22 may 2014 cde competition: information processing and sensemaking presentation
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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
22 May 2014
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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 Future
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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 22 May 2014
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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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27 May 2014
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 Da
ta so
urce
s
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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Home Office exploitation:
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Home Office exploitation:
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Home Office exploitation:
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27 May 2014 UK OFFICIAL
Log, path and web analytics
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©
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27 May 2014 UK OFFICIAL
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MAMBA framework encourages light and agile builds from the shared framework with client builds as needed
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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27 May 2014 UK OFFICIAL
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
– 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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27 May 2014 UK OFFICIAL
Joint Forces Intelligence Group
brief
22 May 2014
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A technology transition model
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