More Related Content Similar to sebis research profile (20) sebis research profile1. sebis Research Profile
20.7.2014, Prof. Dr. Florian Matthes
Software Engineering für betriebliche Informationssysteme (sebis)
Fakultät für Informatik
Technische Universität München
wwwmatthes.in.tum.de
2. Research background
Enterprise Architecture
Management
Communities
Collaborative Work
Digital Content
Social Software
Engineering
§ System cartography
§ EAM tool surveys
§ EAM pattern catalog
§ Capability models in
mergers & acquisitions
§ Building blocks for EAM
§ Wiki4EAM
§ Agile EAM
§ User-centered social
software
§ Authorization models in
social software
§ Introspective model-driven
development
§ Enterprise 2.0 tool surveys
§ Hybrid Wikis
§ Tag-based knowledge
organization
Technology Transfer
Projects
§ CoreMedia AG (Spinoff)
§ infoAsset AG (Spinoff)
§ Business & IT
transformation @ VW
§ EAM 2.0 @ HUK Coburg
§ KPI systems @ SFS
§ Cloud security @ Siemens
§ Strategy assessment @ FI
§ D-MOVE
more >
Sebis Research Profile © sebis 2
3. Team
Social Software Engineering
more >
Alexander
Schneider
Matheus
Hauder
Klym
Shumaiev
Thomas
Reschenhofer
Marin
Zec
Florian
Matthes
Bernhard
Waltl
Aline
Schmidt
Jian
Kong
Enterprise Architecture
Management
Alexander
Waldmann
Sebis Research Profile © sebis 3
4. Project partners since 2002
Enterprises and public administrations
Deutsche
Börse
Systems
Sebis Research Profile © sebis 4
6. Academic education
Bachelor Informatics
§ Introduction to Software
Engineering
§ Software Engineering for
Business Applications
§ Software Engineering in
Industry and Practice
Master Informatics
§ Strategic IT
Management and EAM
§ Web Application
Engineering
§ Software Architectures
§ Global Software
Engineering
§ GFSU (Startups,
Entrepreneurship)
Life-Long Learning
§ Euro CIO Professional
Programme in Business
and Enterprise
Architecture
§ EAMKON Conference
Series
§ Softwareforen Leipzig
Working Group EAM
more >
Sebis Research Profile © sebis 6
7. Prototypical
Solutions
Informatics Engineering Evaluation
Application Domain
Practical
Experience
Research approach
Information &
Communication
Technology
Informatics
Models
Application
Abstraction
Spin-Off
Sebis Research Profile © sebis
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8. Research projects and results
1. Enterprise Architecture Management
§ IT Architecture in Turbulent Times
§ Agile Enterprise Architecture Management
§ Quantitative Models in Enterprise Architecture Management
§ Federated Enterprise Architecture Model Management
§ CALM3: Complexity of Application Landscapes
§ Semantic Processing of Legal Texts for IT Compliance
2. Social Software Engineering
§ Darwin: Process Support for Collaborative Knowledge Work
§ Spreadsheets 2.0: Analysis of Complex Linked Data
§ Social Software for Complex Problem Solving
§ COLVA: Collaborative Learning Video Annotations
Sebis Research Profile © sebis 8
9. The adoption rate for new technologies keeps
accelerating.
Forbes Magazine July 7th 1997
Sebis Research Profile © sebis 9
10. Exponential growth starts inconspicuously, and humans are
not used to reasoning about non-linear processes.
Google Trends December 2013
Sebis Research Profile © sebis 10
11. An enterprises understood as an adaptive system of systems
Humans: Employees, Customers, Suppliers, Partners, Markets, Communities, …
Laws & Regulations
Enterprise
Business Capabilities
Vision, Goals, Strategy
OPTIMIZE TRANSFORM
Information Management
IM Capabilities
Goals, Strategy
OPTIMIZE TRANSFORM
Resources: Energy, Matter, Information, Technology…
Sebis Research Profile © sebis 11
12. Research projects and results
1. Enterprise Architecture Management
§ IT Architecture in Turbulent Times
§ Agile Enterprise Architecture Management
§ Quantitative Models in Enterprise Architecture Management
§ Federated Enterprise Architecture Model Management
§ CALM3: Complexity of Application Landscapes
§ Semantic Processing of Legal Texts for IT Compliance
2. Social Software Engineering
§ Darwin: Process Support for Collaborative Knowledge Work
§ Spreadsheets 2.0: Analysis of Complex Linked Data
§ Social Software for Complex Problem Solving
§ COLVA: Collaborative Learning Video Annotations
Sebis Research Profile © sebis 12
13. Motivation – Most frequent EA challenges
100,00%
90,00%
80,00%
70,00%
60,00%
50,00%
40,00%
30,00%
20,00%
10,00%
0,00%
1. Ad hoc EAM
demands
2. Unclear business
goals
3. Hard to find
experienced
enterprise architects
4. EA demands
unclear for EAM
team
5. Enterprise
environment
changes too quickly
Agree (%)
Neither (%)
Disagree (%)
n=102
Hauder, M., Roth, S., Schulz, C., Matthes, F.: Organizational Factors Influencing Enterprise Architecture Management Challenges, 21st European Conference on Information
Systems (ECIS 2013), Utrecht, Netherland, 2013.
13
Sebis Research Profile © sebis
14. Agile EA management principles
Individuals and interactions over formal processes and tools
Project managers
EA Team
Software architects
Software developers
IT Project 1 IT Project 2 IT Project 3
Top management
Business
stakeholders
Software
development
IT operations
Top management
Strategy office
Business owners
Application owners
IT operations
Purchasing
• Ensure top management
support
• Maintain a good relationship to
people form other
management areas
Sebis Research Profile © sebis
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15. Agile EA management principles
Focus on demands of top stakeholders and speak their languages
Œ
model
collect
motivate
Architecture
blueprints
Business
and IT
strategy
Business
and org.
constraints
Individual
architecture
aspects
Project managers
communicate
explain
involve
support
EA Team
get feedback
Architecture-approval
and
requirements
Architecture
changes
Software architects
Stakeholder-specific
architecture views
Metrics
Visualizations
Reports
Software developers
IT Project 1 IT Project 2 IT Project 3
Top management
Business
stakeholders
Software
development
IT operations
Top management
Strategy office
Business owners
Application owners
IT operations
Purchasing
• A single number or picture is
more helpful than 1000 reports
• Communicate, communicate,
communicate
• Avoid waste
• Benefit form existing model
management processes
Sebis Research Profile © sebis
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16. Agile EA management principles
Reflect behavior and adapt to changes
Œ
model
collect
motivate
adapt
EA Team
get feedback reflect
Ž
Architecture
blueprints
Business
and IT
strategy
Business
and org.
constraints
Individual
architecture
aspects
Project managers
communicate
explain
involve
support
Architecture-approval
and
requirements
Architecture
changes
Software architects
Stakeholder-specific
architecture views
Metrics
Visualizations
Reports
Software developers
IT Project 1 IT Project 2 IT Project 3
Top management
Business
stakeholders
Software
development
IT operations
Top management
Strategy office
Business owners
Application owners
IT operations
Purchasing
• Iterative and Incremental
(one cycle ~12 months)
• Use building blocks and
Sebis Research Profile © sebis
16
patterns
• Request 360° feedback
• Adapt models and processes
• Continuous collaboration
17. Research projects and results
1. Enterprise Architecture Management
§ IT Architecture in Turbulent Times
§ Agile Enterprise Architecture Management
§ Quantitative Models in Enterprise Architecture Management
§ Federated Enterprise Architecture Model Management
§ CALM3: Complexity of Application Landscapes
§ Semantic Processing of Legal Texts for IT Compliance
2. Social Software Engineering
§ Darwin: Process Support for Collaborative Knowledge Work
§ Spreadsheets 2.0: Analysis of Complex Linked Data
§ Social Software for Complex Problem Solving
§ COLVA: Collaborative Learning Video Annotations
Sebis Research Profile © sebis 17
18. Using quantitative models in the context of EAM
System behavior (dynamic)
con
strains
change change
con
strains
System structure (EA, static)
풕−ퟏ t =
푵푶푾
con
strains
풕+ퟏ
1. Assess the
architecture with
metrics
2. Measure
architecture
changes
3. Plan architecture
changes
4. Monitor system
performance
with KPIs
(Business & IT)
Sebis Research Profile © sebis 18
19. Metric Management Method (MMM) as Extension of
the BEAMS Conceptual Framework
Stakeholders
Goals +
Concerns
Organizational
Organizational
Organizational
context
context
Context
Implementation Guide
(Patterns & Building Blocks)
EA
Metric
VBB
Performance
Indicator
VBB VBB
IBB
EA
Metric
IBB
IBB
+ EAM Metric Catalog
Enterprise Architects Enterprise Architects Actors
Development method
Characterize situation Configure EAM function Analyze EAM
function
Adapt and evolve EAM function
Execute
EAM
function
BEAMS , EAM Pattern Catalog and EAM KPI Catalog
Sebis Research Profile © sebis 19
20. Integrated software support for quantitative models
in the domain of EAM
Best practices for EAM metrics & performance measurement
§ KPI template
§ KPI catalog
§ Method for designing a KPI system
Integrated Software Support
§ Query language for KPI definition over complex information models
§ KPI visualization (in progress)
Evaluation
§ Siemens Financial Services
§ Credit Suisse, Bayern LB, Commerzbank, CALM3
Sebis Research Profile © sebis 20
21. Research projects and results
1. Enterprise Architecture Management
§ IT Architecture in Turbulent Times
§ Agile Enterprise Architecture Management
§ Quantitative Models in Enterprise Architecture Management
§ Federated Enterprise Architecture Model Management
§ CALM3: Complexity of Application Landscapes
§ Semantic Processing of Legal Texts for IT Compliance
2. Social Software Engineering
§ Darwin: Process Support for Collaborative Knowledge Work
§ Spreadsheets 2.0: Analysis of Complex Linked Data
§ Social Software for Complex Problem Solving
§ COLVA: Collaborative Learning Video Annotations
Sebis Research Profile © sebis 21
22. What are current problems in EA model maintenance?
N=125, 2013
Challenge n % of all
Huge data collection effort
77 55.00%
Low EA model data quality 77 55.00%
Insufficient tool support 48 34.29%
No management support 44 33.43%
Low return on investment 36 25.71%
Other 32 22.86%
No specific challenge 10 7.14%
Type of collection n % of all
Manually from applications/databases 95 76.00%
Manually via interviews 85 68.00%
Manually modeled in workshops 66 52.80%
Manually via questionnaires 46 36.80%
Partially collected automatically 44 35.20%
More >
Sebis Research Profile © sebis 22
23. Federated enterprise architecture model management
Modeling communities, artifacts, processes and their interactions
Enterprise
E
EAM
Metamodel and
Model
D
Task
fit
Technology
Team
Metamodel Mappings Instance Mappings Modeling Community Modeling Experts
PPM
Metamodel and
Model
A
Task
fit
Technology
model and meta-model
changes to be
integrated
Team
publish model
changes
Federated EA Model Management
• Importing
• Differencing
• Conflict detection
• Conflict resolution
• Collaboration
• Negotiation
BPM
Metamodel and
Model
B
Task
fit
Technology
Team
publish model
changes
ITSM
Metamodel and
Model
C
Task
fit
publish model
changes
Technology
Team
publish model
changes
Sebis Research Profile © sebis 23
24. Federated enterprise architecture model management
Tool support - ModelGlue
1. Import of different models in a metamodel-based
EA tool
2. Synchronization via model merging
Provide means to identify model elements within the
originating information source
3. Conflict detection during merge operation
§ Instance conflicts
§ Schema conflicts
§ Schema/instance conflicts
4. Collaborative conflict resolution
Fine-grained access control is employed to find the
organizational role in a chain of responsibility
5. Customizable conflict resolution strategy
For further information see https://wwwmatthes.in.tum.de/pages/kkdtsjtjkc2g
Sebis Research Profile © sebis 24
25. Research projects and results
1. Enterprise Architecture Management
§ IT Architecture in Turbulent Times
§ Agile Enterprise Architecture Management
§ Quantitative Models in Enterprise Architecture Management
§ Federated Enterprise Architecture Model Management
§ CALM3: Complexity of Application Landscapes
§ Semantic Processing of Legal Texts for IT Compliance
2. Social Software Engineering
§ Darwin: Process Support for Collaborative Knowledge Work
§ Spreadsheets 2.0: Analysis of Complex Linked Data
§ Social Software for Complex Problem Solving
§ COLVA: Collaborative Learning Video Annotations
Sebis Research Profile © sebis 25
26. CALM3: Complexity of application landscapes
Models, metrics and methods
Research questions
§ What does "IT-complexity“ mean?
§ How can complexity be described?
§ Which factors drive application landscape complexity?
§ How can complexity be quantified?
§ How can complexity models contribute to landscape
planning?
Project Partners
10 Industry
experts
CALM3
Workshop
Series
Quarterly
meetings
Extensive EA
data
Concrete
metrics
Visionary
discussions
Tool
development
Sebis Research Profile © sebis 26
28. The complexity cube
Classifying EA literature
EA Complexity
Publications
ACN D1 ACN D2 ACN D3 ACN D4
Janssen et al. (2006) qualitative structural, dynamic objective ordered
Buckl et al. (2009) qualitative structural objective ordered
Saat et al. (2009) qualitative structural, dynamic objective ordered
Dern et al. (2009) quantitative structural objective disordered
Mocker (2009) quantitative structural objective disordered
Zadeh et al. (2012) qualitative, quantitative structural objective ordered
Kandjani et al. (2012) quantitative structural objective ordered
Kandjani et al. (2013) qualitative, quantitative dynamic objective ordered
Schütz et al. (2013) quantitative structural objective disordered
Lagerström et al. (2013) quantitative structural objective disordered
Trend: qualitative à quantitative
Underrepresented: dynamic, subjective
Sebis Research Profile © sebis 28
29. Classification of applications
Visualizing the Hidden Structure of Application Landscapes
§ Calculation base: AL topology (applications, information flows)
§ Calculation: transitive dependencies of each application
Classification
§ Largest cyclic group à Core
§ More outgoing dependencies à Control
§ More incoming dependencies à Shared
§ Less incoming dependencies à Periphery
Propagation cost
§ Part of the AL affected by change
§ Sum of dependencies / applications2
2
1
3 4
5
8 9
Control
Core
Shared
Periphery
Lagerstrom, Robert, Carliss Y. Baldwin, Alan MacCormack, and Stephan Aier. "Visualizing and Measuring Enterprise Application
Architecture: An Exploratory Telecom Case." Harvard Business School Working Paper, No. 13-103, June 2013.
7
6
Sebis Research Profile © sebis 29
30. EA complexity metric based on heterogeneity
Complexity of Enterprise Architectures
§ Elements (amount & heterogeneity)
§ Relationships (amount & heterogeneity)
Calculation of heterogeneity
§ Shannon entropy
§ No effect of proportional changes
§ Significant impact of small changes
Example
§ Heterogeneity of database systems
1
0,8
0,6
0,4
0,2
0
Oracle DB2 SQL Server MySQL
EM = 0.7
EMA = 2
N = 4
Schütz, A.; Widjaja, T.; Kaiser, J. (2013). Complexity in Enterprise Architectures - Conceptualization and Introduction of a Measure from a
System Theoretic Perspective. European Conference on Information Systems (ECIS); Utrecht, Netherlands.
Sebis Research Profile © sebis 30
31. Data collection
§ 6 companies (Financial services and Automotive)
§ More than 20 metrics found
Metrics on Application level
§ Number of Business Functions (3/6)
§ Number of Infrastructure Components (4/6)
Metrics on Domain level
§ Number of Applications (4/6)
§ Number of Information Flows (6/6)
§ Standard conformity (4/6)
§ Number of Function Points (3/6)
§ Functional redundancy (6/6)
Application
Domain
Reoccurring AL complexity metrics in practice
Application
Application
Sebis Research Profile © sebis 31
32. Research projects and results
1. Enterprise Architecture Management
§ IT Architecture in Turbulent Times
§ Agile Enterprise Architecture Management
§ Quantitative Models in Enterprise Architecture Management
§ Federated Enterprise Architecture Model Management
§ CALM3: Complexity of Application Landscapes
§ Semantic Processing of Legal Texts for IT Compliance
2. Social Software Engineering
§ Darwin: Process Support for Collaborative Knowledge Work
§ Spreadsheets 2.0: Analysis of Complex Linked Data
§ Social Software for Complex Problem Solving
§ COLVA: Collaborative Learning Video Annotations
Sebis Research Profile © sebis 32
33. Semantic processing of legal texts for IT compliance
1. Interpreting legal texts is non-trivial
§ > 6000 laws and regulations in Germany
§ Words and expression are hard to understand
§ Uncertain, abstract, indeterminate legal terms
§ adequate, effective, appropriate etc.
§ International agreements and regulations
2. Compliance is desirable but expensive
3. Information systems can support compliance during the
§ creation,
§ exploration,
§ search,
§ interpretation and
§ visualization processes.
Basel II / III
Sarbanes-
Oxley Act
REACH
Sebis Research Profile © sebis 33
34. Semantic processing of legal texts for IT compliance
Company
Employees Assets Tasks Objectives
Requirements
Engineering
IT Requirements
(Business IT Alignment)
IT Systems
COBIT TOGAF
Controlling
Support through IS Compliance
Requirements
(Legal Obligations)
searching, exploration,
interpretation,
change tracking etc.
§
Information-systems
LexInform, Juris,
RIS, …
Laws
KWG, TMG,
BDSG, …
Authorities
(e.g. BaFin)
Sebis Research Profile © sebis 34
35. Semantic processing of legal texts for IT compliance
Compliance
Requirements Controlling
(Legal Obligations)
searching, exploration,
interpretation, change tracking etc.
§
§44 IT-examination, auditing,
(internal/external) revision, etc.
Information-systems
LexInform, Juris,
RIS, …
Laws/
Regulations
KWG, TMG,
BDSG, …
Authorities
(e.g. BaFin)
1. Information Retrieval (IR)
§ Searching, finding and exploring of information in unstructured documents
§ Meet the demand of information
2. Artificial Intelligence (AI)
§ Automatically derive new information / knowledge
§ Answer questions:
§ How has process XY be implemented in order to be compliant?
à NO automation but decision-support
Sebis Research Profile © sebis 35
36. Research projects and results
1. Enterprise Architecture Management
§ IT Architecture in Turbulent Times
§ Agile Enterprise Architecture Management
§ Quantitative Models in Enterprise Architecture Management
§ Federated Enterprise Architecture Model Management
§ CALM3: Complexity of Application Landscapes
§ Semantic Processing of Legal Texts for IT Compliance
2. Social Software Engineering
§ Darwin: Process Support for Collaborative Knowledge Work
§ Spreadsheets 2.0: Analysis of Complex Linked Data
§ Social Software for Complex Problem Solving
§ COLVA: Collaborative Learning Video Annotations
Sebis Research Profile © sebis 36
37. Collaborative knowledge work is ubiquitous in
organizations
Solving complex problems in
Development of large
software systems
communities
Producing new ideas and
innovations
How can software support processes for collaborative knowledge work?
Sebis Research Profile © sebis 37
38. Theoretical basis of the research project involves
three different disciplines
Knowledge Work
Literature on knowledge work in
organizations provides an
understanding of the problem.
Description of the problem:
• Characteristics of knowledge
work
• Complex vs. Complicated
problems
• Roles in knowledge work
Adaptive Case
Management
Adaptive case management is a
novel approach to support
knowledge-intensive processes.
Solution ideas from ACM:
• Essential requirements for ACM
support
• Emergent design of processes
• Evolution of processes with
templates
Social Principles and
Patterns
Knowledge work relies on the
successful collaboration of
different roles.
Facilitating collaboration:
• Building successful online
communities
• Learning from existing
communities on the web
• Principles and patterns
Sebis Research Profile © sebis 38
39. Solution: Empowering users to collaboratively
structure knowledge-intensive processes
Goal Orientation
• Describe which goals should be achieved
• Goals guide the stream of work
• Replaces traditional process model
Emergence
• Empowerment and participation of end users
• Adaptability of templates at run-time
• Continuous improvement of templates
Data Centricity
• Data as driver for knowledge work
• Goal-oriented transformation of data
• Integration of processes and data
Collaboration
• Knowledge creation through interaction
• Building a successful online community
Case Templates
• Sharing and preservation of knowledge
• Access to recurring best practice patterns
Logical and temporal dependencies with CMMN
Create a new task for „Neue Idee“
Adding a new task
Drag and drop of attributes on tasks
Attribute types
Hide completed tasks
Access rights on attributes
Completed tasks
Unstructured information
In-place editing
New attribute for the template
2. LITERATURE REVIEW
4. CASE STUDIES
5. EVALUATION
Sebis Research Profile © sebis 39
Design Principles
§ Flexible stage-gate process for Innovation
Management
§ Development of a future Enterprise
Architecture state
§ Artefact-oriented Requirements Engineering
processes with templates
Case Studies
Analysis of related work
and identification of
research questions for
three domains.
!
!
!
Evaluation 1
Evaluation 2
Evaluation 3
Prototype for collaborative
structuring of knowledge-intensive
processes.
1. RESSCOPE EARCH
Derivation of requirements
for an Adaptive Case
Management solution.
3. PROTOTYPE
Case studies to support
processes for all three
investigated domains.
Qualitative evaluation of the
three case studies with
expert interviews.
Deliverable: Transcript of
expert interviews
Deliverable: Implemented
prototype
Deliverable: Research
questions
Deliverable: Requirements
for Adaptive Case
Management
Deliverable: Prototype
applied in three sample
domains
?
?
?
EA Management
Innovation Management
Requirements Engineering
40. Research projects and results
1. Enterprise Architecture Management
§ IT Architecture in Turbulent Times
§ Agile Enterprise Architecture Management
§ Quantitative Models in Enterprise Architecture Management
§ Federated Enterprise Architecture Model Management
§ CALM3: Complexity of Application Landscapes
§ Semantic Processing of Legal Texts for IT Compliance
2. Social Software Engineering
§ Darwin: Process Support for Collaborative Knowledge Work
§ Spreadsheets 2.0: Analysis of Complex Linked Data
§ Social Software for Complex Problem Solving
§ COLVA: Collaborative Learning Video Annotations
Sebis Research Profile © sebis 40
41. Spreadsheets 2.0
Motivation
Business users love spreadsheets
§ Declarative and interactive paradigm to capture functional dependencies
§ Modeling, analysis, simulation, visualization
§ Empowerment of business-users
§ Emergent structures (data, logic)
Limitations of spreadsheets
§ Collaborative work
§ Complex linked data
social networks, logistic networks, IT architectures, product models, multi-project plans
§ Software Engineering Qualities
modularity, reusability, typing, binding, naming
Sebis Research Profile © sebis 41
42. Spreadsheets 2.0: Analysis of complex linked data
Hierarchical data structures Networks
Bank
Geschäft
IT
Unternehmens
-steuerung
Handel
Kredit
Andere Produkte
Prozesse
Anwendungen
Infrastruktur
Support
Accounting
Controlling
Reporting
Compliance
For more information visit Spreadsheet 2.0 (http://wwwmatthes.in.tum.de)
Sebis Research Profile © sebis 42
43. Spreadsheets 2.0: Analysis of complex linked data
푓
푓
푓
푓
푓
푓
푓
푓
푓
푓
푓
Data Functions / Transformations Visualizations
Users
For more information visit Spreadsheet 2.0 (http://wwwmatthes.in.tum.de)
Sebis Research Profile © sebis 43
44. Spreadsheets 2.0: Analysis of complex linked data
System vision
§ Hybrid Wiki data model
§ Transparency through pipes & filters architecture
§ Functional query language (à la LINQ, Scala, …)
§ Intuitive interactive web-based user experience
§ Fully integrated in collaboration environment
§ Optimized „real time“ evaluation
Research questions
§ User interface concepts and design (data, functions, views)?
§ How do users work with historic data and time series?
§ Language design (DSL, familiarity ó expressiveness)?
§ System architecture and integration with emerging “big data” technologies?
§ Evaluation strategies?
§ Optimization strategies (materialized views, …)?
For more information visit Spreadsheet 2.0 (http://wwwmatthes.in.tum.de)
Sebis Research Profile © sebis 44
45. Research projects and results
1. Enterprise Architecture Management
§ IT Architecture in Turbulent Times
§ Agile Enterprise Architecture Management
§ Quantitative Models in Enterprise Architecture Management
§ Federated Enterprise Architecture Model Management
§ CALM3: Complexity of Application Landscapes
§ Semantic Processing of Legal Texts for IT Compliance
2. Social Software Engineering
§ Darwin: Process Support for Collaborative Knowledge Work
§ Spreadsheets 2.0: Analysis of Complex Linked Data
§ Social Software for Complex Problem Solving
§ COLVA: Collaborative Learning Video Annotations
Sebis Research Profile © sebis 45
46. Information systems for problem solving
Reproductive Thinking
(Heuristics, Algorithms etc.)
Puzzle
Productive Thinking
(Creativity etc.)
Problem Wicked Problem
Problem
Example
Information
System
Support
Degree of
Collaboration
Degree of
Automation
Measuring temperature,
…
Business Model
Generation, …
Sensors, Embedded
Systems, Robotics,
Databases, …
Accounting, …
SAP R/3, Word
Processing,
Spreadsheet
Software, …
Collaborative
Informationsystems,
e.g. Wikis, Dropbox, …
Sebis Research Profile © sebis 46
47. IS support for a complex problem:
Business model generation
• Re-use benefits of existing tools and methods
• Business Model Canvas
• Common terminology
• Visual representation
• Computer-Aided Morphological Analysis
• Basic problem solving process structure
• Interactive model of the problem/solution space
• Clustering of similar business models
• Multi-user support
• Group facilitation support
• Alternate between individual and collaborative phases
è avoid social bias
• Alternate between convergent and divergent phases
è promote creativity
• Alternate between anonymous and identified interactions
è avoid social loafing, increase (constructive)
social competition
Work-in-progress: currently implementing prototype, designing process model
Sebis Research Profile © sebis 47
48. Research projects and results
1. Enterprise Architecture Management
§ IT Architecture in Turbulent Times
§ Agile Enterprise Architecture Management
§ Quantitative Models in Enterprise Architecture Management
§ Federated Enterprise Architecture Model Management
§ CALM3: Complexity of Application Landscapes
§ Semantic Processing of Legal Texts for IT Compliance
2. Social Software Engineering
§ Darwin: Process Support for Collaborative Knowledge Work
§ Spreadsheets 2.0: Analysis of Complex Linked Data
§ Social Software for Complex Problem Solving
§ COLVA: Collaborative Learning Video Annotations
Sebis Research Profile © sebis 48
49. Colva: Collaborative learning video annotations
Motivation
§ Increasing amount of online learning / lecture / teaching / demonstration /
knowledge / … videos
§ New players: universities, schools, individuals, non-profit organizations,
businesses, media companies, …
§ It is difficult for learners and educators to discover new relevant material for a
given topic
§ It is difficult for learners to find the exact location where a particular topic has
been covered
§ Increase quality of the learners feedback on the education material and way
of teaching
Research questions
§ What are the inhibitors of the collaborative learning video annotations?
§ How the tool for collaborative learning video annotations effects the behavior of
instructors and learners?
Sebis Research Profile © sebis 49
50. Colva: Collaborative learning video annotations
A conceptual framework for describing augmented teaching sessions
Phases
Preparation Live teaching
session Post-processing
Actors
Instructor
Learner
Plan timing of
teaching session
Prepare teaching
material.
Present teaching
material
[Take or review
notes.]
Activity
Plan timing of teaching session.
(
verb) (nouns)
activity content involved in the activity
[Take or review notes.]
(brackets)
optional activities
Sebis Research Profile © sebis 50
51. Colva: A collaborative learning video annotations
Possible synchronous and asynchronous collaboration via video annotations
Phases
Preparation Live teaching
session
Post-processing
Actors
Instructor
- View annotation.
View and create
annotation.
Learner
-
Create and view
annotation.
Create and view
annotation.
Sebis Research Profile © sebis 51
52. Colva: Collaborative learning video annotations
on the web
Implementation stages
Stage 1
Stage 2
Stage 3
Provide a web solution for
collecting learners
annotations during the
learning session
Synchronize video-recordings
with collected
real-time user annotations
Test and evaluate different
methods for collaboration
through video annotations
usage
Pilot project
Current objective
Implement concept in viable prototype
For more information contact Klym Shumaiev klym.shumaiev@tum.de
Sebis Research Profile © sebis
52
“Wouldn’t it be nice, if you
as a Bachelor student at the faculty of informatics at TU Munich
could easily create and manage
collaborative annotations aligned with video recordings of the lectures?”
Who?
How?
What?
53. Thank you for your attention. Questions?
Technische Universität München
Department of Informatics
Chair of Software Engineering for
Business Information Systems
Boltzmannstraße 3
85748 Garching bei München
Tel +49.89.289.
Fax +49.89.289.17136
wwwmatthes.in.tum.de
Florian Matthes
Prof.Dr.rer.nat.
17132
matthes@in.tum.de