave Kehrlein, Calif OES

Bob Neches, USC/ISI

Research on the unexpected

The unexpected: a strawman perspective

• Understand the triggers– Reduce scope of what is “unexpected”– Our mission: understand fault models– Not our mission: model threats

• Design robust infrastructural systems– “System” = technology + people + policy + process– Respond gracefully to misuse

Dampen cascading; reduce consequences of failure A dependable system “allows reliance to be justifiably placed on the

service it delivers” [IFIP Dependability WG]– Our mission: identify requirements; understand feasibility

Prevent detect mitigate

• Design robust response systems– Preparedness of response mechanisms

Closely coupled with design of infrastructural systems Rapid change in rules of engagement: a flawed plan

– Our mission: identify requirements; understand feasibility

Building an agenda for impact-oriented research

• What’s the problem?– Who cares? – What are the (social, economic) stakes of failure/success?

• What can we do now?– What are the limiters to progress?

• What are the great ideas?– How can they be developed?– Which research communities to engage?

• What are the barriers to adoption and how to overcome?– Risks, scaling, culture, turf, incentives,…– Economics: funding, incentives, sustainment– E.g., mainstream headroom (cell, net) vs. crisis-specific (tents)

• What steps to take now?– How to get early validation of potential for long-term impact?– What is the expected overall timescale?– What is the structure and scale (critical mass) for the effort?

Scenarios

Information management scenarios

• Multiple October “flu” outbreaks– Instant epidemiology

Sensors + Fusion + Reportback + Iteration

– Detection, confirmation, etc. Data sources: physicians, grocery scans, school

attendance, lab tests, published pt records, etc. Issues: Data overwhelm, etc.

– Fusion: data mining, modeling, visualization Data sources: occupational, industrial, geographic,

weather, transportation routes, etc. Issues: Variable data quality, etc.

Information management scenarios

• Hurricane / earthquake– Instant bureaucracy– Claims management, identification, etc

What data is needed?

– Resourcing: planning, routing, tracking Example: Dave’s cranes

Information management scenarios

• Explosion on a highway– Triage: Bio? Chem? Nuclear? – Placarded?– – Guiding triage

Rapidly eliminate possibilities• Back-propagation in sensor data

– Highway sensors– Vehicle sensors nearby– Airborne sensors

• Fusion of human input• Situational context: anniversary date, etc.

– Role of other databases, web, etc.• Causal reasoning and diagnosis

Prediction• Respond according to worst case?• Or is it a truck explosion?• What will happen next?

Placard reqt?

Information management scenarios

• Explosion on a highway– Triage: Bio? Chem? Nuclear? – Placarded?– Instant confusion– Guiding triage

Rapidly eliminate possibilities• Back-propagation in sensor data

– Highway sensors– Vehicle sensors nearby– Airborne sensors

• Fusion of human input• Situational context: anniversary date, etc.

– Role of other databases, web, etc.• Causal reasoning and diagnosis

Prediction• Respond according to worst case?• Or is it a truck explosion?• What will happen next?

Bad guyon

board

Placard reqt?

Information Management, generally

Human interaction issues

• Attention management – “Overwhelm”

• Stress effects

• Awareness– Tailoring; push and pull

• Computer mediated collaboration– Group effects: f2f and computer mediated– Division of labor, expertise

… a rich HCI and social science literature here, but….

time

perf

computingcomms

stress

Human interaction issues

• Attention management – “Overwhelm”

• Stress effects

• Awareness– Tailoring; push and pull

• Computer mediated collaboration– Group effects: f2f and computer mediated– Division of labor, expertise

… a rich HCI and social science literature here, but….

time

perf

human attn

computingcomms

stress

Cycles and leverage points

• Needs by CM phase– Preparedness– Mitigation– Response– Recovery

• Analog: computer security– Prevent: write “safe code,” …– Detect: IDS, firewall, audit, …– Mitigate: self-healing architecture, …

• Analog: military C2– Military planning cycles– Response: Observe, Decide, Act– C2 goal: shorten/overlap the iterations– Particular challenge: Coalitions, trust, access

… what are the right process models? …

The flow of information

• Provisioning / gathering– Sensors (passive, active/mobile, ubiquitous), human input, simulated– Goal: Everything is a sensor

• Fusion / validation– Linking (human and automated), moniroting, triggering– Goal: Quality and comprehensiveness modeled– Goal: Ongoing (meta)data reconciliation

• Access– Security (military, civilian), authentication, protection– Goal: More trust from more “localized” trusted parties

• Exploitation / dissemination– Mining (automated and human), querying, triggering – Model development and simulation– What-if analysis, planning, decision-making– Exploration, visualization, presentation, pushing – Drive back to {data, sensor, human, fused} source– Goal: Tailored to user (“perceptual dissemination”)– Goal: Manage overwhelm– Goal: Detect errors and drive back to sources

• Collaboration / orgware– Ad hoc– Goal: Awareness (push/pull), no info loss, rapid consensus– Goal: Expertise effectively exploited

The corpus of information

• Schematization– Traditional schema-first

Structural and semantic GIS

– Corpus Traditional IR Textual / image structure Intrinsic metadata

– Semi-structured– Ad-hoc / extrinsic

WWW and raw hyperlinks Schema-later Rich links

• Economics– Who pays and when– Example: Strd Arg failures– Costs/benefits/risks of

preparation

• Information types– DB types– Geographical– Media: imagery, video,

sensor data, documents

• Metadata– Security and privacy

Classification (fed, state) Proprietary (multiple) Limited use (expiry?)

– Quality Trustworthiness Completeness Source

– Policy / legal Authority to use; turf (Coalition warfare model)

– Extrinsic Annotations, links, etc.

The long term trajectory of information

• New issues – Enablement of future crisis-driven integration of info

How to anticipate linking and needs?– Policy consequences: security, privacy

Rapid (emergency) policy reconfiguration Understanding and modeling consequences of release

• Privacy, unwanted linking, etc.• Gander: Pen/paper DB, controlled copies, limited access.

– Destroyed during mop-up.– Understanding the economics

Costs, risks, benefits, time, incentives “Dual use” (train as you fight; fight as you train)

• E.g., headroom in mainstream infrastructure Clearinghouse, transition, validation, maturity

• Risk and access

• Familiar topics, still critical– Schema evolution– Common data elements and metadata consensus– Legacy management: Reconciliation and wrapping

Technical challenges

Sensors and data collection

• Diverse sources– Digital dust

Large sensor networks: 100000’s of sensors– Self-report patterns: 911 calls, etc.– Multi-modal

• Mobile ubiquitous sources– Camera immersion– Sleeper sensors

structural sensors in bridges, buildings automobile sensors

– Rapid sensor-net deployment

• Where to store and process– Processing of massive data streams

• Sensor reliability and maintenance– Models and records– User feedback

• Security, authentication, etc.– E.g., for dust: emergent badness– E.g., for water security: internal sabotage threat

Communication

• Now– Intermittent (wireless) connectivity

Telecom crises are responder crises

– Interoperation challenges– Multiple redundant systems

• Needed– Instant dependable comms infrastructure– Bandwidth– [Enables offsite datacenters, reachback.]– [Raise the baseline]

Ontologies

• Definitions– 1. semantically enriched schema– 2. set of core conceptual elements and relationships– E.g., Objects and classes– E.g., Procedures and entities in the world– Example: street centerline (i.e., digraph of streets)

• Bkgd resources: model this

• Enable rapid filling of crisis-specific gaps

• Accommodate multiple media types– Imagery, sensor data, video, text, maps, etc., etc.

Fusion

• Semantics– Sensor level (raw data)– (information level) How to overcome differences of

definition? E.g., descriptions of fuel for forest fires: beyond “trees” E.g., unleaded gasoline E.g., race in census

• Syntax– Format: XML is not the whole solution

• Scale– Fusion wrt different levels of detail

• Currency, Trustworthiness

• Commensurability– E.g., positional correction

• Policy and policy aggregation

Mining

• Detecting anomalous or “interesting” patterns– Over diverse media types, e.g., surveillance

cameras

• Working “upstream” from an anomalous event– Back-propagation: Mining (preceding) data for

the (new) pattern that should now be detected

• Media and representations

Modeling and simulation

• Decision trees and anticipation– What is our “event type”?

E.g., explosion: chem/bio spread? E.g., anthrax in the mail

– How expected is this unexpected event?– What are the potential cascading steps?

• Multiple simulation models– Location of vulnerabilities

Co-located personnel– Interoperation of simulation models

• Modeling domains– Human behavior under stress– Organizational response

Presentation / visualization

• Emerging– Deployment to field PDAs

• Needed– Personalization/customization to field users,

media, others.– Drilldown and detail-level control– What is the usage model?– Fluid-modal interaction– Support for diversity in user population

Cultural, disabilities, language, context Sharing information with media, general public

GIS role

• Idea– Map as result of planning:

GIS as “instrument of choice” for intelligence fusion

• Current capability– FireScope: tools, people, organizational structure

GIS subcommittee: common mapping system Dependency on mutual aid

• (Calif: 21 fires with >100 fire depts involved)– Mobile GIS labs– Web/FTP sites

• Needed– 4D representation: navigate in {location + time}– USAR issue: CAD + GIS– Policy: tax/insurance advantage to capture data

Architecture and distribution

• Interlinking: systems and organizations

• Instant infrastructure– Pluggable interconnection medium

Comms KB and ontology

• Robustness– Maximize and localize capabilities– Principle: Graceful degradation– E.g., Networked PDAs without networks

HCI and human factors

• Team support and collaboration: – f2f and dist/mediated– Shift change– Role definition– Planning cycle: 12 hours (link w/shift chg)

How to accelerate planning cycles? In ICS: Situation Analysis (intelligence fusion)

– Trust and emotional state

Making it happen

Program formulation issues (examples)

• Delivery mechanism: – Adoption and risk– Examples:

Mainstream headroom (cell, net) vs. crisis-specific (tents)• Principle: Headroom model• E.g., SETI and other grid computing

Awareness of cultural context (e.g., crisis responders) What are the user’s real risk issues for acceptance?

• Role of tight collaborations– Researchers with users– Interdisciplinary:

IT researchers with social scientists Recognize the process cost of collaborative research