EDF2014: BIG - NESSI Networking Session: Edward Curry, National University of Ireland Galway: The Big Data Value Chain

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BIG - NESSI Networking Session, Talk by Edward Curry, National University of Ireland Galway at the European Data Forum 2014, 20 March 2014 in Athens, Greece: The Big Data Value Chain.

Edward Curry, National University of Ireland Galway
European Data Forum, Athens, 20 February 2014

SECTORIAL FORUMS AND TECHNICAL
WORKING GROUPS
Health Public Sector
Finance &
Insurance
Telco, Media&
Entertainment
Manufacturing,
Retail, Energy, T
ransport
Needs Offerings
Big Data Value Chain
Technical Working Groups
Industry Driven Sectorial Forums
Data
Acquisition
Data
Analysis
Data
Curation
Data
Storage
Data
Usage
• Structured data
• Unstructured data
• Event processing
• Sensor networks
• Protocols
• Real-time
• Data streams
• Multimodality
• Stream mining
• Semantic analysis
• Machine learning
• Information
extraction
• Linked Data
• Data discovery
• ‘Whole world’
semantics
• Ecosystems
• Community data
analysis
• Cross-sectorial data
analysis
• Data Quality
• Trust / Provenance
• Annotation
• Data validation
• Human-Data
Interaction
• Top-down/Bottom-up
• Community / Crowd
• Human Computation
• Curation at scale
• Incentivisation
• Automation
• Interoperability
• In-Memory DBs
• NoSQL DBs
• NewSQL DBs
• Cloud storage
• Query Interfaces
• Scalability and
Performance
• Data Models
• Consistency, Availabil
ity, Partition-
tolerance
• Security and Privacy
• Standardization
• Decision support
• Prediction
• In-use analytics
• Simulation
• Exploration
• Visualisation
• Modeling
• Control
• Domain-specific
usage

Senior
Academic
Senior
Management
Middle
Researcher
Middle
Management
Position in Organisation
University
MNC
SME
Other
Types of Organisations
1. Literature & Technical Survey
2. Subject Matter Expert Interviews
3. Stakeholder Workshops
4. Online Questionnaire (with
NESSI)
• Early adopters
• Business enablement
• Technical maturity
• Key Opinion Leaders
Methodology
Interviewee Breakdown
Target Interviewee

Interviews and Technical White Papers available on:
http://www.big-project.eu
Expert Interviews Technical
Whitepapers
▶ Executive Overview
▶ Key Insights
▶ Social & Economic
Impact
▶ Concise State of the Art
▶ Future Requirements &
Emerging Trends
▶ Sector-specific Case
Studies
Next versions released in
April

Key Trends
▶ Lower usability barrier for data tools
▶ Blended human and algorithmic data processing for coping with
for data quality
▶ Leveraging large communities (crowds)
▶ Need for semantic standardized data representation
▶ Significant increase in use of new data models (i.e. graph)
(expressivity and flexibility)
▶ Much of (Big Data) technology
is evolving evolutionary
▶ But business processes change
must be revolutionary
▶ Data variety and verifiability
are key opportunities
▶ Long tail of data variety is a
major shift in the data landscape
The Data Landscape
▶ Lack of Business-driven Big
Data strategies
▶ Need for format and data
storage technology standards
▶ Data exchange between
companies, institutions, individ
uals, etc.
▶ Regulations & markets for data
access
▶ Human resources: Lack of
skilled data scientists
Biggest Blockers
Technical White Papers available on:
http://www.big-project.eu

Data
Acquisition
Data
Analysis
Data
Curation
Data
Storage
Data
Usage
Axel Ngonga
ngonga@infor
matik.uni-
leipzig.de
John Domingue
john.domingue
@open.ac.uk
Edward Curry
ed.curry@der
i.org
Martin Strohbach
MStrohbach@agti
nternational.com
Tilman Becker
becker@dfki.de
▶Join the bigdatavalue.eu
mailing list
@BIG_FP7
http://big-project.eu/
info@big-project.eu

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We live in an era of cloud computing. Many of the services in the life sciences are keenly planning cloud transformations, seeking to create globally distributed ecosystems of harmonised data based on standards from organisations like GA4GH. CINECA faces similar challenges, gathering cohort datasets from all over the globe, many of which are pinned in place, due to their size, legal restrictions, or other considerations. But is “bringing compute to the data” always the right choice? In this webinar, based on experiences from the Human Cell Atlas Data Coordination Platform and other projects from EMBL-EBI, we will explore the concept of “data gravity”: The idea that whilst there are forces that may hold data in one place, there are others that require it to be mobile. We’ll consider how effectively planning a cloud strategy requires consideration of the gravity of datasets, and the impact it may have on team skills required, incentives for good practice, and storage and compute costs. The CINECA webinar series aims to discuss ways to address common challenges and share best practices in the field of cohort data analysis, as well as distribute CINECA project results. All CINECA webinars include an audience Q&A session during which attendees can ask questions and make suggestions. Please note that all webinars are recorded and available for posterior viewing. CINECA webinars include an audience Q&A session during which attendees can ask questions and make suggestions. This webinar took place on 12th November 2020 and is part of the CINECA webinar series. For previous and upcoming CINECA webinars see: https://www.cineca-project.eu/webinars

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CTO Perspectives: What's Next for Data Management and Healthcare?

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EDF2014: BIG - NESSI Networking Session: Edward Curry, National University of Ireland Galway: The Big Data Value Chain

1. Edward Curry, National University of Ireland Galway European Data Forum, Athens, 20 February 2014

2. SECTORIAL FORUMS AND TECHNICAL WORKING GROUPS Health Public Sector Finance & Insurance Telco, Media& Entertainment Manufacturing, Retail, Energy, T ransport Needs Offerings Big Data Value Chain Technical Working Groups Industry Driven Sectorial Forums Data Acquisition Data Analysis Data Curation Data Storage Data Usage • Structured data • Unstructured data • Event processing • Sensor networks • Protocols • Real-time • Data streams • Multimodality • Stream mining • Semantic analysis • Machine learning • Information extraction • Linked Data • Data discovery • ‘Whole world’ semantics • Ecosystems • Community data analysis • Cross-sectorial data analysis • Data Quality • Trust / Provenance • Annotation • Data validation • Human-Data Interaction • Top-down/Bottom-up • Community / Crowd • Human Computation • Curation at scale • Incentivisation • Automation • Interoperability • In-Memory DBs • NoSQL DBs • NewSQL DBs • Cloud storage • Query Interfaces • Scalability and Performance • Data Models • Consistency, Availabil ity, Partition- tolerance • Security and Privacy • Standardization • Decision support • Prediction • In-use analytics • Simulation • Exploration • Visualisation • Modeling • Control • Domain-specific usage

3. Senior Academic Senior Management Middle Researcher Middle Management Position in Organisation University MNC SME Other Types of Organisations 1. Literature & Technical Survey 2. Subject Matter Expert Interviews 3. Stakeholder Workshops 4. Online Questionnaire (with NESSI) • Early adopters • Business enablement • Technical maturity • Key Opinion Leaders Methodology Interviewee Breakdown Target Interviewee

5. Interviews and Technical White Papers available on: http://www.big-project.eu Expert Interviews Technical Whitepapers ▶ Executive Overview ▶ Key Insights ▶ Social & Economic Impact ▶ Concise State of the Art ▶ Future Requirements & Emerging Trends ▶ Sector-specific Case Studies Next versions released in April

6. Key Trends ▶ Lower usability barrier for data tools ▶ Blended human and algorithmic data processing for coping with for data quality ▶ Leveraging large communities (crowds) ▶ Need for semantic standardized data representation ▶ Significant increase in use of new data models (i.e. graph) (expressivity and flexibility) ▶ Much of (Big Data) technology is evolving evolutionary ▶ But business processes change must be revolutionary ▶ Data variety and verifiability are key opportunities ▶ Long tail of data variety is a major shift in the data landscape The Data Landscape ▶ Lack of Business-driven Big Data strategies ▶ Need for format and data storage technology standards ▶ Data exchange between companies, institutions, individ uals, etc. ▶ Regulations & markets for data access ▶ Human resources: Lack of skilled data scientists Biggest Blockers Technical White Papers available on: http://www.big-project.eu

7. Data Acquisition Data Analysis Data Curation Data Storage Data Usage Axel Ngonga ngonga@infor matik.uni- leipzig.de John Domingue john.domingue @open.ac.uk Edward Curry ed.curry@der i.org Martin Strohbach MStrohbach@agti nternational.com Tilman Becker becker@dfki.de ▶Join the bigdatavalue.eu mailing list @BIG_FP7 http://big-project.eu/ info@big-project.eu

EDF2014: BIG - NESSI Networking Session: Edward Curry, National University of Ireland Galway: The Big Data Value Chain

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