SlideShare ist ein Scribd-Unternehmen logo
1 von 35
Big Data and Social
Machines
David De Roure
e-Research Centre, University of Oxford
@dder
Overview
1. Big Data for research (UK perspective)
2. Several shifts in scholarship
3. Social Machines
4. Towards a new knowledge infrastructure
Edwards, P. N., et al. (2013) Knowledge Infrastructures: Intellectual Frameworks
and Research Challenges. Ann Arbor: Deep Blue.
Big Data doesn‟t
respect disciplinary
boundaries
Digital Social Research
theODI.org
The Big Picture
More people
Moremachines
Big Data
Big Compute
Conventional
Computation
“Big Social”
Social Networks
e-infrastructure
online
R&D
Big Data
Production
& Analytics
deeply
about
society
Research Councils UK and Big
Data
▶ „Big data is a term for a collection of datasets
so large and complex that it is beyond the
ability of typical database software tools to
capture, store, manage, and analyse them.
„Big‟ is not defined as being larger than a
certain number of „bytes‟ because as
technology advances over time, the size of
datasets that qualify as big data will also
increase‟ (RCUK)
Big Data Network
Research benefits of new data
▶ Undertaking research on pressing policy-related issues
without the need for new data collection
• Food consumption, social background and obesity
• Energy consumption, housing type and climatic
conditions
• Rural location, private/public transport alternatives and
incomes
• School attainment, higher education participation,
subject choices, student debt and later incomes
▶ New data such as social media enable us to ask big
questions, about big populations, and in real time – this is
transformative
http://www.theguardian.com/uk/series/reading-the-riots
E-infrastructureLeadership
NeilChueHong
Mandy Chessell
F i r s t
Interdisciplinary and “in the wild” *
* “in it” versus “on it”
Nigel Shadbolt et al
Real life is and must be full of all kinds of social
constraint – the very processes from which
society arises. Computers can help if we use
them to create abstract social machines on the
Web: processes in which the people do the
creative work and the machine does the
administration... The stage is set for an
evolutionary growth of new social engines. The
ability to create new forms of social process
would be given to the world at large, and
development would be rapid.
Berners-Lee, Weaving the Web, 1999 (pp. 172–175)
The Order of Social
Machines
SOCIAM: The Theory and Practice of Social Machines is funded by the UK Engineering and Physical Sciences Research Council
(EPSRC) under grant number EPJ017728/1 and comprises the Universities of Southampton, Oxford and Edinburgh. See sociam.org
Physical World
(people and devices)
Building a Social Machine
Design and
Composition
Participation and
Data supply
Model of social interaction
Virtual World
(Network of
social interactions)
Dave Robertson
Kevin Page
Cat De Rourehttp://botornot.net
A revolutionary idea…
Open Science!
http://rstl.royalsocietypublishing.org/
Join the W3C Community Group www.w3.org/community/rosc
Jun
Zhao
www.researchobject.org
www.force11.org
Web as
lens
Web as
artefact
Web
Observatories
http://www.w3.org/community/webobservatory/
Big data elephant versus sense-making
network?
The challenge is to foster the co-constituted socio-technical
system on the right i.e. a computationally-enabled sense-
making network of expertise, data, models, visualisations
and narratives
Iain Buchan
Pip
Willcox
@marstonbikepath
Datasets or dataflows?
Take homes
▶ There are multiple shifts in scholarship occurring:
– Volumes of data and associated automation
– Computational infrastructure and realtime analytics
– Dataflows vs datasets (and curation infrastructure)
– Correlation vs causation
– Responsible Innovation
– Machine-to-Machine and Internet of Things
▶ Social Machines provide an approach to co-
design and analysis in the evolving knowledge
infrastructure
david.deroure@oerc.ox.ac.uk
www.oerc.ox.ac.uk/people/dder
@dder
Slide and image credits: Fiona Armstrong, Christine Borgman, Iain
Buchan, Mandy Chessell, Cat De Roure, Neil Chue Hong, Dave
Robertson, Nigel Shadbolt, Pip Willcox, Jun
Zhao, Guardian, Royal Society
www.oerc.ox.ac.uk
david.deroure@oerc.ox.ac.uk
@dder

Weitere ähnliche Inhalte

Was ist angesagt?

Gateways for Open Science - XSEDE
Gateways for Open Science - XSEDEGateways for Open Science - XSEDE
Gateways for Open Science - XSEDE
Kaitlin Thaney
 
Artificial Intelligence & Machine Learning. Is it Planet Saving Tech?
Artificial Intelligence & Machine Learning. Is it Planet Saving Tech?Artificial Intelligence & Machine Learning. Is it Planet Saving Tech?
Artificial Intelligence & Machine Learning. Is it Planet Saving Tech?
Katina Michael
 

Was ist angesagt? (20)

New Data `New Computation
New Data `New ComputationNew Data `New Computation
New Data `New Computation
 
Social Machines GSS
Social Machines GSSSocial Machines GSS
Social Machines GSS
 
Humanities in the Digital World
Humanities in the Digital WorldHumanities in the Digital World
Humanities in the Digital World
 
Ethics of Automation
Ethics of AutomationEthics of Automation
Ethics of Automation
 
Emerging Forms of Data and Analytics
Emerging Forms of Data and AnalyticsEmerging Forms of Data and Analytics
Emerging Forms of Data and Analytics
 
Social Machines IIIT
Social Machines IIITSocial Machines IIIT
Social Machines IIIT
 
DEVELOPMENT OF INTERNET BY SAIKIRAN PANJALA
DEVELOPMENT OF INTERNET BY SAIKIRAN PANJALADEVELOPMENT OF INTERNET BY SAIKIRAN PANJALA
DEVELOPMENT OF INTERNET BY SAIKIRAN PANJALA
 
OKCon 2008 - Lessons from Environmental information
OKCon 2008 - Lessons from Environmental informationOKCon 2008 - Lessons from Environmental information
OKCon 2008 - Lessons from Environmental information
 
Sharing Scientific Data: Legal, Normative and Social Issues
Sharing Scientific Data: Legal, Normative and Social IssuesSharing Scientific Data: Legal, Normative and Social Issues
Sharing Scientific Data: Legal, Normative and Social Issues
 
Gateways for Open Science - XSEDE
Gateways for Open Science - XSEDEGateways for Open Science - XSEDE
Gateways for Open Science - XSEDE
 
Homelessness Data Discussion
Homelessness Data DiscussionHomelessness Data Discussion
Homelessness Data Discussion
 
Glyn Moody: from open source to open research
Glyn Moody: from open source to open researchGlyn Moody: from open source to open research
Glyn Moody: from open source to open research
 
Artificial Intelligence & Machine Learning. Is it Planet Saving Tech?
Artificial Intelligence & Machine Learning. Is it Planet Saving Tech?Artificial Intelligence & Machine Learning. Is it Planet Saving Tech?
Artificial Intelligence & Machine Learning. Is it Planet Saving Tech?
 
The changing role of the IT leader - Jisc Digital Festival 2015
The changing role of the IT leader - Jisc Digital Festival 2015The changing role of the IT leader - Jisc Digital Festival 2015
The changing role of the IT leader - Jisc Digital Festival 2015
 
Jon richter, CROWD-MAPPING ALTERNATIVES ECONOMIES
Jon richter, CROWD-MAPPING ALTERNATIVES ECONOMIESJon richter, CROWD-MAPPING ALTERNATIVES ECONOMIES
Jon richter, CROWD-MAPPING ALTERNATIVES ECONOMIES
 
The Human Intranet
The Human Intranet The Human Intranet
The Human Intranet
 
Shifting Scientific Practice - ORCID 2015
Shifting Scientific Practice - ORCID 2015Shifting Scientific Practice - ORCID 2015
Shifting Scientific Practice - ORCID 2015
 
Advances in Digital Scholarship Moot
Advances in Digital Scholarship MootAdvances in Digital Scholarship Moot
Advances in Digital Scholarship Moot
 
Removing Barriers to Data Sharing: the Research Data Alliance
Removing Barriers to Data Sharing: the Research Data AllianceRemoving Barriers to Data Sharing: the Research Data Alliance
Removing Barriers to Data Sharing: the Research Data Alliance
 
Public Libraries and Academic Libraries: Digital Partners?"
Public Libraries and Academic Libraries: Digital Partners?"Public Libraries and Academic Libraries: Digital Partners?"
Public Libraries and Academic Libraries: Digital Partners?"
 

Ähnlich wie Big Data and Social Machines

Franck Rebillard, Professeur Université Paris 3
Franck Rebillard, Professeur Université Paris 3Franck Rebillard, Professeur Université Paris 3
Franck Rebillard, Professeur Université Paris 3
SMCFrance
 
WSI Stimulus Project: Centre for longitudinal studies of online citizen parti...
WSI Stimulus Project: Centre for longitudinal studies of online citizen parti...WSI Stimulus Project: Centre for longitudinal studies of online citizen parti...
WSI Stimulus Project: Centre for longitudinal studies of online citizen parti...
Ramine Tinati
 

Ähnlich wie Big Data and Social Machines (20)

Big Data for the Social Sciences - David De Roure - Jisc Digital Festival 2014
Big Data for the Social Sciences - David De Roure - Jisc Digital Festival 2014Big Data for the Social Sciences - David De Roure - Jisc Digital Festival 2014
Big Data for the Social Sciences - David De Roure - Jisc Digital Festival 2014
 
Web Observatories and e-Research
Web Observatories and e-ResearchWeb Observatories and e-Research
Web Observatories and e-Research
 
Social Machines Paradigm
Social Machines ParadigmSocial Machines Paradigm
Social Machines Paradigm
 
New and Emerging Forms of Data
New and Emerging Forms of DataNew and Emerging Forms of Data
New and Emerging Forms of Data
 
Social Machines - A Disruptive Technology?
Social Machines - A Disruptive Technology?Social Machines - A Disruptive Technology?
Social Machines - A Disruptive Technology?
 
Taking IT for Granted
Taking IT for GrantedTaking IT for Granted
Taking IT for Granted
 
Taking IT for Granted - David De Roure
Taking IT for Granted - David De RoureTaking IT for Granted - David De Roure
Taking IT for Granted - David De Roure
 
Future of Scholarly Communications
Future of Scholarly CommunicationsFuture of Scholarly Communications
Future of Scholarly Communications
 
Franck Rebillard, Professeur Université Paris 3
Franck Rebillard, Professeur Université Paris 3Franck Rebillard, Professeur Université Paris 3
Franck Rebillard, Professeur Université Paris 3
 
Big Data meets Big Social: Social Machines and the Semantic Web
Big Data meets Big Social: Social Machines and the Semantic WebBig Data meets Big Social: Social Machines and the Semantic Web
Big Data meets Big Social: Social Machines and the Semantic Web
 
Intersection Scale and Social Machines
Intersection Scale and Social MachinesIntersection Scale and Social Machines
Intersection Scale and Social Machines
 
e-Research and the Demise of the Scholarly Article
e-Research and the Demise of the Scholarly Articlee-Research and the Demise of the Scholarly Article
e-Research and the Demise of the Scholarly Article
 
Social Machines of Scholarly Collaboration
Social Machines of Scholarly CollaborationSocial Machines of Scholarly Collaboration
Social Machines of Scholarly Collaboration
 
Scholarly Social Machines Essay
Scholarly Social Machines EssayScholarly Social Machines Essay
Scholarly Social Machines Essay
 
The wider environment of open scholarship – Jisc and CNI conference 10 July ...
The wider environment of open scholarship – Jisc and CNI conference 10 July ...The wider environment of open scholarship – Jisc and CNI conference 10 July ...
The wider environment of open scholarship – Jisc and CNI conference 10 July ...
 
2066 and all that
2066 and all that2066 and all that
2066 and all that
 
Cook et al
Cook et alCook et al
Cook et al
 
Social Machines Democratization
Social Machines DemocratizationSocial Machines Democratization
Social Machines Democratization
 
WSI Stimulus Project: Centre for longitudinal studies of online citizen parti...
WSI Stimulus Project: Centre for longitudinal studies of online citizen parti...WSI Stimulus Project: Centre for longitudinal studies of online citizen parti...
WSI Stimulus Project: Centre for longitudinal studies of online citizen parti...
 
Scholarly Social Machines
Scholarly Social MachinesScholarly Social Machines
Scholarly Social Machines
 

Mehr von David De Roure

Mehr von David De Roure (11)

Intersection Scale and Social Machines 2016
Intersection Scale and Social Machines 2016Intersection Scale and Social Machines 2016
Intersection Scale and Social Machines 2016
 
Digital Scholarship Intersection
Digital Scholarship IntersectionDigital Scholarship Intersection
Digital Scholarship Intersection
 
The Long and the Short of it: a history of Social Machines
The Long and the Short of it:a history of Social MachinesThe Long and the Short of it:a history of Social Machines
The Long and the Short of it: a history of Social Machines
 
Humanities in the Digital Age
Humanities in the Digital AgeHumanities in the Digital Age
Humanities in the Digital Age
 
Digital Scholarship Intersection Scale Social Machines
Digital Scholarship Intersection Scale Social MachinesDigital Scholarship Intersection Scale Social Machines
Digital Scholarship Intersection Scale Social Machines
 
citizens scale scholarly social machines
citizens scale scholarly social machinescitizens scale scholarly social machines
citizens scale scholarly social machines
 
Executable Music Documents
Executable Music DocumentsExecutable Music Documents
Executable Music Documents
 
Music Objects to Social Machines
Music Objects to Social MachinesMusic Objects to Social Machines
Music Objects to Social Machines
 
Post-Digital Society
Post-Digital SocietyPost-Digital Society
Post-Digital Society
 
Working out the plot: the role of Stories in Social Machines
Working out the plot: the role of Stories in Social MachinesWorking out the plot: the role of Stories in Social Machines
Working out the plot: the role of Stories in Social Machines
 
DR2013 Data Science Panel Introduction
DR2013 Data Science Panel IntroductionDR2013 Data Science Panel Introduction
DR2013 Data Science Panel Introduction
 

Kürzlich hochgeladen

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
Safe Software
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
 

Kürzlich hochgeladen (20)

MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
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
 
"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 ...
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdfGenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
 
Navi Mumbai Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Navi Mumbai Call Girls 🥰 8617370543 Service Offer VIP Hot ModelNavi Mumbai Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Navi Mumbai Call Girls 🥰 8617370543 Service Offer VIP Hot Model
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 
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
 
Apidays Singapore 2024 - Scalable LLM APIs for AI and Generative AI Applicati...
Apidays Singapore 2024 - Scalable LLM APIs for AI and Generative AI Applicati...Apidays Singapore 2024 - Scalable LLM APIs for AI and Generative AI Applicati...
Apidays Singapore 2024 - Scalable LLM APIs for AI and Generative AI Applicati...
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
AXA XL - Insurer Innovation Award Americas 2024
AXA XL - Insurer Innovation Award Americas 2024AXA XL - Insurer Innovation Award Americas 2024
AXA XL - Insurer Innovation Award Americas 2024
 
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
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 

Big Data and Social Machines

  • 1. Big Data and Social Machines David De Roure e-Research Centre, University of Oxford @dder
  • 2. Overview 1. Big Data for research (UK perspective) 2. Several shifts in scholarship 3. Social Machines 4. Towards a new knowledge infrastructure
  • 3. Edwards, P. N., et al. (2013) Knowledge Infrastructures: Intellectual Frameworks and Research Challenges. Ann Arbor: Deep Blue.
  • 4. Big Data doesn‟t respect disciplinary boundaries Digital Social Research
  • 5.
  • 7.
  • 8. The Big Picture More people Moremachines Big Data Big Compute Conventional Computation “Big Social” Social Networks e-infrastructure online R&D Big Data Production & Analytics deeply about society
  • 9. Research Councils UK and Big Data ▶ „Big data is a term for a collection of datasets so large and complex that it is beyond the ability of typical database software tools to capture, store, manage, and analyse them. „Big‟ is not defined as being larger than a certain number of „bytes‟ because as technology advances over time, the size of datasets that qualify as big data will also increase‟ (RCUK)
  • 10.
  • 12. Research benefits of new data ▶ Undertaking research on pressing policy-related issues without the need for new data collection • Food consumption, social background and obesity • Energy consumption, housing type and climatic conditions • Rural location, private/public transport alternatives and incomes • School attainment, higher education participation, subject choices, student debt and later incomes ▶ New data such as social media enable us to ask big questions, about big populations, and in real time – this is transformative
  • 14.
  • 18.
  • 19. F i r s t
  • 20. Interdisciplinary and “in the wild” * * “in it” versus “on it”
  • 22. Real life is and must be full of all kinds of social constraint – the very processes from which society arises. Computers can help if we use them to create abstract social machines on the Web: processes in which the people do the creative work and the machine does the administration... The stage is set for an evolutionary growth of new social engines. The ability to create new forms of social process would be given to the world at large, and development would be rapid. Berners-Lee, Weaving the Web, 1999 (pp. 172–175) The Order of Social Machines
  • 23. SOCIAM: The Theory and Practice of Social Machines is funded by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant number EPJ017728/1 and comprises the Universities of Southampton, Oxford and Edinburgh. See sociam.org
  • 24. Physical World (people and devices) Building a Social Machine Design and Composition Participation and Data supply Model of social interaction Virtual World (Network of social interactions) Dave Robertson
  • 27. A revolutionary idea… Open Science! http://rstl.royalsocietypublishing.org/
  • 28. Join the W3C Community Group www.w3.org/community/rosc Jun Zhao www.researchobject.org
  • 31. Big data elephant versus sense-making network? The challenge is to foster the co-constituted socio-technical system on the right i.e. a computationally-enabled sense- making network of expertise, data, models, visualisations and narratives Iain Buchan
  • 33. Take homes ▶ There are multiple shifts in scholarship occurring: – Volumes of data and associated automation – Computational infrastructure and realtime analytics – Dataflows vs datasets (and curation infrastructure) – Correlation vs causation – Responsible Innovation – Machine-to-Machine and Internet of Things ▶ Social Machines provide an approach to co- design and analysis in the evolving knowledge infrastructure
  • 34. david.deroure@oerc.ox.ac.uk www.oerc.ox.ac.uk/people/dder @dder Slide and image credits: Fiona Armstrong, Christine Borgman, Iain Buchan, Mandy Chessell, Cat De Roure, Neil Chue Hong, Dave Robertson, Nigel Shadbolt, Pip Willcox, Jun Zhao, Guardian, Royal Society

Hinweis der Redaktion

  1. EPSRC: Under ‘Big Data’ we are considering both very large and also complex data, including dynamic and heterogenous data from all the various sources including sensors, social media, industry etc.
  2. ESRC was allocated 64m and much of this is being used to set up the ESRC Big Data Network. The ESRC’s Big Data Network will support the development of a network of innovative investments which will strengthen the UK’s competitive advantage in Big Data for the social sciences. The core aim of this network is to facilitate access to different types of data and thereby stimulate innovative research and develop new methods to undertake that research. Although you should note that diagram it is only illustrative in terms of how the UKDS and ADS will work across – that is still under discussion; and only illustrative in the number of Business and Local Government Data Research.This network has been divided into three phases. In Phase 1 of the Big Data Network the ESRC has invested in the development of the Administrative Data Research Network (ADRN) which will provide access to de-identified administrative data collected by government departments for research use – focus of this meeting and all your grants.A few words about Phase 2 and 3 before we pass to Vanessa to talk about the ADRN some more. Phase 2is currently bring commissioned and will deal primarily with business data and/ or local government data. Phase 3, further details of which will be released in the last autumn / winter and will focus primarily on third sector data and social media data. It is expected that there will be opportunities for interaction across all elements of the ESRC Big Data Network and that they will all work together around the wider objectives of facilitating access to different forms of data and of ensuring maximum impact is generated from the use of that data for the mutual benefit of data owners and researchers, and through the research facilitated by the Network, benefit society and the economy more generally.
  3. Thanks to Simon Hettrick for additional input to this slide.
  4. ESRC Cities Expert Group