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

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mailto:[email protected]

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



Military Advisor Brief

The Problem Space

22 May 2014



Setting the Scene

• Information – Context + Analysis

• Intelligence • Situational Understanding • Decision Making

– Human endeavour

– “More” data is not necessarily better

– Observe, Orient, Decide, Act



Observe

Orient

Decide

Act

• Target based approach – E.g. Detect enemy tanks

The Past



*Decision*

The Present

• Target-based approach does not work – Complex problems – Problem-based approach is required – Huge volume of disparate data available



The Future



The Future



The Future



The Challenge


27 May 2014

Time

Amount Capacity of decision maker

Data

UK OFFICIAL

Information processing and sensemaking

Technical brief 22 May 2014



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



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



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


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



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



Some of our current multi-intelligence activities


27 May 2014

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

UK OFFICIAL

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





http://tanguy/tagcrowd/



Home Office exploitation:












Log, path and web analytics





©







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



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



Example: BANISH – Bayes Net tool

What is the probability that it is raining, given the grass is wet?



• 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



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



• 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

http://www.orchid.ac.uk/eprints/69/1/paper_extended_past2.pdf

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



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



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



Joint Forces Intelligence Group

brief

22 May 2014



A technology transition model

