Software Engineering für betriebliche Informationssysteme (sebis) Fakultät für Informatik Technische Universität München wwwmatthes.in.tum.de
sebis Research Profile 20.7.2014, Prof. Dr. Florian Matthes
Research background
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Enterprise Architecture Management
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
Communities
Collaborative Work
Digital Content
§ 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
Technology Transfer Projects
more >
Team
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more >
Alexander Schneider
Matheus Hauder
Klym Shumaiev
Thomas Reschenhofer
Marin Zec
Florian Matthes
Aline Schmidt
Jian Kong
Enterprise Architecture Management
Social Software Engineering
Bernhard Waltl
Alexander Waldmann
Project partners since 2002
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Enterprises and public administrations
Deutsche Börse Systems
Project partners since 2002
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Consultants and software vendors
Academic education
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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 >
Prototypical Solutions
Practical Experience
Informatics Models
Information & Communication
Technology
Research approach
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Abstraction
Application
Evaluation Engineering
Spin-Off
7
Informatics Application Domain
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
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The adoption rate for new technologies keeps accelerating.
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Forbes Magazine July 7th 1997
Exponential growth starts inconspicuously, and humans are not used to reasoning about non-linear processes.
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Google Trends December 2013
Humans: Employees, Customers, Suppliers, Partners, Markets, Communities, … Laws & Regulations
Resources: Energy, Matter, Information, Technology…
Enterprise
An enterprises understood as an adaptive system of systems
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Business Capabilities
Information Management
OPTIMIZE TRANSFORM
IM Capabilities
OPTIMIZE TRANSFORM
Goals, Strategy
Vision, Goals, Strategy
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
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Motivation – Most frequent EA challenges
0,00%
10,00%
20,00%
30,00%
40,00%
50,00%
60,00%
70,00%
80,00%
90,00%
100,00%
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
13
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.
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Agile EA management principles
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Individuals and interactions over formal processes and tools
IT Project 3 IT Project 2 IT Project 1
Top management
Business stakeholders
Software development
IT operations
Project managers
Software architects
Software developers
Top management
Strategy office
Business owners
Application owners
IT operations
Purchasing
EA Team
• Ensure top management support
• Maintain a good relationship to people form other management areas
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Agile EA management principles
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Focus on demands of top stakeholders and speak their languages
IT Project 3 IT Project 2 IT Project 1
Architecture blueprints
Top management
Business stakeholders
Software development
IT operations
Project managers
Software architects
Software developers
communicate
explain
involve
support
get feedback
� �
Top management
Strategy office
Visualizations Business owners
Application owners
IT operations
Purchasing
EA Team
Stakeholder-specific architecture views
Metrics
Reports
Architecture- approval and requirements
Architecture changes
model
collect
motivate
Business and IT strategy
Individual architecture aspects
Business and org. constraints
• A single number or picture is more helpful than 1000 reports
• Communicate, communicate, communicate
• Avoid waste • Benefit form existing model
management processes
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Agile EA management principles
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Reflect behavior and adapt to changes
IT Project 3 IT Project 2 IT Project 1
Architecture blueprints
Top management
Business stakeholders
Software development
IT operations
Project managers
Software architects
Software developers
�
communicate
explain
involve
support
get feedback reflect
adapt
� �
Top management
Strategy office
Visualizations Business owners
Application owners
IT operations
Purchasing
EA Team
Stakeholder-specific architecture views
Metrics
Reports
Architecture- approval and requirements
Architecture changes
model
collect
motivate
Business and IT strategy
Individual architecture aspects
Business and org. constraints
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• Iterative and Incremental (one cycle ~12 months)
• Use building blocks and 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
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Using quantitative models in the context of EAM
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System structure (EA, static)
change change
con
stra
ins
𝒕−𝟏 t = 𝑵𝑶𝑾
𝒕+𝟏
1 3
System behavior (dynamic)
4
1. Assess the
architecture with metrics
2. Measure architecture changes
3. Plan architecture changes
4. Monitor system performance with KPIs (Business & IT)
2
con
stra
ins
con
stra
ins
Metric Management Method (MMM) as Extension of the BEAMS Conceptual Framework
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Stakeholders
Goals + Concerns
Organizational context
Organizational context
Organizational Context
Actors Enterprise Architects Enterprise Architects
Development method
Characterize situation Configure EAM function Analyze EAM function
Adapt and evolve EAM function
Execute EAM
function
Implementation Guide (Patterns & Building Blocks)
BEAMS , EAM Pattern Catalog and EAM KPI Catalog
EA Metric
VBB
Performance Indicator
VBB VBB
IBB
EA Metric
IBB IBB
+ EAM Metric Catalog
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
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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
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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%
What are current problems in EA model maintenance?
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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%
More >
Federated enterprise architecture model management
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Modeling communities, artifacts, processes and their interactions
E
Metamodel and Model
D EAM
Task
Technology
fit
Metamodel and Model
A PPM
Task
Technology
fit
Team
publish model changes
model and meta-model changes to be
integrated
Metamodel and Model
B BPM
Task
Technology
fit
Team
publish model changes
Metamodel and Model
C ITSM
Task
Technology
fit
Team
publish model changes
publish model changes
Federated EA Model Management
Modeling Experts Modeling Community Metamodel Mappings Instance Mappings
Team
Enterprise
• Importing • Differencing • Conflict detection • Conflict resolution
• Collaboration • Negotiation
Federated enterprise architecture model management
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
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Tool support - ModelGlue
For further information see https://wwwmatthes.in.tum.de/pages/kkdtsjtjkc2g
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
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CALM3: Complexity of application landscapes
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?
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Models, metrics and methods
Project Partners
CALM3 Workshop
Series
10 Industry experts
Quarterly meetings
Extensive EA data
Concrete metrics
Tool development
Visionary discussions
The complexity cube
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The complexity cube
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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
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
Classification of applications
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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.
2
3 4
5
1
6
7
8 9
Control
Core
Shared Periphery
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
EA complexity metric based on heterogeneity
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0
0,2
0,4
0,6
0,8
1
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.
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)
Domain
Reoccurring AL complexity metrics in practice
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Application
Application
Application
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
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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.
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Basel II / III
Sarbanes-Oxley Act
REACH
Semantic processing of legal texts for IT compliance
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Company
Assets Objectives Tasks Employees
IT Requirements (Business IT Alignment)
Requirements Engineering
IT Systems
COBIT TOGAF
Controlling
Support through IS Compliance
Requirements (Legal Obligations)
§ Information-
systems LexInform, Juris,
RIS, …
Laws KWG, TMG,
BDSG, …
Authorities (e.g. BaFin)
searching, exploration, interpretation, change tracking etc.
Semantic processing of legal texts for IT compliance
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Compliance
Controlling Requirements (Legal Obligations)
§ Information-
systems LexInform, Juris,
RIS, …
Laws/ Regulations KWG, TMG,
BDSG, …
Authorities (e.g. BaFin)
searching, exploration, interpretation, change tracking etc.
§44 IT-examination, auditing, (internal/external) revision, etc.
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
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
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Collaborative knowledge work is ubiquitous in organizations
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Development of large
software systems
Solving complex problems in communities
Producing new ideas and
innovations
How can software support processes for collaborative knowledge work?
Theoretical basis of the research project involves three different disciplines
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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
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
Solution: Empowering users to collaboratively structure knowledge-intensive processes
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Create a new task for „Neue Idee“
Logical and temporal dependencies with CMMN
Adding a new task
Attribute types
Drag and drop of attributes on tasks
Access rights on attributes
Completed tasks
Hide completed tasks
Unstructured information
In-place editing
New attribute for the template
Des
ign
Prin
cipl
es
§ Flexible stage-gate process for Innovation Management
§ Development of a future Enterprise Architecture state
§ Artefact-oriented Requirements Engineering processes with templates C
ase
Stu
dies
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.
2. LITERATURE REVIEW
3. PROTOTYPE
Case studies to support processes for all three investigated domains.
4. CASE STUDIES
Qualitative evaluation of the three case studies with expert interviews.
5. EVALUATION
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
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Spreadsheets 2.0
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
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Motivation
Spreadsheets 2.0: Analysis of complex linked data
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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)
Visualizations Functions / Transformations Data
Spreadsheets 2.0: Analysis of complex linked data
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𝑓
𝑓
𝑓
𝑓
𝑓
𝑓
𝑓
𝑓
𝑓
𝑓
𝑓
Users For more information visit Spreadsheet 2.0 (http://wwwmatthes.in.tum.de)
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, …)?
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For more information visit Spreadsheet 2.0 (http://wwwmatthes.in.tum.de)
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
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Information systems for problem solving
Puzzle
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Reproductive Thinking (Heuristics, Algorithms etc.)
Productive Thinking (Creativity etc.)
Wicked Problem Problem
Example
Information System Support
Degree of Automation
Measuring temperature, …
Business Model Generation, …
Sensors, Embedded Systems, Robotics, Databases, …
SAP R/3, Word Processing, Spreadsheet Software, …
Collaborative Informationsystems, e.g. Wikis, Dropbox, …
Problem
Accounting, …
Degree of Collaboration
IS support for a complex problem: Business model generation
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• 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
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
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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?
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Colva: Collaborative learning video annotations
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A conceptual framework for describing augmented teaching sessions
Preparation Live teaching session Post-processing
Phases
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
Colva: A collaborative learning video annotations
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Possible synchronous and asynchronous collaboration via video annotations
Phases
Preparation Live teaching session
Post-processing
Act
ors
Instructor - View annotation.
View and create
annotation.
Learner -
Create and view annotation.
Create and view annotation.
Colva: Collaborative learning video annotations on the web
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Provide a web solution for collecting learners
annotations during the learning session
Implementation stages Stage 1 Stage 2 Stage 3
Synchronize video-recordings with collected real-time user annotations
Test and evaluate different methods for collaboration through video annotations
usage
Current objective Implement concept in viable prototype
Pilot project
For more information contact Klym Shumaiev [email protected]
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“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?
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
Thank you for your attention. Questions?