22 May 2014 CDE competition: Information processing and sensemaking presentation

Information Processing and Sensemaking
C4ISR Concepts and Solutions Tranche 4
22 May 2014
© Crown copyright 2013 Dstl
27 May 2014
UK OFFICIAL
dstlsensors@dstl.gov.uk

Contents
• Military Advisor Brief: Setting the scene
• Technical Brief: The technical approach, challenges and some
of our work
• Customer Brief: A technology transition model and a view
from the customer
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Military Advisor Brief
The Problem Space
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Setting the Scene
• Information
– Context + Analysis
• Intelligence
• Situational Understanding
• Decision Making
– Human endeavour
– “More” data is not necessarily better
– Observe, Orient, Decide, Act
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Observe
Orient
Decide
Act

• Target based approach
– E.g. Detect enemy tanks
The Past
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*Decision*

The Present
• Target-based approach does not work
– Complex problems
– Problem-based approach is required
– Huge volume of disparate data available
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The Future
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The Challenge
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Time
Amount
Capacity of
decision
maker
Data
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Information processing and sensemaking
Technical brief
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Technical challenges
• Data association and correlation of both unstructured and
structured data
• Uncertainty propagation and management across multiple
data representations
• Automated hypothesis generation
• Automated learning to understand complex relationships
• Autonomous model generation
• Techniques that cope with large-scale data
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CDE themed competition
Key dates
– Launch event: 22 May 2014
– Webinar: 3 June 2014
– Competition close: 26 June 2014 at 5pm
– Proof-of-concept research complete: 31 August 2015
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CDE themed competition
• Funding
– £600k of funding for this phase 1 CDE competition
– £50-100k range per proposal
– up to £1M for phase 2 funding
• Duration and delivery
– up to 12 months duration from September 2014 to September 2015
– ideally with close technical partnering for delivery early and often focussing on
prototype code and software, not lengthy literature reviews
– if there is background intellectual property (IP), there should be 2 deliverables
of a full rights and limited version with background information clearly
identified
– Final deliverable for phase 1 should be a phase 2 proposal
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CDE competition funding
• A joint C4ISR concepts and solutions (CCS) and intelligence
collection and exploitation (ICE) project competition
• Joint MOD funding under:
– decision support and experimentation programme
• CCS project
• Project technical lead – Steven Meers
• CDE point of contact – Paul Thomas
– command, control, information and intelligence (C2I2) programme
• ICE project
• project technical lead and CDE point of contact – Warren Marks
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ICE project multi-intelligence work and ethos
• The ICE project delivers against the C2I2 programme
• The work builds on recent multi-intelligence efforts structured around applied
near-term research and development (R&D) and more basic R&D in text
analytics, spatio-temporal correlation and data association and:
– maintain an operational focus
– work in a data-rich environment
– don’t lose sight of the art of the possible
– use open-source technology where possible
– transition technology quickly to operations bearing in mind defence lines of
development (DLOD) considerations
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Some of our current multi-intelligence
activities
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Network analytics
• MAMBA
• MAMBA web services
• Log, path, web analytics
Text analytics
• Baleen
• Tag Crowd
• Dhugal
Spatio-temporal
correlation
• Maritime data association
• Event correlation
• ENVI services engine
Sense-making
• BANISH
• Virtual toolbox
Exploitation
• Technology transition
• User feedback
• B-S-G model
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Baleen – text processor
Datasources
Model generation
Tag Crowd
Baleen – UIMA pipeline
Graph store
Sesame / Apache Jena
Document store
MongoDB
Search index
ElasticSearch
Visualisation
eg Mamba
Search
Dhugal
Document ingest
Entity extraction
Relationship extraction
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Home Office exploitation:
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Log, path and web analytics
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©

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MAMBA framework encourages light and agile builds from the
shared framework with client builds as needed

Technical problems
• There are MOD joint user-agreed FY2014-
2015 research requirements
– data association and spatio-temporal
correlation
– text analytics
– error propagation
– activity-based intelligence
• understanding the world in terms of scenarios
• exploitation of multi-intelligence
• fuse the multi-intelligence to identify scenarios
of interest
– improved sensemaking
– problem decomposition
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Example: BANISH – Bayes Net tool
What is the probability that it is raining, given the grass is wet?
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• Simple, user-centric tool, for forming Bayes Nets applied to defence intelligence (DI)
• Create conceptual models and add variables, dependency, states with values (also
using DI uncertainty yardstick)
• Example opportunities for
further development:
– developments in data API
– crowd-sourced categorical
distributions or PDF
– identification of knowledge
gaps supporting ‘collect’
– fused graph with other users
and data
– judgement representation

Example: event correlation
• Process already collected data and implement correlation metrics producing
probability of association between events
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Process adapted from:
D Wang, D Pedreschi, C Song, F
Giannotti, Human mobility,
social ties, and link prediction,
KDD ‘11, San Diego, 2011

Example: event correlation
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• Example opportunities for development:
– global geohashing
– different data association and information level fusion algorithms eg
weighting – currently tf-idf
– probability of association linked to pattern-of-life work and eg links to
instantaneous entropy
• www.orchid.ac.uk/eprints/69/1/paper_extended_past2.pdf
– front-end spatially focussed dashboards and alerting opportunities
– fusion of derived information and real data within graphical models

Other examples and ideas
• From CCS advanced intelligence exploitation academic workshop:
– human and computer co-working on uncertainty representation leading
to suggested collection parameters for the system and prediction of
uncertainty
– machine learning techniques applied to already collected and analysed
data for future use and model development
• Application of other domain knowledge eg biologically inspired algorithms or
financial applications, such as used in algo trading, for predicting and
forecasting
• Deep learning algorithms, association analysis applied to various sources
– eg association rules produced with Home Office work
• Apply lessons learned from commercial sensemaking
– eg NetFlix prize, Amazon analytics, Google Knowledge Graph
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Potential data sources and frameworks
• Encourage use of free and open-
source software
• Encourage use of open standards
• Encourage good data Application
Programming Interfaces (API)
• Authority from MAMBA
partnership to provide end-user
license agreements (EULA) to
successful parties
• Apache Unstructured Information
Management architecture
• Ozone Widget Framework
• VAST 2014 data
– Previous VAST data sets
• Collected CCS datasets from
previous trials
• Open-source datasets
– Wikimapia
– DBpedia
– Freebase
– Twitter
– Transport for London Datasets
– www.gov.uk
– WW1 War Diaries
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The exploitation challenge
• There is a lot of brain power and effort already from the
community (MOD, open source, academia, industry)
• Together we could translate that into capability over the
proposal length (Sep 2014 to Sep 2015)
• Iterative delivery with a mechanism for exploitation and
verification and validation
• Opportunity for real quantitative and qualitative feedback
• Quickly move from academic papers and low technology
readiness level (TRL) work to medium TRL applications with
exploitation on operational systems
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Joint Forces Intelligence Group
brief
22 May 2014
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A technology
transition model
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22 May 2014 CDE competition: Information processing and sensemaking presentation

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22 May 2014 CDE competition: Information processing and sensemaking presentation