Cervus We were formed in 2013 We come from Force Development - - PowerPoint PPT Presentation
Introduction to Cervus We were formed in 2013 We come from Force Development and Collective Training Backgrounds We exploit military and security training data to provide our customers with a comprehensive understanding of their
Backgrounds
customers with a comprehensive understanding of their
analytics and secure data storage solutions, we provide services to exploit your training data.
required to create the appropriate Environment
required to wrap around the environment with a logical and consistent scenario
required to capture appropriate contextual data and performance data on the training subjects
required to collate, analyse and manage data
required to exploit the data for AAR and as long term actionable insights
MANAGEMENT RULES SCENARIO ENVIRONMENT CAPTURE SYSTEM ANALYSIS SYSTEM EXPLOITATION SYSTEM
Commander Force Development and Training (Comd FDT) July 2011
Focussed on the achievement of Collective Training Objectives (CTOs) Data capture typically to support an after action review Known costs and budgeted Limited opportunity for repeatability Focussed on the experimentation
evidence Data capture to inform measures of performance / effectiveness Requiring repeatability Unknown costs and limited budget
modern sensors, power storage technologies, communications systems and data storage devices means there is no longer a need to limit data gathering to small data sets.
virtually all data, linked to all participants (BLUEFOR, OPFOR, wider COEFOR and
themselves) associated with every training event: allowing for an exponentially richer training data set to mine and exploit – enabled by a “capture everything now, use anything later” approach.
been within the Live training environment, and then mostly positional and firing data. However, there is a plethora of wider useful training data that can be harvested: human biometrics; vehicle usage, performance and health; C4I (voice, data and meta-data); ISR; topographical and meteorological data to name but a few.
as the Live, Virtual and Constructive training environments continue to blend ever more seamlessly into a single synthetic training environment, there is an
these synthetic domains to provide a far more holistic training data set to mine and exploit.
data and HUMS are both undervalued and underused training data sources.
new and emerging technologies allow for the routine and automatic harvesting and storage of far more, and far wider, objective data sets.
expertise-based subjective
but, as importantly, will provide a wealth of context within which to ultimately frame far more meaningful and useful insights from training.
machines provides an exceptional opportunity to move rapidly away from the resource-heavy, time-consuming activity of manual data analysis to an automated – even intelligent – analytical approach.
algorithms, pattern recognition technologies and machine learning approaches now mean that the drawing of meaningful insights from masses of data is simple, time-efficient, ever-improving, self-teaching and increasingly affordable. The obstacles that the manual processing and analysis of large amounts of training data once presented are now easily surmountable.
machine learning capability now allows for a genuine step-change from purely Descriptive analytics (What has just happened? What is happening now?) to Predictive analytics (What is likely to happen next, based upon experience?) to Prescriptive analytics (What could/should be done about what is likely to happen next, so as to achieve a positive outcome?).
data to real-time interpretation and understanding.
(via analysed data in context).
event, to helpful, proactive interventions during (and even before) training.
standards and modern cloud-based storage and processing technologies, the training data gathered, and the analysis drawn from it, will be of use not just to the immediate training community but also to individuals and the wider field army, force development, research, experimentation & acquisition communities – all of whom will be able to access the data they need whenever, and from wherever, necessary.
harvest, categorise, store, and analyse data from collective training environments.
than that, HIVE can deliver genuinely comprehensive and exploitable insights via its innovative machine learning engine and unique visual reporting systems: thus, allowing commanders to quickly spot and interpret trends from training, gain context-derived insights from them, and to rapidly and clearly identify
Matthew Syed
Collective Training Event
Id Name Start Date End Date 1 Initial Assessment 07/05/2018 07:35 07/05/2018 12:13 2 Mission 1 07/05/2018 13:35 07/05/2018 16:13 3 Mission 2 08/05/2018 07:35 08/05/2018 10:11 4 Mission 3 08/05/2018 12:35 07/05/2018 14:10 5 Mission 4 09/05/2018 07:35 09/05/2018 12:26 6 Initial Assessment 08/05/2018 06:55 08/05/2018 12:13 7 Mission 1 08/05/2018 12:35 07/05/2018 14:10 8 Mission 2 09/05/2018 07:35 09/05/2018 12:26 9 Mission 3 09/05/2018 14:13 09/05/2018 16:16 10Mission 4 10/05/2018 07:05 10/05/2018 14:15 11 Initial Assessment 09/05/2018 07:05 09/05/2018 12:11 12Mission 1 09/05/2018 14:40 09/05/2018 16:43 13Mission 2 10/05/2018 07:05 10/05/2018 12:11 14Mission 3 10/05/2018 14:10 10/05/2018 16:32 15Mission 4 11/05/2018 07:09 11/05/2018 12:23
Id Msn 4 Events 1 Battle Preparation 2 Vehicle Move to LoD 3 Pre designated Fires on Enemy Target 4 Adjustment of Fires 5 Fire Support from MIV 6 Cross LoD 7 Assault Building 1 Room 1 8 Assault Building 1 Room 2 9 Assault Building 1 Room 3 10 Enemy Fires onto BLUEFOR 11 Enemy Counter Attack from Building 2 onto Building 1 12 Exploit and Assault from Building 1 to Building 2 13 Assault Building 2 Room 1 14 Reorganisation 15 Casualty Extraction 16 POW Extraction 17 Hot Washup 18 Training System Reset
Fires Bde Fires Fires BG Fires HICON Coy HQ Dismount Sect Comd Dismount Sharpshooter Dismount Grenadier Dismount Grenadier Dismount NLAW Dismount ASM Dismount Sect 2IC Dismount Rifleman MIV AFV Crew Driver Crew Gunner Crew Commander Building Enemy Position Building Enemy Position Observer Mentor Primary OM Observer Mentor Secondary OM Observer Mentor Primary OM Observer Mentor Secondary OM Enemy Dismount Commander Enemy Dismount Sharpshooter Enemy Dismount Grenadier Enemy Dismount Anti Tank Enemy Fires Combat Team Fires
Simulation:
Management and Capture Tablets
Current Systems
Harvest All Data LVC and C2 Objective Data Intelligent Analysis Analytics Broad Utility