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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
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
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
Project partners since 2002 
Enterprises and public administrations 
Deutsche 
Börse 
Systems 
Sebis Research Profile © sebis 4
Project partners since 2002 
Consultants and software vendors 
Sebis Research Profile © sebis 5
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
Prototypical 
Solutions 
Informatics Engineering Evaluation 
Application Domain 
Practical 
Experience 
Research approach 
Information & 
Communication 
Technology 
Informatics 
Models 
Application 
Abstraction 
Spin-Off 
Sebis Research Profile © sebis 
7
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
The adoption rate for new technologies keeps 
accelerating. 
Forbes Magazine July 7th 1997 
Sebis Research Profile © sebis 9
Exponential growth starts inconspicuously, and humans are 
not used to reasoning about non-linear processes. 
Google Trends December 2013 
Sebis Research Profile © sebis 10
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
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
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
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 
14
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 
15
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
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
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
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
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
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
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
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
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
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
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
The complexity cube 
Sebis Research Profile © sebis 27
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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?
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

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sebis research profile

  • 1. 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
  • 5. Project partners since 2002 Consultants and software vendors Sebis Research Profile © sebis 5
  • 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 7
  • 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 14
  • 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 15
  • 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
  • 27. The complexity cube Sebis Research Profile © sebis 27
  • 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