Upload
michael-derntl
View
386
Download
0
Embed Size (px)
DESCRIPTION
Keynote presentation at XVI International Symposium on Computers in Education (SIIE 2014), November 12, 2014, Logroño, Spain
Citation preview
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
1 These slides are licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
Analytics Infrastructures for
Scientific Communities
Michael Derntl
RWTH Aachen University
Advanced Community Information Systems (ACIS)
XVI International Symposium on Computers in Education (SIIE 2014)
November 12, 2014
Logroño, Spain
Parts of the work reported in this presentation have been funded with support from the European
Commission. This presentation reflects the views only of the presenter, and the Commission cannot
be held responsible for any use which may be made of the information contained therein.
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
2
Scientific Communities
Scientific results socially created in scientific
communities
Quality of products success of community
Stakeholder interest in success factors
(Kornfeld & Hewitt 1981)
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
3
Classic Success Indicators
Scholarly Publications,
Citations, Impact Factors,
Rankings, etc.
→ Established communities
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
4
New Publication Channels
© 2012 Intel – Source: http://www.intel.com/content/www/us/en/communications/internet-minute-infographic.html
Web 2.0, social media/networks, etc.
Scattered information and large data volumes – big data
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
5
Big Data – The 4 V’s
© 2013 IBM – Source: http://www.ibmbigdatahub.com/infographic/four-vs-big-data
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
6
Community IS
Community information systems (CIS) provide environments / infrastructures that support community needs, structures and processes
(Some) Challenges
– Short, disruptive innovation cycles
– Scaling of designs and technology
– Long tail issues: Segmentation, diversification, heterogeneity…
– Vendors seeking lock-in situations
– Sustainability, business models
– Privacy and security
– Increasing collaboration needs
– …
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
7
Responsive Open
Community Information
Systems
Community Visualization
and Simulation
Community Analytics
Community Support
Web
An
alytics
Web
En
gin
eeri
ng
Advanced Community Information
Systems (ACIS)
Requirements
Engineering
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
8
CIS Aspects in this Talk
CIS Infrastructure
Analytics
Plug & Play
OpennessScaling
Real-Time
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
9
Community Analytics
Discovery and communication of insights in data
Weapons
– Social network analysis, Text mining, Topic modeling,
Pattern mining, Deep learning, Visual analytics, …
Infrastructure & CIS
– Analytics as a service
– Visual interaction
– Open APIs
Source: quebit.com
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
10
Example: TEL-Map
Future gazing and roadmapping
FP7 Support Action 2011-2013
Methods
– Weak signal analysis
– Trend analysis, Forecasting
– Information visualization
Data sources
– Publications
– Blogosphere
– R&D Projects
http://telmap.org
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
11
Data Processing Layers
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
12
Collaborative Project
Networks
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
13
We invested
hundreds of millions
of Euros! Tell us
what happened with
that money!
How about some
roadmaps, too? And
we need a web portal!
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
14
Collaborative Projects –
Network Analysis
Collaborative projects are key in the R&D value
chain
Stakeholders have an interest in the collaboration
structures & dynamics
Existing work on collaboration networks in FP1-6
– Complex scale-free networks; small diameter, high
clustering
– “Oligarchic core” of organizations
(Barber et al 2006; Roediger-Schluga & Barber 2008; Frachisse et al 2008, Breschi & Cusamano 2004; Lozano et al 2007;
Scherngell & Barber 2009; Roediger-Schluga & Dachs 2006; Voigt et al 2011; Derntl & Klamma 2012)
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
15
0
100
200
300
400
500
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
EC funding (million €)
TEL Projects Data Set
eTEN (39) – eLearning
FP6 (32) – TEL
FP7 (52) – TEL
eContentplus (19) – Educ.
147 projects
1020 organizations
PSP (5) – TEL Status: Oct 2014
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
16
Projects as Social Networks
Projects × Organizations
Project consortium progression
– Nodes: Projects
– Edges: Overlap of consortia (directed, weighted)
Organizational collaboration
– Nodes: Organizations
– Edges: Collaboration in multiple projects (undirected, weighted)
ROLE
TEL-Map
IMC, RWTH,
OU, ZSI
The Open
University
KU
Leuven
STELLAR, EUROGENE,
ROLE, PROLEARN,
iCOPER, ASPECT
(Frachisse et al 2008)
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
17
Consortium
Progression
Overlap ≥ 2
Time diff ≥ 3 months
Nodes 106
Edges 373
Node size proportional
to weighted degree
Node color represents
cluster (Blondel et al 2008)
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
18
Project Impact on the Landscape
Measure impact of project consortium members on sustaining and shaping the social project ties after the project start, relative to opportunity.
𝑆𝑝𝑡,𝑘
projects starting t time units after p and having at least k
partners overlap with p
𝐷𝑝𝑡 all potential successor projects of p after t time units
𝐶𝑝 consortium members of p
Successor projects
relative to opportunity
Cumulative fraction
of successor projects
filled up with p's
members
𝛿𝑝 =𝑆𝑝𝑡,𝑘
𝐷𝑝𝑡∙
𝑞∈𝑆𝑝𝑡,𝑘
𝐶𝑝 ∩ 𝐶𝑞
𝐶𝑝Impact
(Derntl & Klamma 2012)
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
19
Top Projects by Impact
Mainly large
projects
(networks like
NoEs, BPNs,
Pilots; and IPs)
All programs
(FP6, FP7,
eTEN, PSP,
eC+)
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
20
Project Watchlist
Impact correlates positively with – Funding, Consortium size, Betweenness centrality, (Weighted)
in-degree (by size)
Future gazing
Funding m€ ▼wdin(C) wdin/C
OpenDiscoverySpace (2012-15; PSP) 7.7 74 (51) 1.45
Inspiring Science (2013-16; PSP) 4.9 56 (29) 1.93
GALA (2010-14; FP7) 5.7 55 (31) 1.77
WESPOT (2012-15; FP7) 2.9 40 (9) 4.44
GO-LAB (2012-16; FP7) 9.7 38 (19) 2.00
LACE (2014-16; FP7) 1.3 27 (9) 3.00
ITEC (2010-14; FP7) 9.5 22 (27) 0.81
Filter: (Running OR Ended in 2014) AND wdin > 20
(Derntl & Klamma 2012)
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
21
Organizational Collaboration
Collaboration is the fertile soil for R&D output in CPs
Follow-up proposals / projects
Shapes the research agenda
Graph:
– Edge between O1 and O2 if both participated in at least k
projects
– Weight: number of projects
– Direction: none
– Nodes: organizations
The Open
University
KU
Leuven
STELLAR, EUROGENE,
ROLE, PROLEARN,
iCOPER, …
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
22
Edge only if at least two
common projects
Organizational Collaboration
Network
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
23
Organizational Collaboration –
“Oligarchic Core”
Filter: degree ≥ 20
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
24
Fine, but…
How about those
NSF projects?
How about those
ARC projects down
under?
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
25
Projects Space at
Learning Frontiers Portal
http://learningfrontiers.eu/?q=project_space
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
26
Projects Space – Project Details
http://learningfrontiers.eu/?q=tel_project/TEL-MAP
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
27
… DB
Project Aspects
(Confolio)
Portal Architecture
Projects DB
Discourse DB
Project Space Module
Query
Service
… DBDashboard
Learning Frontiers Portal
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
28
Dashboard
http://learningfrontiers.eu/?q=dashboard
(Derntl, Erdtmann, Klamma 2012)
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
29
Embedding in Host Application
Application DataDashboard Container
Dashboard
Visualization
Widget
Application Server
User Data
Widget Data
Data Sources
Database(s)
User
Management
Dashboard
Service
Query
Service
1a
1b3
6b
4a
6a
4b
5c5b
2
5a
Visualization Layer Application Layer Data Layer
1
2
3
4
5
6
Register user (on first visit; automatically done by the embedding application)
Hand over user credentials to the dashboard container
Dashboard container log in user on Application Server
Retrieve list of available visualization widgets
Display visualization in widget
Store user preferences
Component of the
embedding application
Component of the
dashboard framework
Legend
(Derntl, Erdtmann, Klamma 2012)
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
30
Thematic Analysis
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
31
Text Analysis
Extracting meaning from a (text) data corpus with
machine learning techniques
Building statistical models of text documents
– e.g. Latent Semantic Analysis (pLSA), Latent Dirichlet
Allocation (LDA), n-grams, etc.
Goal: Present results so community can explore
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
32
Topic Modeling (LDA)
Image Source: Blei 2012
Generative model: documents are generated by picking words from topics
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
33
Topic Model Visualizations
Goal: facilitate user in interpretation and reasoning
based on LDA results
Adopt paradigm of Visual Analytics:
– “Turning information overload into an opportunity”
– Integrate human judgment by means of
– visual representations and
– interaction techniques
in the analysis process
(Keim et al 2008)
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
34 Source: http://www.princeton.edu/~achaney/tmve/wiki100k/docs/Spain.html
Example Visualization:
Wikipedia Topics
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
35
Topic Dynamics
Assumption: topics and words evolve over time
Various ways of visualization and user interaction
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
36
D-VITA: Dynamic Visual Topic
Analytics
http://is.gd/DVITA
(Derntl et al 2014c; Günnemann et al 2013)
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
37
Fine, but…
How have the themes at
ER conference evolved in
the last 30 years?
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
38
System Architecture
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
39
Topic Model Builder
Backend components for configurable toolchains from raw data to built topic model
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
40
Architectures for Scaling
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
41
Maturing
Interacting with People at the
workplacePaul discovers a problem at the
construction site with PLC equipment ...
Generating dynamic Learning
MaterialThe regional training center observes the
Q&A and links it to their course material
...
Q: How to use PLC equipment …?
• I have seen this before here …
• Last time I did it, I …
• Here is something helpful
Social Semantic Layer
Emerging shared meaning,
giving contextEnergy Consumption
Lightning
X3-PVQX3-PJC
X3-POZ PLC EquipmentInstructional Taxonomy
• What is …
• How to …
• Example of …
Tutorial: How to Use PLC
What is PLC
How to use it?
Examples
Further Information
Hot Questions and
Answers
Work Practice Taxonomy
• Installation
• Testing
• Operation
Peter
Paul
Mary
Interacting in the Physical
WorkplacePhysical workplace is equipped with QR
tags, learning materials are delivered just
in time ...
A list of helpful resources
• Tutorials: How to use …
• Persons: Peter, Mary, …
• Work Practice: Installation,..
• Concepts: PLC, Lightning
• Q&A: …,
Learning Layers in the
Construction Industry
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
42
Scaling Informal Learning
Communities
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
43
Architecture –
The “Layers Box”
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
44
Dropping the Box
A
B
C
D
E
LAPPSLayers App Store
(Derntl et al 2014b)
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
45
METIS: Scaling Co-Design in Teacher
Communities
Goal: Platform to integrate and support the
complete learning design life cycle
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
46
Integrated Learning
Design Environment
Community-based instances; Web
GUI
RESTful APIs for integrating
external tools
Sharing and reuse tracing
features
Used in different educational
contexts
Currently used in Hands-On ICT
MOOC “Design Studio for ICT
based learning activities”
http://ilde.upf.edu(Hernández-Leo et al. 2013, 2014)
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
47
Wrap Up
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
48
Summary
Challenges
– Perpetual change, Tight competition, Big data, Versatile
computing methods
– Need to support community processes, assessing,
reflecting, forecasting, roadmaping community support
infrastructures
Key infrastructure-level aspects of solutions
– State of the art analysis methods, visual analytics
– Embeddable components, scalable architecture
– Openness of processes, software, data
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
49
Thank you!
CIS Infrastructure
Analytics
Plug & Play
OpennessScaling
Real-Time
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
50
References
Barber, M., Krueger, A., Krueger, T., Roediger-Schluga, T.: Network of European Union–funded collaborative research and development projects. Physical Review E 73
(2006)
Blei D.M., Lafferty J.D. 2006. Dynamic topic models. In Proceedings of the 23rd International Conference on Machine Learning, ed.WCohen, A Moore, pp. 113–20. New
York: Assoc. Comput. Mach.
Blei D.M., Ng A.Y., Jordan M.I..(2003) Latent Dirichlet allocation. J. Mach. Learn. Res. 3:993–1022
Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4): 77-84(2012)
Blondel, V. D., Guillaume, J., Lambiotte, R., Lefevre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008 (10)
Breschi, S., Cusmano, L.: Unveiling the texture of a European Research Area: emergence of oligarchic networks under EU Framework Programmes. International Journal of
Technology Management 27(8), 747–772 (2004)
Derntl, M., Klamma, R. (eds.), Hannemann, A., Koren, I., Nicolaescu, P., Renzel, D., Kravcik, M., Shahriari, M., Purma, J., Bachl, M., Bellamy, E., Elferink, R., Tomberg, V.,
Theiler, D., Santos, P. (2014). Customizable Architecture for Flexible Small-Scale Deployment. Learning Layers Deliverable D6.2, October 2014 (2014b).
Derntl, M., Koren, I., Nicolaescu, P., Renzel, D., Klamma, R. (2014a). Blueprint for Software Engineering in Technology Enhanced Learning Projects. In C. Rensing, S. de
Freitas, T. Ley, P. J. Muñoz Merino (Eds.), Open Learning and Teaching in Educational Communities – 9th European Conference on Technology Enhanced Learning, EC-
TEL 2014, Graz, Austria, September 16-19, 2014, Proceedings. Lecture Notes in Computer Science, vol. 8719 (pp. 404-409). (2014a)
Derntl, M., Klamma, R.: The European TEL Projects Community from a Social Network Analysis Perspective. EC-TEL 2012: 51-64
Derntl, M., Erdtmann, S., Klamma, R. (2012). An Embeddable Dashboard for Widget-Based Visual Analytics on Scientific Communities. Proceedings of 12th International
Conference on Knowledge Management and Knowledge Technologies, I-KNOW '12, Article No 23. ACM Press.
Derntl, M., Günnemann, N., Tillmann, A., Klamma, R., Jarke, M. (2014c). Building and Exploring Dynamic Topic Models on the Web. In Proceedings of ACM CIKM 2014,
Shanghai, China. ACM Press.
Frachisse, D., Billand, P., Massard, N.: The Sixth Framework Program as an Affiliation Network: Representation and Analysis (2008), http://ssrn.com/abstract=1117966
Keim, D. A., Mansmann, F, Schneidewind, J, Thomas, J., Ziegler, H.: Visual Analytics: Scope and Challenges, pages 76–90.Springer LNCS, 2008
Kornfeld, W. A., Hewitt, C.: The Scientic Community Metaphor. IEEE Trans. Syst., Man, and Cybern., SMC-11(1):24-33, 1981
Lozano, S., Duch, J., Arenas, A.: Analysis of large social datasets by community detection. The European Physical Journal Special Topics 143(1), 257–259 (2007)
Roediger-Schluga, T., Barber, M.J.: R&D collaboration networks in the European Framework Programmes: data processing, network construction and selected results.
International Journal of Foresight and Innovation Policy 4(3/4), 321–347 (2008)
Roediger-Schluga, T., Dachs, B.: Does technology affect network structure? – A quantitative analysis of collaborative research projects in two specific EU programmes.
UNU-MERIT Working Paper Series 041 (2006)
Scherngell, T., Barber, M.J.: Spatial interaction modelling of cross-region R&D collaborations: empirical evidence from the 5th EU framework programme. Papers in Regional
Science 88(3), 531–546 (2009)
Thomas, J., Cook, K.A. (eds.) Illuminating the Path: The Research and Development Agenda for Visual Analytics. IEEE, 2005.
Voigt, C. (ed.): Deliverable D7.5, STELLAR Nework of Excellence (2011)