Transcript Overarching Technologies: Information Management

Overarching technologies:
Information Mgmt
Ed Hovy, USC/ISI
Bill Scherlis, CMU
Phil Cohen, OHSU
Hsinchun Chen, Arizona
Mike Goodchild, UCSB
Eva Kingsbury, NSF/CISE
Sharad Mehrotra, UCI
Dave 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?
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– 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?
Placard reqt?
Bad guy
on
board
Information Management, generally
Human interaction issues
• Attention management
computing
comms
– “Overwhelm”
• Stress effects
• Awareness
– Tailoring; push and pull
perf
time
stress
• Computer mediated collaboration
– Group effects: f2f and computer mediated
– Division of labor, expertise
… a rich HCI and social science literature here, but….
Human interaction issues
• Attention management
computing
comms
– “Overwhelm”
• Stress effects
• Awareness
– Tailoring; push and pull
perf
human attn
time
stress
• Computer mediated collaboration
– Group effects: f2f and computer mediated
– Division of labor, expertise
… a rich HCI and social science literature here, but….
Cycles and leverage points
• Needs by CM phase
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Preparedness
Mitigation
Response
Recovery
• Analog: computer security
– Prevent: write “safe code,” …
– Detect: IDS, firewall, audit, …
– Mitigate: self-healing architecture, …
• Analog: military C2
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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
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Provisioning / gathering
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Fusion / validation
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Access
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Exploitation / dissemination
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Collaboration / orgware
– Sensors (passive, active/mobile, ubiquitous), human input, simulated
– Goal: Everything is a sensor
– Linking (human and automated), moniroting, triggering
– Goal: Quality and comprehensiveness modeled
– Goal: Ongoing (meta)data reconciliation
– Security (military, civilian), authentication, protection
– Goal: More trust from more “localized” trusted parties
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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
– Ad hoc
– Goal: Awareness (push/pull), no info loss, rapid consensus
– Goal: Expertise effectively exploited
The corpus of information
• 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.
• 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
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
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Instant dependable comms infrastructure
Bandwidth
[Enables offsite datacenters, reachback.]
[Raise the baseline]
Ontologies
• Definitions
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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:
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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