SlideShare una empresa de Scribd logo
1 de 18
Safe Haven In a Box
Project Overview
AS-IS Process Analysis
Petros Papapanagiotou
presented at SOCIAM all-hands, Oxford, 18-21 September 2017
Current
architecture
Level 4
Administrative data (e.g., housing, education,
local authority)
Level 3
Health Board data x 14 regions
Level 2
NSS data beyond the 10 datasets
Level 1
NSH
10 datasets
Datasets by location
Proposed architecture
Visualisation
Data integration
Data provision Knowledge integration
entity
integration
Knowledge
Base
Entity Base
(Hub)
knowledge
integration
Spoke
spoke
visualisation
knowledge
management
import
query
import
search,
query
import
project data
preparation
query
CSV
conversion
DCAT
CSV
Spoke
import
DCAT
MySQL
DB
CSV
data
extraction
standards,
conven-
tions
Processes Infrastructure and Deployment Business Model
WP1
Specification
WP2
Knowledge
WP3
Data integration
and storage
WP5
Analytics and
visualization
WP6
Deployment
WP4
Process
W7
Business model
WP1
Specification
WP2
Knowledge
WP3
Data integration
and storage
WP5
Analytics and
visualization
WP6
Deployment
WP4
Process
W7
Business model
WP1
Specification
WP2
Knowledge
WP3
Data integration
and storage
WP5
Analytics and
visualization
WP6
Deployment
WP4
Process
AS-IS TO-BE
W7
Business model
✔
Business analysis – Process mapping
• Interviews w/ 4 eDRIS members
• Documents:
• SOP, checklists, process maps, guidelines
• Iterative BPMN Workflow modelling
• Different levels: 1  1  10  25 workflows
• Survey
• Report
Roles
Information Consultant
Research Coordinator
Analyst
Admin
Stakeholders
Researcher Organisation Data Provider
Safe Haven
(EPCC)
Public Benefit and
Privacy Panel for
Health and Social
Care (PBPP)
Indexing Team
Stages
Scoping Preparation Study Archive
Data
Extraction
Advice +
Approvals
Analysis +
Disclosure
High-level workflow
Timings survey
Step eDRIS Work Time Total Time
Min Max Min Max
Triage ??? ??? ??? ???
Request ??? ??? ??? ???
Check Approved Researcher ??? ??? ??? ???
Approvals ??? ??? ??? ???
Request Data Extraction ??? ??? ??? ???
Extract Data ??? ??? ??? ???
Indexing ??? ??? ??? ???
Sign Agreements ??? ??? ??? ???
Request Study Setup ??? ??? ??? ???
Linkage Process ??? ??? ??? ???
Analysis ??? ??? ??? ???
Disclosure ??? ??? ??? ???
Archive ??? ??? ??? ???
Return from Archive ??? ??? ??? ???
Study Closure ??? ??? ??? ???
(results redacted pending approval for public disclosure)
Timings survey
• 11 responses across eDRIS
• Total time to data: 20 days – 5.5 years
• Extreme cases – include Researcher delays
• 4 – 50 days worth of eDRIS work
• Half on Request and Data Extraction
Timings survey
Max eDRIS Work Time Max Total Time
Triage
4%
Request
15%
Check Approved
Researcher
15%
Approvals
7%
Request Data
Extraction
4%
Extract Data
6%
Indexing
1%
Sign Agreements
1%
Request
Study Setup
0%
Linkage
Process
1%
Analysis
45%
Disclosure
1%
Archive
0%
Return from Archive
0%
Study Closure
0%
Triage
2%
Request
31%
Check
Approved
Researcher
0%Approvals
11%
Request Data
Extraction
0%
Extract Data
21%
Indexing
2%
Sign
Agreements
2%
Reque
st
Study
Setup
0%
Linkage Process
11%
Analysis
10%
Disclosure
10%
Archive
0%
Return from
Archive
0%
Study
Closure
0%
Process Improvement
Knowledge
Management
• Dataset
Schemata
• Cohorts
• Synthetic data
• Query
Formalisation
• Data Extraction
• External Data
• Data Verification
• Disclosure
Verification
Operation
• Documentation
• Supportive
Documents
• Tracking &
Reminders
• Auditing
• Workflow
Automation
Integration
• Cost Estimation
• Redundant
Specifications
• PBPP
Integration
• Version Control
Process Improvement
Knowledge
Management
• Dataset
Schemata
• Cohorts
• Synthetic data
• Query
Formalisation
• Data Extraction
• External Data
• Data
Verification
• Disclosure
Verification
Operation
• Documentation
• Supportive
Documents
• Tracking &
Reminders
• Auditing
• Workflow
Automation
Integration
• Cost
Estimation
• Redundant
Specifications
• PBPP
Integration
• Version Control
Coming up…
• Validation with higher-ups
• Communication across team
• Dissemination
• TO-BE model

Más contenido relacionado

La actualidad más candente

Developing open data analysis pipelines in the cloud: Enabling the ‘big data’...
Developing open data analysis pipelines in the cloud: Enabling the ‘big data’...Developing open data analysis pipelines in the cloud: Enabling the ‘big data’...
Developing open data analysis pipelines in the cloud: Enabling the ‘big data’...Juan Antonio Vizcaino
 
Lightning Talk: Real-Time Analytics from MongoDB
Lightning Talk: Real-Time Analytics from MongoDBLightning Talk: Real-Time Analytics from MongoDB
Lightning Talk: Real-Time Analytics from MongoDBMongoDB
 
Data Strategy and Services at the British Library: Data, Software and PIDs
Data Strategy and Services at the British Library: Data, Software and PIDsData Strategy and Services at the British Library: Data, Software and PIDs
Data Strategy and Services at the British Library: Data, Software and PIDsSarah Anna Stewart
 
Workflows for Publishing Data; Scientific Data's experience as an early adopter
Workflows for Publishing Data; Scientific Data's experience as an early adopterWorkflows for Publishing Data; Scientific Data's experience as an early adopter
Workflows for Publishing Data; Scientific Data's experience as an early adopterVarsha Khodiyar
 
Secure Lab at the UK Data Service
Secure Lab at the UK Data ServiceSecure Lab at the UK Data Service
Secure Lab at the UK Data ServiceJisc RDM
 
Automated and Explainable Deep Learning for Clinical Language Understanding a...
Automated and Explainable Deep Learning for Clinical Language Understanding a...Automated and Explainable Deep Learning for Clinical Language Understanding a...
Automated and Explainable Deep Learning for Clinical Language Understanding a...Databricks
 
The Climate Tagger - a tagging and recommender service for climate informatio...
The Climate Tagger - a tagging and recommender service for climate informatio...The Climate Tagger - a tagging and recommender service for climate informatio...
The Climate Tagger - a tagging and recommender service for climate informatio...Martin Kaltenböck
 
"Don't Publish, Release" - Revisited
"Don't Publish, Release" - Revisited "Don't Publish, Release" - Revisited
"Don't Publish, Release" - Revisited Paul Groth
 

La actualidad más candente (8)

Developing open data analysis pipelines in the cloud: Enabling the ‘big data’...
Developing open data analysis pipelines in the cloud: Enabling the ‘big data’...Developing open data analysis pipelines in the cloud: Enabling the ‘big data’...
Developing open data analysis pipelines in the cloud: Enabling the ‘big data’...
 
Lightning Talk: Real-Time Analytics from MongoDB
Lightning Talk: Real-Time Analytics from MongoDBLightning Talk: Real-Time Analytics from MongoDB
Lightning Talk: Real-Time Analytics from MongoDB
 
Data Strategy and Services at the British Library: Data, Software and PIDs
Data Strategy and Services at the British Library: Data, Software and PIDsData Strategy and Services at the British Library: Data, Software and PIDs
Data Strategy and Services at the British Library: Data, Software and PIDs
 
Workflows for Publishing Data; Scientific Data's experience as an early adopter
Workflows for Publishing Data; Scientific Data's experience as an early adopterWorkflows for Publishing Data; Scientific Data's experience as an early adopter
Workflows for Publishing Data; Scientific Data's experience as an early adopter
 
Secure Lab at the UK Data Service
Secure Lab at the UK Data ServiceSecure Lab at the UK Data Service
Secure Lab at the UK Data Service
 
Automated and Explainable Deep Learning for Clinical Language Understanding a...
Automated and Explainable Deep Learning for Clinical Language Understanding a...Automated and Explainable Deep Learning for Clinical Language Understanding a...
Automated and Explainable Deep Learning for Clinical Language Understanding a...
 
The Climate Tagger - a tagging and recommender service for climate informatio...
The Climate Tagger - a tagging and recommender service for climate informatio...The Climate Tagger - a tagging and recommender service for climate informatio...
The Climate Tagger - a tagging and recommender service for climate informatio...
 
"Don't Publish, Release" - Revisited
"Don't Publish, Release" - Revisited "Don't Publish, Release" - Revisited
"Don't Publish, Release" - Revisited
 

Similar a Safe Haven in a Box, Petros Papapanagiotou

Research Data Management at the University of Salford
Research Data Management at the University of SalfordResearch Data Management at the University of Salford
Research Data Management at the University of SalfordDavid Clay
 
Taming the Beast: Test/QA on Large-scale Projects
Taming the Beast: Test/QA on Large-scale ProjectsTaming the Beast: Test/QA on Large-scale Projects
Taming the Beast: Test/QA on Large-scale ProjectsTechWell
 
Rabobank - There is something about Data
Rabobank - There is something about DataRabobank - There is something about Data
Rabobank - There is something about DataBigDataExpo
 
Niamh Brennan (Trinity College Dublin) – CERIFy
Niamh Brennan (Trinity College Dublin) – CERIFyNiamh Brennan (Trinity College Dublin) – CERIFy
Niamh Brennan (Trinity College Dublin) – CERIFyRepository Fringe
 
Tufts Research: Strategies from Data Management Leaders to Speed Clinical Trials
Tufts Research: Strategies from Data Management Leaders to Speed Clinical TrialsTufts Research: Strategies from Data Management Leaders to Speed Clinical Trials
Tufts Research: Strategies from Data Management Leaders to Speed Clinical TrialsVeeva Systems
 
PT-CRIS: EUROCRIS: 2013: Eco-system of research information systems
PT-CRIS: EUROCRIS: 2013: Eco-system of research information systemsPT-CRIS: EUROCRIS: 2013: Eco-system of research information systems
PT-CRIS: EUROCRIS: 2013: Eco-system of research information systemsJoão Mendes Moreira
 
Pt cris-casrai-rome-16-may-2014-v1
Pt cris-casrai-rome-16-may-2014-v1Pt cris-casrai-rome-16-may-2014-v1
Pt cris-casrai-rome-16-may-2014-v1João Mendes Moreira
 
UCSF Informatics Day 2014 - David Dobbs, "Enterprise Data Warehouse"
UCSF Informatics Day 2014 - David Dobbs, "Enterprise Data Warehouse"UCSF Informatics Day 2014 - David Dobbs, "Enterprise Data Warehouse"
UCSF Informatics Day 2014 - David Dobbs, "Enterprise Data Warehouse"CTSI at UCSF
 
Rethink Analytics with an Enterprise Data Hub
Rethink Analytics with an Enterprise Data HubRethink Analytics with an Enterprise Data Hub
Rethink Analytics with an Enterprise Data HubCloudera, Inc.
 
Fried data summit big data for lob content
Fried data summit big data for lob contentFried data summit big data for lob content
Fried data summit big data for lob contentJeff Fried
 
Data science.pptx
Data science.pptxData science.pptx
Data science.pptxHakkinsRaj
 
Forging Cultural Change: Transforming Your Organization Into a Data-Driven Ma...
Forging Cultural Change: Transforming Your Organization Into a Data-Driven Ma...Forging Cultural Change: Transforming Your Organization Into a Data-Driven Ma...
Forging Cultural Change: Transforming Your Organization Into a Data-Driven Ma...Erika Roach
 
BioIT 2017 - Ontoforce and Amgen Gene Knowledge Discovery
BioIT 2017 - Ontoforce and Amgen Gene Knowledge DiscoveryBioIT 2017 - Ontoforce and Amgen Gene Knowledge Discovery
BioIT 2017 - Ontoforce and Amgen Gene Knowledge DiscoveryWolfgang G. Hoeck
 
RDM shared services at IDCC
RDM shared services at IDCCRDM shared services at IDCC
RDM shared services at IDCCJisc RDM
 
FAIR BioData Management
FAIR BioData ManagementFAIR BioData Management
FAIR BioData ManagementUlrike Wittig
 
Jisc research data shared service overview IDCC 2016
Jisc research data shared service overview IDCC 2016Jisc research data shared service overview IDCC 2016
Jisc research data shared service overview IDCC 2016Jisc RDM
 
The State of the Data Warehouse in 2017 and Beyond
The State of the Data Warehouse in 2017 and BeyondThe State of the Data Warehouse in 2017 and Beyond
The State of the Data Warehouse in 2017 and BeyondSingleStore
 
OSA Con 2022 - Scaling your Pandas Analytics with Modin - Doris Lee - Ponder.pdf
OSA Con 2022 - Scaling your Pandas Analytics with Modin - Doris Lee - Ponder.pdfOSA Con 2022 - Scaling your Pandas Analytics with Modin - Doris Lee - Ponder.pdf
OSA Con 2022 - Scaling your Pandas Analytics with Modin - Doris Lee - Ponder.pdfAltinity Ltd
 
2013 OHSUG - Use Cases for Using the Program Type View in Oracle Life Science...
2013 OHSUG - Use Cases for Using the Program Type View in Oracle Life Science...2013 OHSUG - Use Cases for Using the Program Type View in Oracle Life Science...
2013 OHSUG - Use Cases for Using the Program Type View in Oracle Life Science...Perficient
 

Similar a Safe Haven in a Box, Petros Papapanagiotou (20)

Research Data Management at the University of Salford
Research Data Management at the University of SalfordResearch Data Management at the University of Salford
Research Data Management at the University of Salford
 
W7
W7W7
W7
 
Taming the Beast: Test/QA on Large-scale Projects
Taming the Beast: Test/QA on Large-scale ProjectsTaming the Beast: Test/QA on Large-scale Projects
Taming the Beast: Test/QA on Large-scale Projects
 
Rabobank - There is something about Data
Rabobank - There is something about DataRabobank - There is something about Data
Rabobank - There is something about Data
 
Niamh Brennan (Trinity College Dublin) – CERIFy
Niamh Brennan (Trinity College Dublin) – CERIFyNiamh Brennan (Trinity College Dublin) – CERIFy
Niamh Brennan (Trinity College Dublin) – CERIFy
 
Tufts Research: Strategies from Data Management Leaders to Speed Clinical Trials
Tufts Research: Strategies from Data Management Leaders to Speed Clinical TrialsTufts Research: Strategies from Data Management Leaders to Speed Clinical Trials
Tufts Research: Strategies from Data Management Leaders to Speed Clinical Trials
 
PT-CRIS: EUROCRIS: 2013: Eco-system of research information systems
PT-CRIS: EUROCRIS: 2013: Eco-system of research information systemsPT-CRIS: EUROCRIS: 2013: Eco-system of research information systems
PT-CRIS: EUROCRIS: 2013: Eco-system of research information systems
 
Pt cris-casrai-rome-16-may-2014-v1
Pt cris-casrai-rome-16-may-2014-v1Pt cris-casrai-rome-16-may-2014-v1
Pt cris-casrai-rome-16-may-2014-v1
 
UCSF Informatics Day 2014 - David Dobbs, "Enterprise Data Warehouse"
UCSF Informatics Day 2014 - David Dobbs, "Enterprise Data Warehouse"UCSF Informatics Day 2014 - David Dobbs, "Enterprise Data Warehouse"
UCSF Informatics Day 2014 - David Dobbs, "Enterprise Data Warehouse"
 
Rethink Analytics with an Enterprise Data Hub
Rethink Analytics with an Enterprise Data HubRethink Analytics with an Enterprise Data Hub
Rethink Analytics with an Enterprise Data Hub
 
Fried data summit big data for lob content
Fried data summit big data for lob contentFried data summit big data for lob content
Fried data summit big data for lob content
 
Data science.pptx
Data science.pptxData science.pptx
Data science.pptx
 
Forging Cultural Change: Transforming Your Organization Into a Data-Driven Ma...
Forging Cultural Change: Transforming Your Organization Into a Data-Driven Ma...Forging Cultural Change: Transforming Your Organization Into a Data-Driven Ma...
Forging Cultural Change: Transforming Your Organization Into a Data-Driven Ma...
 
BioIT 2017 - Ontoforce and Amgen Gene Knowledge Discovery
BioIT 2017 - Ontoforce and Amgen Gene Knowledge DiscoveryBioIT 2017 - Ontoforce and Amgen Gene Knowledge Discovery
BioIT 2017 - Ontoforce and Amgen Gene Knowledge Discovery
 
RDM shared services at IDCC
RDM shared services at IDCCRDM shared services at IDCC
RDM shared services at IDCC
 
FAIR BioData Management
FAIR BioData ManagementFAIR BioData Management
FAIR BioData Management
 
Jisc research data shared service overview IDCC 2016
Jisc research data shared service overview IDCC 2016Jisc research data shared service overview IDCC 2016
Jisc research data shared service overview IDCC 2016
 
The State of the Data Warehouse in 2017 and Beyond
The State of the Data Warehouse in 2017 and BeyondThe State of the Data Warehouse in 2017 and Beyond
The State of the Data Warehouse in 2017 and Beyond
 
OSA Con 2022 - Scaling your Pandas Analytics with Modin - Doris Lee - Ponder.pdf
OSA Con 2022 - Scaling your Pandas Analytics with Modin - Doris Lee - Ponder.pdfOSA Con 2022 - Scaling your Pandas Analytics with Modin - Doris Lee - Ponder.pdf
OSA Con 2022 - Scaling your Pandas Analytics with Modin - Doris Lee - Ponder.pdf
 
2013 OHSUG - Use Cases for Using the Program Type View in Oracle Life Science...
2013 OHSUG - Use Cases for Using the Program Type View in Oracle Life Science...2013 OHSUG - Use Cases for Using the Program Type View in Oracle Life Science...
2013 OHSUG - Use Cases for Using the Program Type View in Oracle Life Science...
 

Más de Ulrik Lyngs

Social Machines: Theoretical perspectives, Paul Smart
Social Machines: Theoretical perspectives, Paul SmartSocial Machines: Theoretical perspectives, Paul Smart
Social Machines: Theoretical perspectives, Paul SmartUlrik Lyngs
 
Mandevillian Intelligence, Paul Smart
Mandevillian Intelligence, Paul SmartMandevillian Intelligence, Paul Smart
Mandevillian Intelligence, Paul SmartUlrik Lyngs
 
Human-Extended Machine Cognition, Paul Smart
Human-Extended Machine Cognition, Paul SmartHuman-Extended Machine Cognition, Paul Smart
Human-Extended Machine Cognition, Paul SmartUlrik Lyngs
 
Understanding Algorithmic Decisions
Understanding Algorithmic DecisionsUnderstanding Algorithmic Decisions
Understanding Algorithmic DecisionsUlrik Lyngs
 
Zooniverse Update
Zooniverse UpdateZooniverse Update
Zooniverse UpdateUlrik Lyngs
 
Data sharing in the age of the Social Machine
Data sharing in the age of the Social MachineData sharing in the age of the Social Machine
Data sharing in the age of the Social MachineUlrik Lyngs
 
Ulysses in Cyberspace: Distraction and Self-Regulation in Social Machines
Ulysses in Cyberspace: Distraction and Self-Regulation in Social MachinesUlysses in Cyberspace: Distraction and Self-Regulation in Social Machines
Ulysses in Cyberspace: Distraction and Self-Regulation in Social MachinesUlrik Lyngs
 
SoLiD co operating.systems
SoLiD co operating.systemsSoLiD co operating.systems
SoLiD co operating.systemsUlrik Lyngs
 
Sociagrams: How to design a social machine
Sociagrams: How to design a social machineSociagrams: How to design a social machine
Sociagrams: How to design a social machineUlrik Lyngs
 
Privacy-Preserving Data Analysis, Adria Gascon
Privacy-Preserving Data Analysis, Adria GasconPrivacy-Preserving Data Analysis, Adria Gascon
Privacy-Preserving Data Analysis, Adria GasconUlrik Lyngs
 
A Privacy Framework for Social Machines
A Privacy Framework for Social MachinesA Privacy Framework for Social Machines
A Privacy Framework for Social MachinesUlrik Lyngs
 
SOCIAM Book: The Theory and Practice of Social Machines
SOCIAM Book: The Theory and Practice of Social MachinesSOCIAM Book: The Theory and Practice of Social Machines
SOCIAM Book: The Theory and Practice of Social MachinesUlrik Lyngs
 
Provenance and Analytics for Social Machines, Trung Dong Huynh
Provenance and Analytics for Social Machines, Trung Dong HuynhProvenance and Analytics for Social Machines, Trung Dong Huynh
Provenance and Analytics for Social Machines, Trung Dong HuynhUlrik Lyngs
 

Más de Ulrik Lyngs (14)

Social Machines: Theoretical perspectives, Paul Smart
Social Machines: Theoretical perspectives, Paul SmartSocial Machines: Theoretical perspectives, Paul Smart
Social Machines: Theoretical perspectives, Paul Smart
 
Mandevillian Intelligence, Paul Smart
Mandevillian Intelligence, Paul SmartMandevillian Intelligence, Paul Smart
Mandevillian Intelligence, Paul Smart
 
Human-Extended Machine Cognition, Paul Smart
Human-Extended Machine Cognition, Paul SmartHuman-Extended Machine Cognition, Paul Smart
Human-Extended Machine Cognition, Paul Smart
 
Understanding Algorithmic Decisions
Understanding Algorithmic DecisionsUnderstanding Algorithmic Decisions
Understanding Algorithmic Decisions
 
Zooniverse Update
Zooniverse UpdateZooniverse Update
Zooniverse Update
 
Data sharing in the age of the Social Machine
Data sharing in the age of the Social MachineData sharing in the age of the Social Machine
Data sharing in the age of the Social Machine
 
Ulysses in Cyberspace: Distraction and Self-Regulation in Social Machines
Ulysses in Cyberspace: Distraction and Self-Regulation in Social MachinesUlysses in Cyberspace: Distraction and Self-Regulation in Social Machines
Ulysses in Cyberspace: Distraction and Self-Regulation in Social Machines
 
SoLiD co operating.systems
SoLiD co operating.systemsSoLiD co operating.systems
SoLiD co operating.systems
 
Sociagrams: How to design a social machine
Sociagrams: How to design a social machineSociagrams: How to design a social machine
Sociagrams: How to design a social machine
 
App Observatory
App ObservatoryApp Observatory
App Observatory
 
Privacy-Preserving Data Analysis, Adria Gascon
Privacy-Preserving Data Analysis, Adria GasconPrivacy-Preserving Data Analysis, Adria Gascon
Privacy-Preserving Data Analysis, Adria Gascon
 
A Privacy Framework for Social Machines
A Privacy Framework for Social MachinesA Privacy Framework for Social Machines
A Privacy Framework for Social Machines
 
SOCIAM Book: The Theory and Practice of Social Machines
SOCIAM Book: The Theory and Practice of Social MachinesSOCIAM Book: The Theory and Practice of Social Machines
SOCIAM Book: The Theory and Practice of Social Machines
 
Provenance and Analytics for Social Machines, Trung Dong Huynh
Provenance and Analytics for Social Machines, Trung Dong HuynhProvenance and Analytics for Social Machines, Trung Dong Huynh
Provenance and Analytics for Social Machines, Trung Dong Huynh
 

Último

ICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesrafiqahmad00786416
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native ApplicationsWSO2
 
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot ModelMcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot ModelDeepika Singh
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxRustici Software
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Victor Rentea
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWERMadyBayot
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc
 
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamUiPathCommunity
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Zilliz
 
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Orbitshub
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...apidays
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Angeliki Cooney
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingEdi Saputra
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...Zilliz
 
Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityPlatformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityWSO2
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobeapidays
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...apidays
 

Último (20)

ICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesICT role in 21st century education and its challenges
ICT role in 21st century education and its challenges
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot ModelMcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
 
Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityPlatformless Horizons for Digital Adaptability
Platformless Horizons for Digital Adaptability
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 

Safe Haven in a Box, Petros Papapanagiotou

  • 1. Safe Haven In a Box Project Overview AS-IS Process Analysis Petros Papapanagiotou presented at SOCIAM all-hands, Oxford, 18-21 September 2017
  • 3. Level 4 Administrative data (e.g., housing, education, local authority) Level 3 Health Board data x 14 regions Level 2 NSS data beyond the 10 datasets Level 1 NSH 10 datasets Datasets by location
  • 4. Proposed architecture Visualisation Data integration Data provision Knowledge integration entity integration Knowledge Base Entity Base (Hub) knowledge integration Spoke spoke visualisation knowledge management import query import search, query import project data preparation query CSV conversion DCAT CSV Spoke import DCAT MySQL DB CSV data extraction standards, conven- tions Processes Infrastructure and Deployment Business Model
  • 5. WP1 Specification WP2 Knowledge WP3 Data integration and storage WP5 Analytics and visualization WP6 Deployment WP4 Process W7 Business model
  • 6. WP1 Specification WP2 Knowledge WP3 Data integration and storage WP5 Analytics and visualization WP6 Deployment WP4 Process W7 Business model
  • 7. WP1 Specification WP2 Knowledge WP3 Data integration and storage WP5 Analytics and visualization WP6 Deployment WP4 Process AS-IS TO-BE W7 Business model ✔
  • 8. Business analysis – Process mapping • Interviews w/ 4 eDRIS members • Documents: • SOP, checklists, process maps, guidelines • Iterative BPMN Workflow modelling • Different levels: 1  1  10  25 workflows • Survey • Report
  • 10. Stakeholders Researcher Organisation Data Provider Safe Haven (EPCC) Public Benefit and Privacy Panel for Health and Social Care (PBPP) Indexing Team
  • 11. Stages Scoping Preparation Study Archive Data Extraction Advice + Approvals Analysis + Disclosure
  • 13. Timings survey Step eDRIS Work Time Total Time Min Max Min Max Triage ??? ??? ??? ??? Request ??? ??? ??? ??? Check Approved Researcher ??? ??? ??? ??? Approvals ??? ??? ??? ??? Request Data Extraction ??? ??? ??? ??? Extract Data ??? ??? ??? ??? Indexing ??? ??? ??? ??? Sign Agreements ??? ??? ??? ??? Request Study Setup ??? ??? ??? ??? Linkage Process ??? ??? ??? ??? Analysis ??? ??? ??? ??? Disclosure ??? ??? ??? ??? Archive ??? ??? ??? ??? Return from Archive ??? ??? ??? ??? Study Closure ??? ??? ??? ??? (results redacted pending approval for public disclosure)
  • 14. Timings survey • 11 responses across eDRIS • Total time to data: 20 days – 5.5 years • Extreme cases – include Researcher delays • 4 – 50 days worth of eDRIS work • Half on Request and Data Extraction
  • 15. Timings survey Max eDRIS Work Time Max Total Time Triage 4% Request 15% Check Approved Researcher 15% Approvals 7% Request Data Extraction 4% Extract Data 6% Indexing 1% Sign Agreements 1% Request Study Setup 0% Linkage Process 1% Analysis 45% Disclosure 1% Archive 0% Return from Archive 0% Study Closure 0% Triage 2% Request 31% Check Approved Researcher 0%Approvals 11% Request Data Extraction 0% Extract Data 21% Indexing 2% Sign Agreements 2% Reque st Study Setup 0% Linkage Process 11% Analysis 10% Disclosure 10% Archive 0% Return from Archive 0% Study Closure 0%
  • 16. Process Improvement Knowledge Management • Dataset Schemata • Cohorts • Synthetic data • Query Formalisation • Data Extraction • External Data • Data Verification • Disclosure Verification Operation • Documentation • Supportive Documents • Tracking & Reminders • Auditing • Workflow Automation Integration • Cost Estimation • Redundant Specifications • PBPP Integration • Version Control
  • 17. Process Improvement Knowledge Management • Dataset Schemata • Cohorts • Synthetic data • Query Formalisation • Data Extraction • External Data • Data Verification • Disclosure Verification Operation • Documentation • Supportive Documents • Tracking & Reminders • Auditing • Workflow Automation Integration • Cost Estimation • Redundant Specifications • PBPP Integration • Version Control
  • 18. Coming up… • Validation with higher-ups • Communication across team • Dissemination • TO-BE model